diff --git a/Fatigue_Life/Evaluation_results/k40_epoch1000/fatigue_life_evaluation_log_1.csv b/Fatigue_Life/Evaluation_results/k40_epoch1000/fatigue_life_evaluation_log_1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ed2210ecd623b7ed04e77e72ef9a6648ac2341b7
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k40_epoch1000/fatigue_life_evaluation_log_1.csv
@@ -0,0 +1,563 @@
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diff --git a/Fatigue_Life/Evaluation_results/k40_epoch1000/fatigue_life_evaluation_log_2.csv b/Fatigue_Life/Evaluation_results/k40_epoch1000/fatigue_life_evaluation_log_2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..01a6eaa6a850a9324199d068f54d5a32bbbb1563
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k40_epoch1000/fatigue_life_evaluation_log_2.csv
@@ -0,0 +1,744 @@
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diff --git a/Fatigue_Life/Evaluation_results/k40_epoch1000/fatigue_life_evaluation_log_3.csv b/Fatigue_Life/Evaluation_results/k40_epoch1000/fatigue_life_evaluation_log_3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e8ae53de42e239290e5ecc3dc925b80e92a40bac
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k40_epoch1000/fatigue_life_evaluation_log_3.csv
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diff --git a/Fatigue_Life/Evaluation_results/k40_epoch1000/fatigue_life_evaluation_log_4.csv b/Fatigue_Life/Evaluation_results/k40_epoch1000/fatigue_life_evaluation_log_4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..fd446aac811496ea0676aca42365a573f9c53b6e
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k40_epoch1000/fatigue_life_evaluation_log_4.csv
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diff --git a/Fatigue_Life/Evaluation_results/k40_epoch1000/fatigue_life_evaluation_log_5.csv b/Fatigue_Life/Evaluation_results/k40_epoch1000/fatigue_life_evaluation_log_5.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5a8fd2c5020003d55e902b4742194339c11b408b
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k40_epoch1000/fatigue_life_evaluation_log_5.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch1000/fatigue_life_evaluation_log_1.csv b/Fatigue_Life/Evaluation_results/k60_epoch1000/fatigue_life_evaluation_log_1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c50a76cd8788ac7fcdfe7058b13df57d16667af0
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch1000/fatigue_life_evaluation_log_1.csv
@@ -0,0 +1,744 @@
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch1000/fatigue_life_evaluation_log_2.csv b/Fatigue_Life/Evaluation_results/k60_epoch1000/fatigue_life_evaluation_log_2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4aba2e5a2b3bc45a7ff21d59e8c1fefff9a12bd4
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch1000/fatigue_life_evaluation_log_2.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch1000/fatigue_life_evaluation_log_3.csv b/Fatigue_Life/Evaluation_results/k60_epoch1000/fatigue_life_evaluation_log_3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6e5c60d929e3c126d7be7bae51b86da104f47bd2
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch1000/fatigue_life_evaluation_log_3.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch1000/fatigue_life_evaluation_log_4.csv b/Fatigue_Life/Evaluation_results/k60_epoch1000/fatigue_life_evaluation_log_4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7a2f258bdb7672e02415af83eab6e762a22be499
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch1000/fatigue_life_evaluation_log_4.csv
@@ -0,0 +1,688 @@
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch1000/fatigue_life_evaluation_log_5.csv b/Fatigue_Life/Evaluation_results/k60_epoch1000/fatigue_life_evaluation_log_5.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c70fcf7d740e7cbfb09c5123e953c53a41b4455e
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch1000/fatigue_life_evaluation_log_5.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch1000_nonlog/fatigue_life_evaluation_log_1.csv b/Fatigue_Life/Evaluation_results/k60_epoch1000_nonlog/fatigue_life_evaluation_log_1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3a162ba0389aeafd4d99b12beb65f1f922aa44f6
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch1000_nonlog/fatigue_life_evaluation_log_1.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch1000_nonlog/fatigue_life_evaluation_log_2.csv b/Fatigue_Life/Evaluation_results/k60_epoch1000_nonlog/fatigue_life_evaluation_log_2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..910a12f57476a615b4a8eb9a9f279372c6daf501
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch1000_nonlog/fatigue_life_evaluation_log_2.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch1000_nonlog/fatigue_life_evaluation_log_3.csv b/Fatigue_Life/Evaluation_results/k60_epoch1000_nonlog/fatigue_life_evaluation_log_3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ce96e6a501032733c5d109ba4d0664cd5c11eaab
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch1000_nonlog/fatigue_life_evaluation_log_3.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch1000_nonlog/fatigue_life_evaluation_log_4.csv b/Fatigue_Life/Evaluation_results/k60_epoch1000_nonlog/fatigue_life_evaluation_log_4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e015e22890f25dbec5161ca4a2251b678940acec
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch1000_nonlog/fatigue_life_evaluation_log_4.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch1000_nonlog/fatigue_life_evaluation_log_5.csv b/Fatigue_Life/Evaluation_results/k60_epoch1000_nonlog/fatigue_life_evaluation_log_5.csv
new file mode 100644
index 0000000000000000000000000000000000000000..94d6bc602ea27f4d7fd3c0827d25c594913c9e04
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch1000_nonlog/fatigue_life_evaluation_log_5.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500/fatigue_life_evaluation_log_1.csv b/Fatigue_Life/Evaluation_results/k60_epoch500/fatigue_life_evaluation_log_1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..dbc97d856487fcb5ed79b53840dbacff031cca97
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500/fatigue_life_evaluation_log_1.csv
@@ -0,0 +1,563 @@
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500/fatigue_life_evaluation_log_2.csv b/Fatigue_Life/Evaluation_results/k60_epoch500/fatigue_life_evaluation_log_2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..be632523a111cad6a2e08107b641a340072942c8
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500/fatigue_life_evaluation_log_2.csv
@@ -0,0 +1,744 @@
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500/fatigue_life_evaluation_log_3.csv b/Fatigue_Life/Evaluation_results/k60_epoch500/fatigue_life_evaluation_log_3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..89ef84593a6c03aeb7fcae7cfc07fbc11c0d805c
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500/fatigue_life_evaluation_log_3.csv
@@ -0,0 +1,686 @@
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500/fatigue_life_evaluation_log_4.csv b/Fatigue_Life/Evaluation_results/k60_epoch500/fatigue_life_evaluation_log_4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4137433e5e64cb4233dfe694f0f3cad8a568a410
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500/fatigue_life_evaluation_log_4.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500/fatigue_life_evaluation_log_5.csv b/Fatigue_Life/Evaluation_results/k60_epoch500/fatigue_life_evaluation_log_5.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a3f939dacfbdf6464d0f835cffe9719770b144b6
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500/fatigue_life_evaluation_log_5.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_exponent_multi_metrics_2/prediction_sample2.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_exponent_multi_metrics_2/prediction_sample2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..416397f8cff7ad787c393250ff539834d02ecdf7
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_exponent_multi_metrics_2/prediction_sample2.csv
@@ -0,0 +1,1487 @@
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_exponent_multi_metrics_2/prediction_sample3.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_exponent_multi_metrics_2/prediction_sample3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4abf4de77302905aaae14e52dfad1fbb548a7c95
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_exponent_multi_metrics_2/prediction_sample3.csv
@@ -0,0 +1,1371 @@
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_exponent_multi_metrics_2/prediction_sample4.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_exponent_multi_metrics_2/prediction_sample4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2bb5e6bd12381219ed96dc3bc7eddd95e4a3eed8
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_exponent_multi_metrics_2/prediction_sample4.csv
@@ -0,0 +1,1375 @@
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_exponent_multi_metrics_2/prediction_sample5.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_exponent_multi_metrics_2/prediction_sample5.csv
new file mode 100644
index 0000000000000000000000000000000000000000..f7bd5456ba0bd08f149458a7fd028bc3528caa1b
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_exponent_multi_metrics_2/prediction_sample5.csv
@@ -0,0 +1,1367 @@
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_extra2/prediction_sample2.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_extra2/prediction_sample2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..210323b76f6b301263f4dd7b0fa1c90ce47abad9
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_extra2/prediction_sample2.csv
@@ -0,0 +1,744 @@
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_extra2/prediction_sample3.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_extra2/prediction_sample3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..d41552bc43c0d006a8917dea136ee36db159c66e
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_extra2/prediction_sample3.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_extra2/prediction_sample4.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_extra2/prediction_sample4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..21bfb8bc7342861792453e706b8a82f9986c35f2
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_extra2/prediction_sample4.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_extra2/prediction_sample5.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_extra2/prediction_sample5.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b65edb15877d5502855cdf3bfaab7334cdaa00a8
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_extra2/prediction_sample5.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics/prediction_sample2.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics/prediction_sample2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..12a8386880d0556f2d70ac642fc0533ecd8dde2c
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics/prediction_sample2.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics/prediction_sample3.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics/prediction_sample3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5609003bc5eb01a3f3bb749c9235dce333f3a7aa
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics/prediction_sample3.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics/prediction_sample4.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics/prediction_sample4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..51675d0b454e6e02ce49c1b0cfaee9bae36d5aba
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics/prediction_sample4.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics/prediction_sample5.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics/prediction_sample5.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a7d6969e058de5c90abd86694322b296f93e44f3
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics/prediction_sample5.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics2/prediction_sample2.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics2/prediction_sample2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..360daed525b9ef770f947232a77da01e3d762d68
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics2/prediction_sample2.csv
@@ -0,0 +1,744 @@
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics2/prediction_sample3.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics2/prediction_sample3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2a00e904661c0aec36a9b0ee39ec7b9301d2a03d
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics2/prediction_sample3.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics2/prediction_sample4.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics2/prediction_sample4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1be38a96862dd76d66d08633977b65e4b5cb6bdf
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics2/prediction_sample4.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics2/prediction_sample5.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics2/prediction_sample5.csv
new file mode 100644
index 0000000000000000000000000000000000000000..d8f99ff2a0b54dbf834dc377db8a69c25df8fec5
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics2/prediction_sample5.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2/prediction_sample2.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2/prediction_sample2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..416397f8cff7ad787c393250ff539834d02ecdf7
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2/prediction_sample2.csv
@@ -0,0 +1,1487 @@
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2/prediction_sample3.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2/prediction_sample3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4abf4de77302905aaae14e52dfad1fbb548a7c95
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2/prediction_sample3.csv
@@ -0,0 +1,1371 @@
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2/prediction_sample4.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2/prediction_sample4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2bb5e6bd12381219ed96dc3bc7eddd95e4a3eed8
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2/prediction_sample4.csv
@@ -0,0 +1,1375 @@
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2/prediction_sample5.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2/prediction_sample5.csv
new file mode 100644
index 0000000000000000000000000000000000000000..f7bd5456ba0bd08f149458a7fd028bc3528caa1b
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2/prediction_sample5.csv
@@ -0,0 +1,1367 @@
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2_featuredrop/prediction_sample2.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2_featuredrop/prediction_sample2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..d65c87e2cca0c744a0707e99ce3afc557bcdf0bf
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2_featuredrop/prediction_sample2.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2_featuredrop/prediction_sample3.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2_featuredrop/prediction_sample3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..65b259f7cc9b73ba6ffb3791a3ce6bd4cf040868
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2_featuredrop/prediction_sample3.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2_featuredrop/prediction_sample4.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2_featuredrop/prediction_sample4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..392b8bd55e8fed7362fce5dc95bdaad445458b2e
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2_featuredrop/prediction_sample4.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2_featuredrop/prediction_sample5.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2_featuredrop/prediction_sample5.csv
new file mode 100644
index 0000000000000000000000000000000000000000..48ad0f0b94aa016fc23f300a3d2cf6b431fb11b9
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_2_featuredrop/prediction_sample5.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_normal_mesh/prediction_sample2.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_normal_mesh/prediction_sample2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..360daed525b9ef770f947232a77da01e3d762d68
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_normal_mesh/prediction_sample2.csv
@@ -0,0 +1,744 @@
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_normal_mesh/prediction_sample3.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_normal_mesh/prediction_sample3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2a00e904661c0aec36a9b0ee39ec7b9301d2a03d
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_normal_mesh/prediction_sample3.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_normal_mesh/prediction_sample4.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_normal_mesh/prediction_sample4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1be38a96862dd76d66d08633977b65e4b5cb6bdf
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_normal_mesh/prediction_sample4.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_normal_mesh/prediction_sample5.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_normal_mesh/prediction_sample5.csv
new file mode 100644
index 0000000000000000000000000000000000000000..d8f99ff2a0b54dbf834dc377db8a69c25df8fec5
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_multi_metrics_normal_mesh/prediction_sample5.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_single_metrics/prediction_sample2.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_single_metrics/prediction_sample2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..be632523a111cad6a2e08107b641a340072942c8
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_single_metrics/prediction_sample2.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_single_metrics/prediction_sample3.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_single_metrics/prediction_sample3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..89ef84593a6c03aeb7fcae7cfc07fbc11c0d805c
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_single_metrics/prediction_sample3.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_single_metrics/prediction_sample4.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_single_metrics/prediction_sample4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4137433e5e64cb4233dfe694f0f3cad8a568a410
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_single_metrics/prediction_sample4.csv
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diff --git a/Fatigue_Life/Evaluation_results/k60_epoch500_single_metrics/prediction_sample5.csv b/Fatigue_Life/Evaluation_results/k60_epoch500_single_metrics/prediction_sample5.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a3f939dacfbdf6464d0f835cffe9719770b144b6
--- /dev/null
+++ b/Fatigue_Life/Evaluation_results/k60_epoch500_single_metrics/prediction_sample5.csv
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diff --git a/Fatigue_Life/Visuize/__pycache__/read_node.cpython-310.pyc b/Fatigue_Life/Visuize/__pycache__/read_node.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..fce56d6e0167d1164adc67b2659939255b435136
Binary files /dev/null and b/Fatigue_Life/Visuize/__pycache__/read_node.cpython-310.pyc differ
diff --git a/Fatigue_Life/Visuize/__pycache__/read_node.cpython-312.pyc b/Fatigue_Life/Visuize/__pycache__/read_node.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c0037f4db0785fc0fac21cba5d566c5746cfb59f
Binary files /dev/null and b/Fatigue_Life/Visuize/__pycache__/read_node.cpython-312.pyc differ
diff --git a/Fatigue_Life/Visuize/life_D15_d5_r2/life_D15_d5_r2.dat b/Fatigue_Life/Visuize/life_D15_d5_r2/life_D15_d5_r2.dat
new file mode 100644
index 0000000000000000000000000000000000000000..afaa66a2dad542b68f2f5a0b6e8039460b42f928
--- /dev/null
+++ b/Fatigue_Life/Visuize/life_D15_d5_r2/life_D15_d5_r2.dat
@@ -0,0 +1,2339 @@
+/batch
+/config,noeldb,1     ! force off writing results to database
+*get,_wallstrt,active,,time,wall
+! ANSYS input file written by Workbench version 2025 R1
+! File used for geometry attach: D:\AnK\Project Fatigue\Stepped_shaft\step_shaft_files\dp0\SYS\DM\SYS.scdocx
+/title,step_shaft--Static Structural (A5)
+! ****** Begin Custom Load Command Snippet ******
+/COM, ACT Extensions:
+/COM,     LSDYNA, 2025.1
+/COM,     5f463412-bd3e-484b-87e7-cbc0a665e474, wbex
+/COM,/COM,     AnsysForcedResponse, 2022.2
+/COM,     6ef906f8-8529-4043-bfde-45577efdf8c8, wbex
+/COM,/COM,     ANSYSMotion, 2025.1
+/COM,     20180725-3f81-49eb-9f31-41364844c769, wbex
+/COM,
+! ****** End   Custom Load Command Snippet ******
+*DIM,_wb_ProjectScratch_dir,string,248
+_wb_ProjectScratch_dir(1) = 'D:\AnK\Project Fatigue\Stepped_shaft\step_shaft_files\dp0\SYS\MECH\'
+*DIM,_wb_SolverFiles_dir,string,248
+_wb_SolverFiles_dir(1) = 'D:\AnK\Project Fatigue\Stepped_shaft\step_shaft_files\dp0\SYS\MECH\'
+*DIM,_wb_userfiles_dir,string,248
+_wb_userfiles_dir(1) = 'D:\AnK\Project Fatigue\Stepped_shaft\step_shaft_files\user_files\'
+/com,--- Data in consistent MKS units. See Solving Units in the help system for more information.
+/units,MKS
+/nopr
+/wb,file,start              !  signify a WB generated input file
+/prep7
+! Turn off shape checking because checks already performed inside WB mesher.
+! See help system for more information.
+SHPP,OFF,,NOWARN
+/nolist
+etcon,set             ! allow ANSYS to choose best KEYOP's for 180x elements, resets any applicable keyopt to MAPDL defaults
+/com,*********** Nodes for the whole assembly ***********
+nblock,3,,565
+(1i9,3e20.9e3)
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+-1
+/wb,elem,start            !  set before creation of elements
+/com,*********** Elements for Body 1 'SYS\Solid' ***********
+et,1,185
+eblock,19,solid,,1604
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+        1        1        1        1        0        0        0        0        8        0     1594      245      429      431      431      268      268      268      268
+        1        1        1        1        0        0        0        0        8        0     1595      429      431      476      476      487      487      487      487
+        1        1        1        1        0        0        0        0        8        0     1596      429      476      431      431      430      430      430      430
+        1        1        1        1        0        0        0        0        8        0     1597      161      472      486      486      418      418      418      418
+        1        1        1        1        0        0        0        0        8        0     1598      160      486      161      161      472      472      472      472
+        1        1        1        1        0        0        0        0        8        0     1599      147      149      485      485      418      418      418      418
+        1        1        1        1        0        0        0        0        8        0     1600      130      485      147      147      149      149      149      149
+        1        1        1        1        0        0        0        0        8        0     1601       80       81      491      491      457      457      457      457
+        1        1        1        1        0        0        0        0        8        0     1602       73       80       81       81      491      491      491      491
+        1        1        1        1        0        0        0        0        8        0     1603       71      491      439      439      494      494      494      494
+        1        1        1        1        0        0        0        0        8        0     1604       71      439      491      491       72       72       72       72
+-1
+! Material Id = {9A05FE04-5EDF-4FB0-8CD8-C98DB114DC60}
+/wb,elem,end               !  done creating elements
+/com,*********** Send User Defined Coordinate System(s) ***********
+csys,0
+toffst,273.15,      ! Temperature offset from absolute zero
+/com,*********** Set Reference Temperature ***********
+tref,22.
+/wb,mat,start              !  starting to send materials
+/com,*********** Send Materials ***********
+Temperature = 'TEMP' ! Temperature
+MP,DENS,1,7850,	! kg m^-3
+MP,ALPX,1,1.2e-05,	! C^-1
+MP,C,1,434,	! J kg^-1 C^-1
+MP,KXX,1,60.5,	! W m^-1 C^-1
+MP,RSVX,1,1.7e-07,	! kg m^3 A^-2 s^-3
+MP,EX,1,200000000000,	! Pa
+MP,NUXY,1,0.3,
+MP,MURX,1,10000,
+MP,UVID,1,98b61a57-d3bb-4617-9ef5-feaa1a778986
+MP,UMID,1,59002a79-71c2-428e-906b-55d7dbee100e
+
+/wb,mat,end                !  done sending materials
+!************************* Model Summary ********************
+!SYS\Solid,	Structural Steel,	matid,	1
+!************************* End Model Summary ********************
+! get the diagonal of the bounding box. Needed later for other things
+*get,_xmin,node,,mnloc,x
+*get,_ymin,node,,mnloc,y
+*get,_zmin,node,,mnloc,z
+*get,_xmax,node,,mxloc,x
+*get,_ymax,node,,mxloc,y
+*get,_zmax,node,,mxloc,z
+_ASMDIAG=(_xmax-_xmin)*(_xmax-_xmin)+(_ymax-_ymin)*(_ymax-_ymin)+(_zmax-_zmin)*(_zmax-_zmin)
+_ASMDIAG=SQRT(_ASMDIAG)
+/wb,contact,start          !  starting to send contact
+/wb,contact,end            !  done creating contacts
+/golist
+/wb,load,start             !  starting to send loads
+/com,*********** Fixed Supports ***********
+CMBLOCK,_FIXEDSU,NODE,       18
+(8i10)
+        23        24        25        26        27        28        29        30
+        31        32        33        34       404       405       406       407
+       408       409
+cmsel,s,_FIXEDSU
+d,all,all
+nsel,all
+/com,*********** Define Force Using Surface Effect Elements ***********
+local,12,0,0.,0.,0.,0.,0.,0.
+csys,0
+et,2,154
+eblock,10,,,17
+(15i9)
+     2427        2        2        2       12       16       17      411      411
+     2428        2        2        2       12      410      411      412      412
+     2429        2        2        2       12       21       22      410      410
+     2430        2        2        2       12       17       18      411      411
+     2431        2        2        2       12      410      413      411      411
+     2432        2        2        2       12       20       21      412      412
+     2433        2        2        2       12       12       13      410      410
+     2434        2        2        2       12       19       20      412      412
+     2435        2        2        2       12       12      410       22       22
+     2436        2        2        2       12       21      410      412      412
+     2437        2        2        2       12       18       19      412      412
+     2438        2        2        2       12       18      412      411      411
+     2439        2        2        2       12       14       15      413      413
+     2440        2        2        2       12       15       16      413      413
+     2441        2        2        2       12       16      411      413      413
+     2442        2        2        2       12       13       14      413      413
+     2443        2        2        2       12       13      413      410      410
+-1
+esel,s,type,,2
+keyop,2,2,1         ! Apply load in local coordinate system
+keyop,2,7,1         ! Use original area so load is constant in large deformation
+esel,all
+/gst,on,on
+fini
+*get,_numnode,node,0,count
+*get,_numelem,elem,0,count
+*get, _MAXELEMNUM, elem, 0, NUM, MAX
+*get, _MAXNODENUM, node, 0, NUM, MAX
+*get, _MAXELEMTYPE, etyp, 0, NUM, MAX
+*get, _MAXREALCONST, rcon, 0, NUM, MAX
+/go
+/wb,load,end               !  done creating loads
+/com,--- Number of total nodes = %_numnode%
+/com,--- Number of contact elements = 17
+/com,--- Number of spring elements = 0
+/com,--- Number of bearing elements = 0
+/com,--- Number of solid elements = 1604
+/com,--- Number of condensed parts = 0
+/com,--- Number of total elements = %_numelem%
+*get,_wallbsol,active,,time,wall
+/com,****************************************************************************
+/com,*************************    SOLUTION       ********************************
+/com,****************************************************************************
+/solu
+antype,0                   ! static analysis
+_thickRatio=  1     ! Ratio of thick parts in the model
+eqsl,pcg,1e-8,,,,,,1
+cntr,print,1               ! print out contact info and also make no initial contact an error
+cncheck,dmp		!	Enable program controlled contact splitting for distributed memory parallel run
+dmpoption,emat,no             ! Don't combine emat file for DANSYS
+dmpoption,esav,no             ! Don't combine esav file for DANSYS
+nldiag,cont,iter           ! print out contact info each equilibrium iteration
+rescontrol,,none                 ! Do not keep any restart files
+/com,****************************************************
+/com,******************* SOLVE FOR LS 1 OF 1 ****************
+esel,s,type,,2
+nsle
+sfe,all,1,pres,1,0.
+sfe,all,2,pres,1,0.
+sfe,all,3,pres,1,538081997.356747
+nsel,all
+esel,all
+/nopr
+/gopr
+nsub,1,1,1
+time,1.
+outres,erase
+outres,all,none
+outres,nsol,all,
+outres,rsol,all
+outres,eangl,all
+outres,etmp,all
+outres,veng,all
+outres,strs,all,
+outres,epel,all,
+outres,eppl,all,
+outres,cont,all,
+! *********** WB SOLVE COMMAND ***********
+! check interactive state
+*get,ANSINTER_,active,,int
+*if,ANSINTER_,ne,0,then
+/eof
+*endif
+solve
+/com *************** Write FE CONNECTORS ********* 
+CEWRITE,file,ce,,INTE
+/com,****************************************************
+/com,*************** FINISHED SOLVE FOR LS 1 *************
+*get,_wallasol,active,,time,wall
+/nopr
+*get,_numnode,node,0,count
+*get,_numelem,elem,0,count
+*get, _MAXELEMNUM, elem, 0, NUM, MAX
+*get, _MAXNODENUM, node, 0, NUM, MAX,,,INTERNAL
+*get, _MAXELEMTYPE, etyp, 0, NUM, MAX
+*get, _MAXREALCONST, rcon, 0, NUM, MAX
+*get,_NSOLTHREADS,active,0,ntfs
+*get,_NSOLPROCESSES,active,0,npfs
+/gopr
+*get,_PCGITER,active,,solu,cgiter
+/post1
+xmlo,ENCODING,ISO-8859-1
+xmlo,parm
+/xml,parm,xml
+fini
+/gopr
+*get,_walldone,active,,time,wall
+_preptime=(_wallbsol-_wallstrt)*3600
+_solvtime=(_wallasol-_wallbsol)*3600
+_posttime=(_walldone-_wallasol)*3600
+_totaltim=(_walldone-_wallstrt)*3600
+*get,_dlbratio,active,0,solu,dlbr
+*get,_combtime,active,0,solu,comb
+*get,_ssmode,active,0,solu,ssmm
+*get,_ndofs,active,0,solu,ndof
+*get,_sol_end_time,active,0,set,time
+*if,_sol_end_time,eq,1.000000,then
+/fclean				!clean distributed files
+*endif
+/com,--- Total number of nodes = %_numnode%
+/com,--- Total number of elements = %_numelem%
+/com,--- Element load balance ratio = %_dlbratio%
+/com,--- Time to combine distributed files = %_combtime%
+/com,--- Sparse memory mode = %_ssmode%
+/com,--- Number of DOF = %_ndofs%
+/wb,file,end               ! done with WB generated input
diff --git a/Fatigue_Life/Visuize/life_D15_d5_r2/life_D15_d5_r2.txt b/Fatigue_Life/Visuize/life_D15_d5_r2/life_D15_d5_r2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b402e4b338f8731790432af9ee6271b3d4f36561
--- /dev/null
+++ b/Fatigue_Life/Visuize/life_D15_d5_r2/life_D15_d5_r2.txt
@@ -0,0 +1,515 @@
+node    Life
+0    1935.932898
+2    2086.145042
+3    1899.237164
+4    1869.749389
+5    1833.324744
+6    1775.032462
+7    2233.736805
+8    2194.729494
+9    2048.666189
+10    1951.082349
+11    1937.973347
+12    1601.612317
+13    1631.174036
+14    1633.160999
+15    1603.471333
+16    1600.771339
+17    1600.311772
+18    1612.893902
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diff --git a/Fatigue_Life/Visuize/test.py b/Fatigue_Life/Visuize/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c2822491c707567cae40cadec44a2255ed930ba
--- /dev/null
+++ b/Fatigue_Life/Visuize/test.py
@@ -0,0 +1,215 @@
+
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from read_node import read_dat_file, read_result_file
+import os
+import numpy as np
+import pyvista as pv
+from scipy.spatial import distance_matrix
+
+"""
+folder_path = r"D:\AnK\FatigueNet\Fatigue_Life\datasets\Raw data\shaft_high\life_D15_d5_r1"
+folder_base_name = os.path.basename(folder_path).replace("_data", "")
+dat_file_path = os.path.join(folder_path, f"{folder_base_name}.dat")
+fatigue_life_path = os.path.join(folder_path, f"{folder_base_name}.txt")
+
+positions, connectivity = read_dat_file(dat_file_path)
+
+positions = np.array(positions)
+connectivity = np.array(connectivity)
+
+# Calculate appropriate sphere size based on point density
+# Find minimum distance between points to avoid overlap
+from scipy.spatial.distance import pdist
+distances = pdist(positions)
+min_distance = np.min(distances)
+max_distance = np.max(distances)
+print(f"Min distance between points: {min_distance:.6f}")
+print(f"Max distance between points: {max_distance:.6f}")
+
+# Matplotlib version (for comparison)
+x = positions[:, 0]
+y = positions[:, 1]
+z = positions[:, 2]
+
+fig = plt.figure(figsize=(10, 8))
+ax = fig.add_subplot(111, projection='3d')
+ax.scatter(x, y, z, c='red', s=50, alpha=0.8)
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+ax.set_zlabel('Z')
+ax.set_title('3D Points Visualization - Matplotlib')
+ax.grid(True)
+plt.show()
+"""
+#.........................................................................................................................................................................................
+#.........................................................................................................................................................................................
+
+"""
+import os
+import numpy as np
+import pyvista as pv
+from scipy.spatial.distance import pdist
+from read_node import read_dat_file
+
+# === Load dataset ===
+folder_path = r"D:\AnK\FatigueNet\Fatigue_Life\datasets\Raw data\shaft_high\life_D15_d5_r1"
+folder_base_name = os.path.basename(folder_path).replace("_data", "")
+dat_file_path = os.path.join(folder_path, f"{folder_base_name}.dat")
+
+# Read point positions
+positions, connectivity = read_dat_file(dat_file_path)
+#print(connectivity)
+#print(positions)
+
+positions = np.array(positions)
+connectivity = np.array(connectivity)
+
+# === Compute spacing metrics ===
+distances = pdist(positions)
+min_distance = np.min(distances)
+max_distance = np.max(distances)
+print(f"Min distance between points: {min_distance:.6f}")
+print(f"Max distance between points: {max_distance:.6f}")
+
+# === Create PyVista point cloud ===
+point_cloud = pv.PolyData(positions)
+
+# === Option A: Use glyphs (spheres) for geometric visualization ===
+# Choose adaptive sphere size
+sphere_radius = min_distance * 0.35  # 10% of min spacing
+sphere = pv.Sphere(radius=sphere_radius)
+
+# Attach dummy scalar field (optional, for glyph binding)
+point_cloud["id"] = np.arange(len(positions))
+
+# Generate glyphs
+glyphs = point_cloud.glyph(geom=sphere, scale=False)
+
+# === PyVista Plot ===
+plotter = pv.Plotter()
+plotter.add_mesh(glyphs, color="red", opacity=1.0, show_scalar_bar=False)
+
+# Add axes and grid
+plotter.add_axes()
+plotter.show_grid()
+plotter.set_background("white")
+plotter.add_title("3D Point Cloud - PyVista (Spheres)")
+
+# Display
+plotter.show()
+"""
+#.........................................................................................................................................................................................
+#.........................................................................................................................................................................................
+
+"""
+import os
+import numpy as np
+import pyvista as pv
+from scipy.spatial.distance import pdist
+from read_node import read_dat_file, read_result_file  # Make sure these are available
+
+# === Load dataset ===
+folder_path = r"D:\AnK\FatigueNet\Fatigue_Life\datasets\Raw data\shaft_high\life_D15_d5_r1"
+folder_base_name = os.path.basename(folder_path).replace("_data", "")
+dat_file_path = os.path.join(folder_path, f"{folder_base_name}.dat")
+fatigue_life_path = os.path.join(folder_path, f"{folder_base_name}.txt")
+
+positions, connectivity = read_dat_file(dat_file_path)
+#print(connectivity)
+#print(positions)
+
+positions = np.array(positions)
+connectivity = np.array(connectivity)
+fatigue_life = np.array(read_result_file(fatigue_life_path))
+
+assert positions.shape[0] == fatigue_life.shape[0], "Mismatch between points and fatigue data!"
+
+# === Compute spacing metrics ===
+distances = pdist(positions)
+min_distance = np.min(distances)
+max_distance = np.max(distances)
+print(f"Min distance between points: {min_distance:.6f}")
+print(f"Max distance between points: {max_distance:.6f}")
+
+# === Create point cloud and attach fatigue life ===
+point_cloud = pv.PolyData(positions)
+point_cloud["fatigue_life"] = fatigue_life  # <-- this is used for coloring
+point_cloud["fatigue_life"] = np.log10(fatigue_life + 1e-12) # comment this if you want the absolute mapping of the life on nodes
+
+
+# === Create glyphs (spheres) ===
+sphere_radius = min_distance * 0.35
+sphere = pv.Sphere(radius=sphere_radius)
+glyphs = point_cloud.glyph(geom=sphere, scale=False)
+
+# === PyVista plot ===
+plotter = pv.Plotter()
+plotter.add_mesh(glyphs,
+                 scalars="fatigue_life",
+                 cmap="viridis",  # or try "plasma", "coolwarm", "inferno"
+                 opacity=1.0,
+                 show_scalar_bar=True)
+
+plotter.add_axes()
+plotter.show_grid()
+plotter.set_background("white")
+plotter.add_title("3D Point Cloud Colored by Fatigue Life")
+plotter.show()
+"""
+
+
+#.........................................................................................................................................................................................
+# visualization with connectivity
+#.........................................................................................................................................................................................
+
+import os
+import numpy as np
+import pyvista as pv
+from scipy.spatial.distance import pdist
+from read_node import read_dat_file, read_result_file  # Make sure these are available
+
+# === Load dataset ===
+folder_path = r"D:\AnK\FatigueNet\Fatigue_Life\Visuize\life_D15_d5_r2"
+folder_base_name = os.path.basename(folder_path).replace("_data", "")
+dat_file_path = os.path.join(folder_path, f"{folder_base_name}.dat")
+fatigue_life_path = os.path.join(folder_path, f"{folder_base_name}.txt")
+
+
+positions, connectivity = read_dat_file(dat_file_path)
+#print(connectivity)
+#print(positions)
+
+positions = np.array(positions)
+connectivity = np.array(connectivity)
+
+fatigue_life = np.array(read_result_file(fatigue_life_path))
+
+# Convert connectivity to VTK format for unstructured grid
+n_elements = len(connectivity)
+cell_types = np.full(n_elements, pv.CellType.TETRA, dtype=np.uint8)
+
+if np.min(connectivity) > 0:
+    connectivity -= 1
+
+# Format: [n_points, pt1, pt2, pt3, pt4, ...]
+cells = []
+for conn in connectivity:
+    cells.append(4)
+    cells.extend(conn)
+cells = np.array(cells)
+
+# Build unstructured grid
+grid = pv.UnstructuredGrid(cells, cell_types, positions)
+
+# Attach fatigue life as point data
+grid["fatigue_life"] = fatigue_life
+
+# Plot
+plotter = pv.Plotter()
+plotter.add_mesh(grid, scalars="fatigue_life", cmap="viridis_r", show_edges=True)
+plotter.add_axes()
+plotter.show_grid()
+plotter.set_background("white")
+plotter.add_title("Fatigue Mesh Visualization (Connected Elements)")
+plotter.show()
diff --git a/Fatigue_Life/datasets/extracted_data/Shaft_low/shaft_low_.h5 b/Fatigue_Life/datasets/extracted_data/Shaft_low/shaft_low_.h5
new file mode 100644
index 0000000000000000000000000000000000000000..6c8eba8648b3cc7cd596f5dce6fc281f5a2f2e82
--- /dev/null
+++ b/Fatigue_Life/datasets/extracted_data/Shaft_low/shaft_low_.h5
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18a7fb5f38cb5d53df79d8199c0cae7d6e0def867a0586b60b49cb57118408bd
+size 744872
diff --git a/Fatigue_Life/datasets/extracted_data/Shaft_low/shaft_low_.json b/Fatigue_Life/datasets/extracted_data/Shaft_low/shaft_low_.json
new file mode 100644
index 0000000000000000000000000000000000000000..a51f7df8edff108b079e92c666334c0ea36b6703
--- /dev/null
+++ b/Fatigue_Life/datasets/extracted_data/Shaft_low/shaft_low_.json
@@ -0,0 +1,100 @@
+{
+    "group_0": {
+        "geometry_folder": "life_D14.2_d6.8_r2.7",
+        "number_of_nodes": 748,
+        "number_of_cells": 2423,
+        "number_of_fatigue_life": 748,
+        "data_type": "fatigue_life"
+    },
+    "group_1": {
+        "geometry_folder": "life_D14.5_d6.5_r2.2",
+        "number_of_nodes": 731,
+        "number_of_cells": 2415,
+        "number_of_fatigue_life": 731,
+        "data_type": "fatigue_life"
+    },
+    "group_2": {
+        "geometry_folder": "life_D14_d6_r2",
+        "number_of_nodes": 659,
+        "number_of_cells": 2134,
+        "number_of_fatigue_life": 659,
+        "data_type": "fatigue_life"
+    },
+    "group_3": {
+        "geometry_folder": "life_D14_d7_r2.4",
+        "number_of_nodes": 761,
+        "number_of_cells": 2489,
+        "number_of_fatigue_life": 761,
+        "data_type": "fatigue_life"
+    },
+    "group_4": {
+        "geometry_folder": "life_D15.2_d6.2_r1.6",
+        "number_of_nodes": 675,
+        "number_of_cells": 2205,
+        "number_of_fatigue_life": 675,
+        "data_type": "fatigue_life"
+    },
+    "group_5": {
+        "geometry_folder": "life_D15.5_d5.5_r1.8",
+        "number_of_nodes": 542,
+        "number_of_cells": 1705,
+        "number_of_fatigue_life": 542,
+        "data_type": "fatigue_life"
+    },
+    "group_6": {
+        "geometry_folder": "life_D15.8_d5.2_r1.3",
+        "number_of_nodes": 518,
+        "number_of_cells": 1629,
+        "number_of_fatigue_life": 518,
+        "data_type": "fatigue_life"
+    },
+    "group_7": {
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+        "number_of_cells": 1604,
+        "number_of_fatigue_life": 514,
+        "data_type": "fatigue_life"
+    },
+    "group_8": {
+        "geometry_folder": "life_D15_d6_r1.5",
+        "number_of_nodes": 655,
+        "number_of_cells": 2116,
+        "number_of_fatigue_life": 655,
+        "data_type": "fatigue_life"
+    },
+    "group_9": {
+        "geometry_folder": "life_D15_d6_r2",
+        "number_of_nodes": 669,
+        "number_of_cells": 2144,
+        "number_of_fatigue_life": 669,
+        "data_type": "fatigue_life"
+    },
+    "group_10": {
+        "geometry_folder": "life_D15_d6_r2.5",
+        "number_of_nodes": 697,
+        "number_of_cells": 2271,
+        "number_of_fatigue_life": 697,
+        "data_type": "fatigue_life"
+    },
+    "group_11": {
+        "geometry_folder": "life_D15_d7_r2",
+        "number_of_nodes": 798,
+        "number_of_cells": 2650,
+        "number_of_fatigue_life": 798,
+        "data_type": "fatigue_life"
+    },
+    "group_12": {
+        "geometry_folder": "life_D16_d6_r2",
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+        "number_of_cells": 2240,
+        "number_of_fatigue_life": 687,
+        "data_type": "fatigue_life"
+    },
+    "group_13": {
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+        "number_of_nodes": 683,
+        "number_of_cells": 2196,
+        "number_of_fatigue_life": 683,
+        "data_type": "fatigue_life"
+    }
+}
\ No newline at end of file
diff --git a/Fatigue_Life/datasets/extracted_data/cylinder_high/cylinder_.h5 b/Fatigue_Life/datasets/extracted_data/cylinder_high/cylinder_.h5
new file mode 100644
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+version https://git-lfs.github.com/spec/v1
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+size 3069368
diff --git a/Fatigue_Life/datasets/extracted_data/cylinder_high/cylinder_.json b/Fatigue_Life/datasets/extracted_data/cylinder_high/cylinder_.json
new file mode 100644
index 0000000000000000000000000000000000000000..b013ff7ce2b19ee09359405d796bd0c7d0071b41
--- /dev/null
+++ b/Fatigue_Life/datasets/extracted_data/cylinder_high/cylinder_.json
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+{
+    "group_0": {
+        "geometry_folder": "life_D16_l70",
+        "number_of_nodes": 4693,
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+        "number_of_fatigue_life": 4693,
+        "data_type": "fatigue_life"
+    },
+    "group_1": {
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+        "number_of_nodes": 1341,
+        "number_of_cells": 4413,
+        "number_of_fatigue_life": 1341,
+        "data_type": "fatigue_life"
+    },
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+        "number_of_nodes": 1918,
+        "number_of_cells": 6593,
+        "number_of_fatigue_life": 1918,
+        "data_type": "fatigue_life"
+    },
+    "group_3": {
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+        "number_of_nodes": 4355,
+        "number_of_cells": 15699,
+        "number_of_fatigue_life": 4355,
+        "data_type": "fatigue_life"
+    },
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+        "number_of_fatigue_life": 2229,
+        "data_type": "fatigue_life"
+    },
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+        "number_of_nodes": 3383,
+        "number_of_cells": 12087,
+        "number_of_fatigue_life": 3383,
+        "data_type": "fatigue_life"
+    },
+    "group_6": {
+        "geometry_folder": "life_D6_l70",
+        "number_of_nodes": 1625,
+        "number_of_cells": 5497,
+        "number_of_fatigue_life": 1625,
+        "data_type": "fatigue_life"
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+        "number_of_nodes": 3125,
+        "number_of_cells": 11111,
+        "number_of_fatigue_life": 3125,
+        "data_type": "fatigue_life"
+    },
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+        "number_of_cells": 13758,
+        "number_of_fatigue_life": 3842,
+        "data_type": "fatigue_life"
+    },
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+        "number_of_cells": 10007,
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+        "data_type": "fatigue_life"
+    },
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+        "number_of_cells": 2239,
+        "number_of_fatigue_life": 734,
+        "data_type": "fatigue_life"
+    },
+    "group_11": {
+        "geometry_folder": "life_D4_l70",
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+        "number_of_cells": 3274,
+        "number_of_fatigue_life": 1020,
+        "data_type": "fatigue_life"
+    },
+    "group_12": {
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+        "number_of_cells": 8676,
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+        "number_of_cells": 14852,
+        "number_of_fatigue_life": 4140,
+        "data_type": "fatigue_life"
+    }
+}
\ No newline at end of file
diff --git a/Fatigue_Life/datasets/extracted_data/cylinder_low_.h5 b/Fatigue_Life/datasets/extracted_data/cylinder_low_.h5
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/Fatigue_Life/datasets/extracted_data/cylinder_low_.json b/Fatigue_Life/datasets/extracted_data/cylinder_low_.json
new file mode 100644
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--- /dev/null
+++ b/Fatigue_Life/datasets/extracted_data/cylinder_low_.json
@@ -0,0 +1,86 @@
+{
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+        "data_type": "fatigue_life"
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+    },
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+        "number_of_cells": 3319,
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+    },
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+        "number_of_fatigue_life": 1341,
+        "data_type": "fatigue_life"
+    }
+}
\ No newline at end of file
diff --git a/Fatigue_Life/datasets/extracted_data/shaft_.h5 b/Fatigue_Life/datasets/extracted_data/shaft_.h5
new file mode 100644
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diff --git a/Fatigue_Life/datasets/extracted_data/shaft_.json b/Fatigue_Life/datasets/extracted_data/shaft_.json
new file mode 100644
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+++ b/Fatigue_Life/datasets/extracted_data/shaft_.json
@@ -0,0 +1,128 @@
+{
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@@ -0,0 +1,3 @@
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+    },
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diff --git a/Fatigue_Life/datasets/extracted_data/shaft_low_extra_normalized__train.pt b/Fatigue_Life/datasets/extracted_data/shaft_low_extra_normalized__train.pt
new file mode 100644
index 0000000000000000000000000000000000000000..a816675a14516c61a34cad3ed06b31fb21b731ad
--- /dev/null
+++ b/Fatigue_Life/datasets/extracted_data/shaft_low_extra_normalized__train.pt
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+version https://git-lfs.github.com/spec/v1
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+size 2526346
diff --git a/Fatigue_Life/datasets/extracted_data/shaft_low_extra_normalized__val.pt b/Fatigue_Life/datasets/extracted_data/shaft_low_extra_normalized__val.pt
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+version https://git-lfs.github.com/spec/v1
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diff --git a/Fatigue_Life/datasets/save_h5.py b/Fatigue_Life/datasets/save_h5.py
new file mode 100644
index 0000000000000000000000000000000000000000..48b0818dcc6b9e4c959f138fc7b165d5d26a0c9d
--- /dev/null
+++ b/Fatigue_Life/datasets/save_h5.py
@@ -0,0 +1,95 @@
+import h5py
+import torch
+import json
+import sys
+import os
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from datasets.utilities.trajectory_solid import generate_trajectory_h5 as generate_trajectory
+# Add the utilities directory to Python path
+sys.path.append(os.path.join(os.path.dirname(__file__), 'utilities'))
+
+
+def list_subfolders(directory):
+    return [f.name for f in os.scandir(directory) if f.is_dir()]
+
+def save_fatigue_dataset_to_h5(raw_folder_path, output_folder_path, device):
+
+    basename = os.path.basename(raw_folder_path)
+    h5_path = os.path.join(output_folder_path, f"{basename}_.h5")
+    meta_json_path = os.path.join(output_folder_path, f"{basename}_.json")
+
+    with h5py.File(h5_path, 'w') as f:
+        group_to_folder_map = {}
+        group_counter = 0
+
+        # Each folder in raw_folder_path is a geometry folder
+        geometry_folders = list_subfolders(raw_folder_path)
+        print(f"Found {len(geometry_folders)} geometry folders")
+
+        for geometry_folder in geometry_folders:
+            geometry_path = os.path.join(raw_folder_path, geometry_folder)
+
+            print(f"Processing geometry: {geometry_folder}")
+
+            # Generate fatigue data from folder
+            trajectory= generate_trajectory(geometry_path, device)
+            #if len(trajectory) == 0:
+            #        continue
+            # Skip if no valid data
+            if trajectory is None:
+                print(f"Skipping invalid or empty: {geometry_folder}")
+                continue
+
+            # Create a unique group name
+            group_name = f"group_{group_counter}"
+            traj_group = f.create_group(group_name)
+
+            # Save all tensors to the group
+            for key, value in trajectory[0].items():
+                traj_group.create_dataset(key, data=value.cpu().numpy())
+
+            # Meta info
+            num_nodes = trajectory[0]['mesh_pos'].shape[0]
+            num_cells = trajectory[0]['cells'].shape[0]
+            num_fatigue_life = trajectory[0]['fatigue_life'].shape[0]
+            
+            group_to_folder_map[group_name] = {
+                'geometry_folder': geometry_folder,
+                'number_of_nodes': num_nodes,
+                'number_of_cells': num_cells,
+                'number_of_fatigue_life': num_fatigue_life,
+                'data_type': 'fatigue_life',
+            }
+
+
+            print(f"✓ Added group {group_name} for geometry {geometry_folder}")
+            print(f"  - Nodes: {num_nodes}, Cells: {num_cells}")
+            group_counter += 1
+
+        # Save meta JSON
+        with open(meta_json_path, 'w') as jf:
+            json.dump(group_to_folder_map, jf, indent=4)
+        print(f"Saved metadata to {meta_json_path}")
+        print(f"Processed {group_counter} geometries successfully")
+
+def main():
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    print(f"Using device: {device}")
+    
+    raw_folder_path = r"D:\AnK\FatigueNet\Fatigue_Life\datasets\Raw data\shaft_low_extra"
+    output_folder_path = r"D:\AnK\FatigueNet\Fatigue_Life\datasets\extracted_data"
+
+    # Check if raw folder exists
+    if not os.path.exists(raw_folder_path):
+        print(f"Error: Raw folder path does not exist: {raw_folder_path}")
+        return
+
+    # Create output directory if it doesn't exist
+    os.makedirs(output_folder_path, exist_ok=True)
+    print(f"Output will be saved to: {output_folder_path}")
+
+    # Convert data
+    save_fatigue_dataset_to_h5(raw_folder_path, output_folder_path, device)
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
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diff --git a/Fatigue_Life/surrogateAI/models/fatigue_eval.py b/Fatigue_Life/surrogateAI/models/fatigue_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..75d0e5ec26b379aa9236005e1242c476d9d525e0
--- /dev/null
+++ b/Fatigue_Life/surrogateAI/models/fatigue_eval.py
@@ -0,0 +1,18 @@
+import torch
+
+device = torch.device('cuda')
+
+def evaluate(model, trajectory, num_steps=None):
+    """Performs model rollouts and create stats."""
+    
+    prediction = model(trajectory, is_training=False)
+    
+    traj_ops = {
+        'cells': trajectory['cells'],
+        'mesh_pos': trajectory['mesh_pos'],
+        'gt_fatigue_life': trajectory['fatigue_life'],
+        'pred_fatigue_life': prediction,
+        'node_type': trajectory['node_type']
+    }
+    
+    return traj_ops
\ No newline at end of file
diff --git a/Fatigue_Life/surrogateAI/models/fatigue_model.py b/Fatigue_Life/surrogateAI/models/fatigue_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..294b429048dd429133d069efe3ab122bbdf596ba
--- /dev/null
+++ b/Fatigue_Life/surrogateAI/models/fatigue_model.py
@@ -0,0 +1,143 @@
+import torch
+from torch import nn as nn
+from models import normalization
+from utilities import common
+from models import regDGCNN_seg
+
+device = torch.device('cuda')
+class Model(nn.Module):
+    def __init__(self, params, core_model_name="regDGCNN_seg"):                                       
+        #params is a dictionray of some parameters like k and other
+        super(Model, self).__init__() # intitailize the super class or the contructor of the parent class
+        self._params = params
+        self._output_life_normalizer = normalization.Normalizer(size=1, name='output_pos_normalizer')
+        self.k = params['k']                                                                            # value of key 'k'
+        self._model_type = params['model'].__name__
+        self._displacement_base = None
+
+        self.core_model_name = core_model_name
+
+        if core_model_name == 'regDGCNN_seg':
+            self.core_model = regDGCNN_seg
+            self.is_multigraph = False,
+
+            self.learned_model = regDGCNN_seg.regDGCNN_seg(   # instatntiaing of the regDGCNN model of regdgcnn seg module (just object created which will be used later in the name of learned_model)
+                output_size=params['output_size'],            
+                input_dims=params['input_size'],  
+                k=self.k, 
+                emb_dims=1024,  
+                dropout=0.1 
+            )
+        else:
+            raise ValueError(f"Unsupported core model: {self.core_model_name}")
+
+    def forward(self, inputs, is_training):
+        if is_training:
+            return self.learned_model(self.process_inputs(inputs, device)) 
+        else: 
+            return self._update(self.learned_model(self.process_inputs(inputs, device)))
+            """✅ So, what does regDGCNN_seg.regDGCNN_seg return?
+            From conventions in DGCNN-style networks:
+
+            Input: [B, in_channels, N] → e.g., [1, 3, 1024] for batch size 1, 3D points
+
+            Output: [B, out_channels, N] → e.g., [1, 1, 1024] if you're doing per-node regression"""
+        """self.process_inputs(...)
+
+            Prepares the input tensor (shape: [1, input_features, num_points])
+
+            Example: converts [N, 3] → [1, 3, N] to match expected input of DGCNN.
+
+            self.learned_model(...)
+
+            Applies the core neural network (regDGCNN_seg) to this input.
+
+            Returns the raw output, e.g., predicted fatigue life per node (normalized)."""
+
+    def _update(self, per_node_network_output):
+        """Integrate model outputs."""
+        # print("per node network output shape",per_node_network_output.shape)
+        fatigue_pred = self._output_life_normalizer.inverse(per_node_network_output[:, :]) #The model internally reshapes from [1, 1, N] to [N, 1], or
+        # and denormalize the predicted output of fatigue life.
+        return (fatigue_pred)
+
+    def process_inputs(self, inputs, device): #input is also a dictionary of our datasets which contains mes pos, tyope fatigue life
+        
+        """
+        Processes input tensors and prepares features for graph construction.
+
+        Args:
+            inputs (dict): A dictionary containing 'curr_pos' and 'node_type'.
+            device (torch.device): The device to move tensors to.
+
+        Returns:
+            torch.Tensor: The processed feature tensor (1, num_features, num_points).
+            torch.Tensor: Node type tensor for filtering.
+        """
+        device = next(self.parameters()).device
+        mesh_pos = inputs['mesh_pos'].to(device)
+        node_type = inputs['node_type'].to(device)  # Shape: (num_points, 1)
+        radial_distance = inputs['radial_distance'].to(device)  # Shape: (num_points, num_dims)
+        r_norm = inputs['r_norm'].to(device)  # Shape: (num_points, 1)
+        z_norm = inputs['z_norm'].to(device)  # Shape: (num_points, 1)
+        d_step = inputs['d_step'].to(device)  # Shape: (num_points, 1)
+        d_step_norm = inputs['d_step_norm'].to(device)  # Shape: (num_points, 1)
+        
+        
+        #x = mesh_pos
+        # node_type = inputs['node_type'].to(device)
+        # region_node_type = inputs['region_node_type'].to(device)
+
+        # Create one-hot encodings for the categorical features
+        # node_type_one_hot = nn.functional.one_hot(node_type.squeeze(-1).long(), num_classes=self.num_node_type_classes).float()
+        # region_type_one_hot = nn.functional.one_hot(region_node_type.squeeze(-1).long(), num_classes=self.num_region_type_classes).float()
+
+        # Concatenate all features: position + node type + region type
+        # x = torch.cat([mesh_pos, node_type_one_hot, region_type_one_hot], dim=1)
+        # node_type = inputs['node_type'].to(device)  # Shape: (num_points,)
+
+        # Concatenate world position with one-hot encoded node types
+        #x = torch.cat((mesh_pos), dim=1)  # Shape: (num_points, num_features)
+        
+        #....................................
+                # node_type = inputs['node_type'].to(device)
+        # region_node_type = inputs['region_node_type'].to(device)
+
+        # Create one-hot encodings for the categorical features
+        # node_type_one_hot = nn.functional.one_hot(node_type.squeeze(-1).long(), num_classes=self.num_node_type_classes).float()
+        # region_type_one_hot = nn.functional.one_hot(region_node_type.squeeze(-1).long(), num_classes=self.num_region_type_classes).float()
+
+        # Concatenate all features: position + node type + region type
+        # x = torch.cat([mesh_pos, node_type_one_hot, region_type_one_hot], dim=1)
+        #.....................................
+        x = torch.cat([
+        mesh_pos,        # (N, 3)
+        radial_distance, # (N, 1)
+        r_norm,          # (N, 1)
+        z_norm,          # (N, 1)
+        d_step,          # (N, 1)
+        d_step_norm,      # (N, 1)
+        node_type       # (N, 1)
+        ], dim=1)  # (N, 8)
+        
+        
+        x = x.T.unsqueeze(0)  # Shape: ( num_features, num_points) 
+        #x.T → transposes the tensor from shape [N, F] to [F, N]
+        #unsqueeze(0) → adds a batch dimension at the front
+        return x # [1, F, N]  # Batch size 1, F features per node, N nodes (in case of only one feature other wise the final tensor would be the conactination of the one hot incoding of other featueres too)
+    
+     
+    def get_output_life_normalizer(self):
+        return (self._output_life_normalizer)
+    
+    def save_model(self, path):
+        torch.save(self.learned_model, path + "_learned_model.pth")
+        torch.save(self._output_life_normalizer, path + "_output_life_normalizer.pth")
+
+    def load_model(self, path):
+        self.learned_model = torch.load(path + "_learned_model.pth")
+        self._output_life_normalizer = torch.load(path + "_output_life_normalizer.pth")
+
+    def evaluate(self):
+        self.eval()
+        self.learned_model.eval()
diff --git a/Fatigue_Life/surrogateAI/models/normalization.py b/Fatigue_Life/surrogateAI/models/normalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..9746d497e9f43bd60f9a4ed00427624efadc6196
--- /dev/null
+++ b/Fatigue_Life/surrogateAI/models/normalization.py
@@ -0,0 +1,77 @@
+# Lint as: python3
+# pylint: disable=g-bad-file-header
+# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Online data normalization."""
+
+# import sonnet as snt
+# import tensorflow.compat.v1 as tf
+import torch
+import torch.nn as nn
+
+device = torch.device('cuda')
+
+
+# class Normalizer(snt.AbstractModule):
+class Normalizer(nn.Module):
+    """Feature normalizer that accumulates statistics online."""
+
+    def __init__(self, size, name, max_accumulations=10 ** 6, std_epsilon=1e-8, ):
+        super(Normalizer, self).__init__()
+        self._name = name
+        self._max_accumulations = max_accumulations
+        self._std_epsilon = torch.tensor([std_epsilon], requires_grad=False).to(device)
+
+        self._acc_count = torch.zeros(1, dtype=torch.float32, requires_grad=False).to(device)
+        self._num_accumulations = torch.zeros(1, dtype=torch.float32, requires_grad=False).to(device)
+        self._acc_sum = torch.zeros(size, dtype=torch.float32, requires_grad=False).to(device)
+        self._acc_sum_squared = torch.zeros(size, dtype=torch.float32, requires_grad=False).to(device)
+
+    def forward(self, batched_data, node_num=None, accumulate=True):
+        """Normalizes input data and accumulates statistics."""
+        if accumulate and self._num_accumulations < self._max_accumulations:
+            # stop accumulating after a million updates, to prevent accuracy issues
+            self._accumulate(batched_data)
+        return (batched_data - self._mean()) / self._std_with_epsilon()
+
+    def inverse(self, normalized_batch_data):
+        """Inverse transformation of the normalizer."""
+        return normalized_batch_data * self._std_with_epsilon() + self._mean()
+
+    def _accumulate(self, batched_data, node_num=None):
+        """Function to perform the accumulation of the batch_data statistics."""
+        count = torch.tensor(batched_data.shape[0], dtype=torch.float32, device=device)
+
+        data_sum = torch.sum(batched_data, dim=0)
+        squared_data_sum = torch.sum(batched_data ** 2, dim=0)
+        self._acc_sum = self._acc_sum.add(data_sum)
+        self._acc_sum_squared = self._acc_sum_squared.add(squared_data_sum)
+        self._acc_count = self._acc_count.add(count)
+        self._num_accumulations = self._num_accumulations.add(1.)
+
+    def _mean(self):
+        safe_count = torch.maximum(self._acc_count, torch.tensor([1.], device=device))
+        return self._acc_sum / safe_count
+
+    def _std_with_epsilon(self):
+        safe_count = torch.maximum(self._acc_count, torch.tensor([1.], device=device))
+        diff = self._acc_sum_squared / safe_count - self._mean() ** 2
+        if (diff < 0).any():
+            diff = self._std_epsilon**2
+        std = torch.sqrt(diff)
+        return torch.maximum(std, self._std_epsilon)
+
+    def get_acc_sum(self):
+        return self._acc_sum
diff --git a/Fatigue_Life/surrogateAI/models/regDGCNN_seg.py b/Fatigue_Life/surrogateAI/models/regDGCNN_seg.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fdcd171a90b80f05660d5b9cc2c89eadf9e8e5e
--- /dev/null
+++ b/Fatigue_Life/surrogateAI/models/regDGCNN_seg.py
@@ -0,0 +1,257 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+@author: Mohamed Elrefaie, mohamed.elrefaie@mit.edu mohamed.elrefaie@tum.de
+
+This module is part of the research presented in the paper:
+"DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks".
+
+This module is used to define both point-cloud based and graph-based models, including RegDGCNN, PointNet, and several Graph Neural Network (GNN) models
+for the task of surrogate modeling of the aerodynamic drag.
+"""
+import torch
+import torch.nn as nn
+from typing import Tuple
+
+device = torch.device('cuda')
+
+def intermediate(x, xx):
+    inner = -2*torch.matmul(x.transpose(2, 1), x)
+    torch.cuda.empty_cache()
+    return -xx - inner
+
+def knn(x, k):
+    x = x.to(torch.float16)
+    xx = torch.sum(x**2, dim=1, keepdim=True)
+    pairwise_distance = intermediate(x, xx) - xx.transpose(2, 1)
+    torch.cuda.empty_cache()
+    idx = pairwise_distance.topk(k=k, dim=-1)[1]   # (batch_size, num_points, k)
+    return idx
+
+
+def get_graph_feature(x, k=20, idx=None, dim9=False):
+    batch_size = x.size(0)
+    num_points = x.size(2)
+    x = x.view(batch_size, -1, num_points)
+    if idx is None:
+        if dim9 == False:
+            idx = knn(x, k=k)   # (batch_size, num_points, k)
+        else:
+            idx = knn(x[:, 6:], k=k)
+    torch.cuda.empty_cache()
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    idx_base = torch.arange(0, batch_size, device=device).view(-1, 1, 1)*num_points
+
+    idx = idx + idx_base
+
+    idx = idx.view(-1)
+
+    _, num_dims, _ = x.size()
+
+    x = x.transpose(2, 1).contiguous()   # (batch_size, num_points, num_dims)  -> (batch_size*num_points, num_dims) #   batch_size * num_points * k + range(0, batch_size*num_points)
+    feature = x.view(batch_size*num_points, -1)[idx, :]
+    feature = feature.view(batch_size, num_points, k, num_dims)
+    x = x.view(batch_size, num_points, 1, num_dims).repeat(1, 1, k, 1)
+
+    feature = torch.cat((feature-x, x), dim=3).permute(0, 3, 1, 2).contiguous()
+
+    torch.cuda.empty_cache()
+    return feature      # (batch_size, 2*num_dims, num_points, k)
+
+class regDGCNN_seg(nn.Module):
+    """
+    Graph-based segmentation network using Dynamic Graph CNN (DGCNN) blocks.
+    Processes point cloud inputs and produces per-point predictions.
+
+    Args:
+        output_size (int): Number of output channels per point (e.g., classes or regression dims).
+        input_dims (int): Dimensionality of input point features.
+        k (int): Number of nearest neighbors for graph construction.
+        emb_dims (int): Embedding dimensionality in the bottleneck layer.
+        dropout (float): Dropout probability for final classifier.
+    """
+
+    def __init__(
+        self,
+        output_size: int = 3,
+        input_dims: int = 6,
+        k: int = 20,
+        emb_dims: int = 1024,
+        dropout: float = 0.5
+    ):
+        super().__init__()
+        self.output_size = output_size
+        self.input_dims = input_dims
+        self.k = k
+        self.emb_dims = emb_dims
+        self.dropout = dropout
+
+        # GroupNorm layers for 2D convolutions (graph feature extractors)
+        self.bn1a  = nn.GroupNorm(num_groups=8,  num_channels=64)
+        self.bn1b  = nn.GroupNorm(num_groups=8,  num_channels=64)
+        self.bn2a  = nn.GroupNorm(num_groups=8,  num_channels=64)
+        self.bn2b  = nn.GroupNorm(num_groups=8,  num_channels=64)
+        self.bn3a  = nn.GroupNorm(num_groups=8,  num_channels=64)
+        self.bn3b = nn.GroupNorm(num_groups=8,  num_channels=64)
+        self.bn4a = nn.GroupNorm(num_groups=8,  num_channels=64)
+        self.bn4b = nn.GroupNorm(num_groups=8,  num_channels=64)
+
+        # GroupNorm layers for 1D convolutions 
+        self.bn5   = nn.GroupNorm(num_groups=16, num_channels=emb_dims)  
+        self.bn6   = nn.GroupNorm(num_groups=32, num_channels=1024)
+        self.bn7  = nn.GroupNorm(num_groups=16, num_channels=512)
+        self.bn8   = nn.GroupNorm(num_groups=16, num_channels=256)
+        
+        # Graph feature extraction layers
+        self.conv1a = nn.Sequential(
+            nn.Conv2d(input_dims * 2, 64, kernel_size=1, bias=False),
+            self.bn1a,
+            nn.LeakyReLU(0.2)
+        )  # First-edge features
+        self.conv1b = nn.Sequential(
+            nn.Conv2d(64, 64, kernel_size=1, bias=False),
+            self.bn1b,
+            nn.LeakyReLU(0.2)
+        )  # Second-edge features
+
+        # Second graph block uses concatenated features
+        self.conv2a = nn.Sequential(
+            nn.Conv2d(64 * 2, 64, kernel_size=1, bias=False),
+            self.bn2a,
+            nn.LeakyReLU(0.2)
+        )
+        self.conv2b = nn.Sequential(
+            nn.Conv2d(64, 64, kernel_size=1, bias=False),
+            self.bn2b,
+            nn.LeakyReLU(0.2)
+        )
+
+        # Third graph block
+        self.conv3a = nn.Sequential(
+            nn.Conv2d(64 * 2, 64, kernel_size=1, bias=False),
+            self.bn3a,
+            nn.LeakyReLU(0.2)
+        )
+        self.conv3b = nn.Sequential(
+            nn.Conv2d(64, 64, kernel_size=1, bias=False),
+            self.bn3b,
+            nn.LeakyReLU(0.2)
+        )
+
+        # Fourth graph block with two parallel convs
+        self.conv4a = nn.Sequential(
+            nn.Conv2d(64 * 2, 64, kernel_size=1, bias=False),
+            self.bn4a,
+            nn.LeakyReLU(0.2)
+        )
+        self.conv4b = nn.Sequential(
+            nn.Conv2d(64, 64, kernel_size=1, bias=False),
+            self.bn4b,
+            nn.LeakyReLU(0.2)
+        )
+
+        # Embedding layer - aggregates concatenated graph outputs
+        self.conv5 = nn.Sequential(
+            nn.Conv1d(64 * 4, emb_dims, kernel_size=1, bias=False),
+            self.bn5,
+            nn.LeakyReLU(0.2)
+        )
+
+        # Final per-point classifier
+        self.conv6 = nn.Sequential(
+            nn.Conv1d(emb_dims + 64 * 4, 1024, kernel_size=1, bias=False),
+            self.bn6,
+            nn.LeakyReLU(0.2)
+        )
+        self.conv7 = nn.Sequential(
+            nn.Conv1d(1024, 512, kernel_size=1, bias=False),
+            self.bn7,
+            nn.LeakyReLU(0.2)
+        )
+        self.conv8 = nn.Sequential(
+            nn.Conv1d(512, 256, kernel_size=1, bias=False),
+            self.bn8,
+            nn.LeakyReLU(0.2)
+        )
+        self.dp1 = nn.Dropout(self.dropout)
+        self.conv9 = nn.Conv1d(256, output_size, kernel_size=1, bias=False)
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass through the network.
+        Args:
+            inputs: Raw point cloud tensor (e.g., [B, dims, N]).
+            device: Torch device for processing.
+
+        Returns:
+            Per-point predictions of shape [B, output_size, N] or [N, output_size] if B==1.
+        """
+        # print('reached here')
+        # Preprocess inputs (e.g., feature selection or encoding)
+        B, _, N = inputs.size()
+
+        # ---- Graph block 1 ----
+        x1 = self._dgcnn_block(inputs, k=self.k, convs=[self.conv1a, self.conv1b])
+        # ---- Graph block 2 ----
+        x2 = self._dgcnn_block(x1, k=self.k, convs=[self.conv2a, self.conv2b])
+        # ---- Graph block 3 ----
+        x3 = self._dgcnn_block(x2, k=self.k, convs=[self.conv3a, self.conv3b])
+        # ---- Graph block 4 (parallel paths) ----
+        x4 = self._dgcnn_block(x3, k=self.k, convs=[self.conv4a, self.conv4b])
+
+        # Concatenate all local features
+        local_feats = torch.cat([x1, x2, x3, x4], dim=1)  # [
+
+        # Global embedding
+        emb = self.conv5(local_feats)   # [B, emb_dims, N]
+        emb = torch.max(emb, -1, keepdim=True)[0]  # [B, emb_dims, 1]
+        emb = emb.repeat(1, 1, N)  # Broadcast to all points
+
+        # Combine global and local features for classification
+        feats = torch.cat([emb, local_feats], dim=1)  # [B, emb_dims+64*4, N]
+
+        # Classification head
+        x = self.conv6(feats)
+        x = self.conv7(x)
+        x = self.conv8(x)
+        x = self.dp1(x)
+        x = self.conv9(x) 
+
+        # If batch size is 1, return [N, output_size]
+        if B == 1:
+            x = x.squeeze(0).permute(1, 0)
+        return x
+
+    def _dgcnn_block(
+        self,
+        x: torch.Tensor,
+        k: int,
+        convs: Tuple[nn.Sequential, nn.Sequential]
+    ) -> torch.Tensor:
+        """
+        Helper for two-layer DGCNN block: feature extraction, neighbor aggregation, and max pooling.
+        """
+        # Build graph features: [B, C*2, N, k]
+        x = get_graph_feature(x, k=k)
+        # Apply first conv + activation
+        x = convs[0](x)
+        # Apply second conv + activation
+        x = convs[1](x)
+        # Aggregate via max over neighbors
+        return x.max(dim=-1, keepdim=False)[0]
+
+    def _single_dgcnn(
+        self,
+        x: torch.Tensor,
+        k: int,
+        conv: nn.Sequential
+    ) -> torch.Tensor:
+        """
+        Single-layer DGCNN block: conv + max pooling over k neighbors.
+        """
+        x = get_graph_feature(x, k=k)
+        x = conv(x)
+        return x.max(dim=-1, keepdim=False)[0]
+
+
diff --git a/Fatigue_Life/surrogateAI/train.py b/Fatigue_Life/surrogateAI/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e4da8cadd0a0e01c66e46e963b3583b7d94a3f8
--- /dev/null
+++ b/Fatigue_Life/surrogateAI/train.py
@@ -0,0 +1,401 @@
+import os
+from pathlib import Path
+import pickle
+import time
+import datetime
+import argparse
+
+import torch
+from torch.nn.parallel import DistributedDataParallel as DDP
+import numpy as np
+from torch.optim.lr_scheduler import OneCycleLR
+
+from utilities import common
+from utilities.dataset import TrajectoryDataset
+
+# from models import fatigue_model
+from models import fatigue_model
+from models import fatigue_eval
+
+device = torch.device('cuda')
+import torch.nn as nn
+
+
+def squeeze_data_frame(data_frame):
+    for k, v in data_frame.items():
+        if isinstance(v, torch.Tensor): # This check solves the problem
+            data_frame[k] = torch.squeeze(v, 0)
+    return data_frame
+
+def squeeze_data(data):
+    # CORRECT: Only squeezes tensors, leaves other types alone
+    transformed_data = {key: value.squeeze(0) if isinstance(value, torch.Tensor) else value for key, value in data.items()}
+    return transformed_data
+
+def pickle_save(path, data):
+    with open(path, 'wb') as f:
+        pickle.dump(data, f)
+
+def pickle_load(path):
+    with open(path, 'rb') as f:
+        return pickle.load(f)
+
+'''
+# standard loss function for fatigue life prediction
+def loss_fn(inputs, network_output, model):
+    """
+    Calculates the L2 (Mean Squared Error) loss for position prediction.
+    
+    The model predicts where particles will move next, and this function
+    measures how far off those predictions are from the actual next positions.
+    """
+    # Extract current and target positions from input data
+    target_life = inputs['fatigue_life'].to(device)  # it stores the traget life or the ground truth of the target life which was given to this method as a input
+    #letter the input will take frame as its parameter and network output will take the return of forward () method of the model cclass which can be called directly by model(x) which is a feature of pytorch model.nn class
+    #it also takes model object as inpput too so that the normalizer method can be called.
+    # Normalize the target values using the model's built-in normalizer
+    target_normalizer = model.get_output_life_normalizer()
+    target_life_normalized = target_normalizer(target_life)
+
+    # Extract the life prediction
+    life_prediction = network_output[:,:1]
+
+    # prepare an error vector of length B
+    diff_life = target_life_normalized - life_prediction
+
+    loss = torch.mean(diff_life ** 2)
+
+    return  loss
+'''
+
+
+
+def loss_fn(inputs, network_output, w1=1.0, w2=1.0):
+    """
+    Custom loss function for predicting fatigue life in scientific form:
+        fatigue_life = a * 10^b
+    network_output: [num_nodes, 2] → columns: [a_pred, b_pred]
+    inputs['fatigue_life']: [num_nodes] or [num_nodes, 1]
+    """
+    # Move target to device and squeeze extra dimensions
+    target_life = inputs['fatigue_life'].to(device).squeeze(-1)  # [num_nodes]
+
+    # Convert true fatigue life to scientific form (a * 10^b)
+    true_b = torch.floor(torch.log10(target_life))      # exponent
+    true_a = target_life / (10 ** true_b)               # coefficient
+
+    # Predictions from network
+    pred_a = network_output[:, 0]  # [num_nodes]
+    pred_b = network_output[:, 1]  # [num_nodes]
+
+    # Compute separate MSE losses
+    criterion = nn.MSELoss()
+    loss_coeff = criterion(pred_a, true_a)
+    loss_exp   = criterion(pred_b, true_b)
+
+    # Weighted sum of losses
+    loss = w1 * loss_coeff + w2 * loss_exp
+
+    return loss
+
+'''
+def loss_fn(inputs, network_output):
+    """
+    Custom loss function for predicting fatigue life in scientific form:
+        fatigue_life = a * 10^b
+    network_output: [num_nodes, 2] → columns: [a_pred, b_pred]
+    inputs['fatigue_life']: [num_nodes] or [num_nodes, 1]
+    """
+    target_life = inputs['fatigue_life'].to(device).squeeze(-1)  # [num_nodes]
+
+    # Predictions from network
+    pred_a = network_output[:, 0]  # [num_nodes]
+    pred_b = network_output[:, 1]  # [num_nodes]
+
+    # Reconstruct predicted fatigue life
+    pred_life = pred_a * (10 ** pred_b)
+
+    # Compute MSE on log10 scale for numerical stability
+    loss = torch.mean((torch.log10(pred_life) - torch.log10(target_life)) ** 2)
+
+    return loss
+'''
+
+
+def prepare_files_and_directories(output_dir, model_num, train_data_path, experiment):
+    """
+    Creates a organized directory structure for saving training outputs.
+    
+    The structure will be:
+    output_dir/model_num/dataset_name/timestamp/
+    ├── checkpoint/  (saved model states)
+    ├── log/         (training metrics and logs)
+    └── rollout/     (evaluation results)
+    """
+    # Extract dataset name from the full path
+    train_data = train_data_path.split("/")[-1].split(".")[0]
+    output_dir = os.path.join(output_dir, str(model_num), train_data, f"EXPERIMENT_{experiment}")
+    
+    # Create a unique timestamp for this training run
+    run_create_time = time.time()
+    run_create_datetime = datetime.datetime.fromtimestamp(run_create_time).strftime('%c')
+    # Replace spaces and colons with dashes to make it filesystem-friendly
+    run_create_datetime_datetime_dash = run_create_datetime.replace(" ", "-").replace(":", "-")
+    
+    # Create the main run directory
+    run_dir = os.path.join(output_dir, run_create_datetime_datetime_dash)
+    Path(run_dir).mkdir(parents=True, exist_ok=True)
+    
+    # Create subdirectories for different types of outputs
+    checkpoint_dir = os.path.join(run_dir, 'checkpoint')  # For saving model weights
+    log_dir = os.path.join(run_dir, 'log')               # For training metrics
+    rollout_dir = os.path.join(run_dir, 'rollout')       # For evaluation results
+    
+    # Create all directories (parents=True creates intermediate dirs if needed)
+    Path(checkpoint_dir).mkdir(parents=True, exist_ok=True)
+    Path(log_dir).mkdir(parents=True, exist_ok=True)
+    Path(rollout_dir).mkdir(parents=True, exist_ok=True)
+    
+    return checkpoint_dir, log_dir, rollout_dir
+
+
+def save_checkpoint(checkpoint_dir, model, optimizer, scheduler, epoch, is_periodic = False):
+    """
+    Saves the current training state to checkpoint files.
+    """
+    try:
+        # Save epoch info
+        torch.save({'epoch': epoch}, Path(checkpoint_dir) / "epoch_checkpoint.pth")
+        
+        # Save model state with epoch number for periodic saves
+        if is_periodic:
+            model_checkpoint_name = f"epoch_{epoch + 1}_model_checkpoint"
+        else:
+            model_checkpoint_name = "epoch_model_checkpoint"
+            
+        model.save_model(str(Path(checkpoint_dir) / model_checkpoint_name))
+        
+        # Save optimizer and scheduler states
+        torch.save(optimizer.state_dict(), Path(checkpoint_dir) / "epoch_optimizer_checkpoint.pth")
+        torch.save(scheduler.state_dict(), Path(checkpoint_dir) / "epoch_scheduler_checkpoint.pth")
+        
+        print(f"Checkpoint saved for epoch {epoch+1}")
+        
+    except Exception as e:
+        print(f"Error saving checkpoint for epoch {epoch+1}: {e}")
+
+def main(args):
+    device = torch.device('cuda')
+
+    div_factor       = args.peak_lr / args.base_lr      # 5e-3 / 1e-3 = 5.0
+    final_div_factor = args.peak_lr / args.final_lr        # 5e-3 / 1e-4 = 50.0
+
+
+    start_epoch = 0
+    start_time = time.time()
+
+    end_epoch = args.epochs
+    print(f"starting training from epoch {start_epoch} to {end_epoch}")
+   
+    train_dataset = TrajectoryDataset(args.train_data, split='train')
+    val_dataset = TrajectoryDataset(args.val_data, split='val')
+ 
+    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)
+    val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=False)
+
+    params = dict(field='world_pos', output_size=args.output_size, model=fatigue_model, evaluator=fatigue_eval, k=args.neighbor_k, input_size=args.input_size)
+
+    core_model = 'regDGCNN_seg'
+    
+    model = fatigue_model.Model(params,core_model_name=core_model).to(device)  # create an object of the Model method of fatigue_module
+    #now the model takes the input of dict prams, model name 
+    optimizer = torch.optim.AdamW(model.parameters(), lr=args.base_lr, weight_decay=1e-3)
+    if args.scheduler == 'cosine':
+        scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0 = args.T_0, T_mult = args.T_mult)  
+    else:
+        scheduler = OneCycleLR(
+                optimizer,
+                max_lr=args.base_lr,
+                epochs=(end_epoch-start_epoch),
+                steps_per_epoch=len(train_dataloader),
+                pct_start=args.pct_start,
+                div_factor=div_factor,
+                final_div_factor=final_div_factor,
+                cycle_momentum=False
+            )
+  
+    checkpoint_dir, log_dir, rollout_dir = prepare_files_and_directories(args.output_dir,core_model,args.train_data, args.experiment_id)
+    
+    epoch_training_losses = []
+    epoch_learning_rate = []
+    step_training_losses = []
+    epoch_run_times = []
+    
+    #Save eval  losses
+    epoch_eval_losses = []
+
+
+    for epoch in range(start_epoch, end_epoch):
+        print(f"\n=== Epoch {epoch + 1}/{end_epoch} ===")
+        epoch_start_time = time.time()
+        epoch_training_loss = 0.0
+        print("---------------Training Started---------------")
+        model.train()
+        for data in train_dataloader:
+            frame = squeeze_data_frame(data)
+            output = model(frame,is_training=True)
+            loss  = loss_fn(frame, output,w1=1.0, w2=1.0) # input to an loss function is the input (frame) whihc is the data set of ours and model and output 
+            #letter the input will take frame as its parameter and network output will take the return of forward () method of the model cclass which can be called directly by model(x) which is a feature of pytorch model.nn class
+            #it also takes model object as inpput too so that the normalizer method can be called.
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+            scheduler.step()   
+            step_loss = loss.detach().cpu()
+            step_training_losses.append(step_loss)
+            epoch_training_loss += step_loss
+
+        # Record metrics for this epoch
+        epoch_training_losses.append(epoch_training_loss)
+        epoch_learning_rate.append(optimizer.param_groups[0]['lr'])
+        epoch_run_time = time.time() - epoch_start_time
+        epoch_run_times.append(epoch_run_time)
+        
+        print(f"Epoch {epoch + 1} completed, Training loss: {epoch_training_loss:.6f},  Time taken: {epoch_run_time:.2f}s,  Learning rate: {optimizer.param_groups[0]['lr']:.2e}")   
+        
+        loss_record = {
+            'train_total_loss': torch.sum(torch.stack(epoch_training_losses)).item(),
+            'train_mean_epoch_loss': torch.mean(torch.stack(epoch_training_losses)).item(),
+            'train_max_epoch_loss': torch.max(torch.stack(epoch_training_losses)).item(),
+            'train_min_epoch_loss': torch.min(torch.stack(epoch_training_losses)).item(),
+            'train_epoch_losses': epoch_training_losses,
+            'all_step_train_losses': 
+            step_training_losses,
+            'learning_rate': epoch_learning_rate,
+            'epoch_run_times': epoch_run_times
+        }
+
+        if len(epoch_training_losses) >= 2 and epoch_training_losses[-1]<epoch_training_losses[-2]:
+            model.save_model(str(Path(checkpoint_dir) / f"best_model_checkpoint_{epoch}"))
+            torch.save(optimizer.state_dict(), str(Path(checkpoint_dir) / "best_optimizer_checkpoint.pth"))
+            torch.save(scheduler.state_dict(), str(Path(checkpoint_dir) / "best_scheduler_checkpoint.pth"))
+        
+        should_evaluate = (epoch + 1) % 20 == 0 or epoch == start_epoch or (epoch + 1) == end_epoch
+        
+        if should_evaluate:
+            print(f"Saving checkpoint and evaluating at epoch {epoch + 1}...")
+            
+            # Save training metrics
+            temp_train_loss_file = Path(log_dir) / f'temp_train_loss_{epoch + 1}.pkl'
+            pickle_save(str(temp_train_loss_file), loss_record)
+            
+            # Save checkpoint
+            save_checkpoint(checkpoint_dir, model, optimizer, scheduler, epoch)
+            
+            print("Running evaluation...")
+            model.eval()
+            
+            trajectories = []  # Store predicted trajectories
+            mse_losses = []    # Mean Squared Error losses
+            l1_losses = []     # Mean Absolute Error losses
+           
+            # Set up loss functions for evaluation
+            mse_loss_fn = torch.nn.MSELoss()  # Mean Squared Error
+            l1_loss_fn = torch.nn.L1Loss()    # Mean Absolute Error
+            
+            with torch.no_grad():
+                for data in val_loader:
+                    data=squeeze_data(data)
+                    # print(data)
+                    prediction_trajectory = fatigue_eval.evaluate(model, data)
+                    
+                    # Calculate different types of loss metrics
+                    target = torch.squeeze(data['fatigue_life'].to(device), dim=0)
+
+                    # Extract predictions from the trajectory
+                    pred = prediction_trajectory['pred_fatigue_life']
+
+                    # Calculate losses
+                    mse_loss = mse_loss_fn(target, pred)
+                    l1_loss = l1_loss_fn(target, pred)
+
+                    # Store the results
+                    mse_losses.append(mse_loss.cpu())
+                    l1_losses.append(l1_loss.cpu())                 
+                    
+                    trajectories.append(prediction_trajectory)
+            epoch_eval_losses.append(torch.mean(torch.stack(mse_losses)).item())     
+            # Save evaluation trajectories
+            rollout_file = Path(rollout_dir) / f"rollout_epoch_{epoch + 1}.pkl"
+            pickle_save(str(rollout_file), trajectories)
+# So this val loop over val dataloader( whihc consiste the sample of ahsfts that were splited into this validation datasets.) Now similar to the data in data loader.
+# this aslo iterates thorugh the val shafts sample but this time the training is false so all dropout are off so that model know this is just validating. Here it uses the learned model from abpuve trainina.
+# That is the letest wights and biases are used in this step and the model predicts the output. Now the output of the model is compared with the ground truth of the sample shaft or the batch shaft and loss is calculated.
+#It is the same stpes of getting the predicted output from the model and calculating the loss function.
+# the only difference is that dufing train dataloader the predicted valuses were compared with the same sample shaft which was input into the model with random wights and biases or updated wights and biases from previous shaft.
+# But here the loss is calcualted with the same dataset which is present inside the train val loader for every 20 epoch.
+            # Create comprehensive evaluation metrics
+            eval_loss_record = {
+                'eval_total_mse_loss': torch.sum(torch.stack(mse_losses)).item(),
+                'eval_total_l1_loss': torch.sum(torch.stack(l1_losses)).item(),
+                'eval_mean_mse_loss': torch.mean(torch.stack(mse_losses)).item(),
+                'eval_max_mse_loss': torch.max(torch.stack(mse_losses)).item(),
+                'eval_min_mse_loss': torch.min(torch.stack(mse_losses)).item(),
+                'eval_mean_l1_loss': torch.mean(torch.stack(l1_losses)).item(),
+                'eval_max_l1_loss': torch.max(torch.stack(l1_losses)).item(),
+                'eval_min_l1_loss': torch.min(torch.stack(l1_losses)).item(),
+                'eval_mse_losses': mse_losses,
+                'eval_l1_losses': l1_losses,
+                'epoch_eval_losses': epoch_eval_losses,
+            }
+            
+            # Save evaluation metrics
+            eval_loss_file = Path(log_dir) / f'eval_loss_epoch_{epoch + 1}.pkl'
+            pickle_save(str(eval_loss_file), eval_loss_record)
+
+            print(f"Evaluation completed,   Mean MSE Loss: {eval_loss_record['eval_mean_mse_loss']:.6f}, Mean L1 Loss: {eval_loss_record['eval_mean_l1_loss']:.6f}")
+
+    print("\nTraining completed! Saving final results...")
+    # Save final comprehensive training loss record
+    final_loss_record = {
+        'train_total_loss': torch.sum(torch.stack(epoch_training_losses)).item() if epoch_training_losses else 0,
+        'train_mean_epoch_loss': torch.mean(torch.stack(epoch_training_losses)).item() if epoch_training_losses else 0,
+        'train_max_epoch_loss': torch.max(torch.stack(epoch_training_losses)).item() if epoch_training_losses else 0,
+        'train_min_epoch_loss': torch.min(torch.stack(epoch_training_losses)).item() if epoch_training_losses else 0,
+        'train_epoch_losses': epoch_training_losses,
+        'all_step_train_losses': step_training_losses,
+        'learning_rate': epoch_learning_rate,
+        'epoch_run_times': epoch_run_times
+    }
+    
+    # Save final timing information
+    pickle_save(str(Path(log_dir) / 'epoch_run_times.pkl'), epoch_run_times)
+    pickle_save(str(Path(log_dir) / 'final_train_loss.pkl'), final_loss_record)
+    
+    # Save final model state
+    model.save_model(str(Path(checkpoint_dir) / "final_model_checkpoint"))
+    torch.save(optimizer.state_dict(), str(Path(checkpoint_dir) / "final_optimizer_checkpoint.pth"))
+    torch.save(scheduler.state_dict(), str(Path(checkpoint_dir) / "final_scheduler_checkpoint.pth"))
+    return
+    
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--train_data', type=str, default="/home/gd_user1/AnK/project_PINN/Project_Fatigue/Fatigue_Life/datasets/extracted_data/shaft_low_extra2_.h5")
+    parser.add_argument('--val_data', type=str, default="/home/gd_user1/AnK/project_PINN/Project_Fatigue/Fatigue_Life/datasets/extracted_data/shaft_low_extra2_.h5")
+    parser.add_argument('--output_dir', type=str, default="/home/gd_user1/AnK/project_PINN/Project_Fatigue/Fatigue_Life/output_shaft_extra")
+    parser.add_argument('--batch_size', type=int, default=1)
+    parser.add_argument('--epochs', type=int, default=1500)
+    parser.add_argument('--base_lr', type=float, default=2e-4)
+    parser.add_argument('--peak_lr', type=float, default=8e-4)
+    parser.add_argument('--final_lr', type=float, default=5e-5)
+    parser.add_argument('--pct_start', type=float, default=0.1)
+    parser.add_argument('--T_0', type=int, default=10)
+    parser.add_argument('--T_mult', type=int, default=2)
+    parser.add_argument('--scheduler', type=str, default="cosine", choices=["cosine", "onecycle"])
+    parser.add_argument('--experiment_id', type=str, default="multi_feat_exponent_k60_500_shaft_low_extra2")
+    parser.add_argument('--neighbor_k', type=int, default=60)
+    parser.add_argument('--model_name', type=str, default="regDGCNN_seg")
+    parser.add_argument('--input_size', type=int, default=9)
+    parser.add_argument('--output_size', type=int, default=2)
+    args = parser.parse_args()
+    main(args)
\ No newline at end of file
diff --git a/Fatigue_Life/surrogateAI/train_dest.py b/Fatigue_Life/surrogateAI/train_dest.py
new file mode 100644
index 0000000000000000000000000000000000000000..79fdc7c6364e141b7e9027521e66748f61fe166a
--- /dev/null
+++ b/Fatigue_Life/surrogateAI/train_dest.py
@@ -0,0 +1,373 @@
+import os
+from pathlib import Path
+import pickle
+import time
+import datetime
+import argparse
+
+import torch
+from torch.nn.parallel import DistributedDataParallel as DDP
+import numpy as np
+from torch.optim.lr_scheduler import OneCycleLR
+# from model import DGCNN, MagNet
+from utilities import common
+from utilities.dataset import TrajectoryDataset
+
+# from models import press_model
+from models import dest_model as press_model
+from models import dest_eval as press_eval
+
+
+device = torch.device('cuda')
+
+
+def squeeze_data_frame(data_frame):
+    for k, v in data_frame.items():
+        data_frame[k] = torch.squeeze(v, 0)
+    return data_frame
+
+def pickle_save(path, data):
+    with open(path, 'wb') as f:
+        pickle.dump(data, f)
+
+def pickle_load(path):
+    with open(path, 'rb') as f:
+        return pickle.load(f)
+
+def loss_fn(inputs, network_output, model):
+    """
+    Calculates the L2 (Mean Squared Error) loss for position prediction.
+    
+    The model predicts where particles will move next, and this function
+    measures how far off those predictions are from the actual next positions.
+    """
+    # Extract current and target positions from input data
+    world_pos = inputs['curr_pos'].to(device)  # Where particles are now
+    target_world_pos = inputs['next_pos'].to(device)  # Target positions at next time step
+    target_von = inputs['next_von'].to(device)  # Target von Mises stress at next position
+
+    # Calculate the expected movement (velocity) between time steps
+    target_pos_velocity = target_world_pos - world_pos # This is what we want to predict
+    
+    # Normalize the target values using the model's built-in normalizer
+    # This helps with training stability by keeping values in a reasonable range
+    world_pos_normalizer = model.get_output_pos_normalizer()
+    target_pos_normalized = world_pos_normalizer(target_pos_velocity)
+
+    von_normalizer = model.get_output_von_normalizer()
+    target_von_normalized = von_normalizer(target_von)
+
+    # Create a mask to only calculate loss for "normal" particles
+    # Some particles might be boundary conditions or special types we don't want to predict
+    node_type = inputs['node_type'].to(device)
+    node_type      = node_type.view(-1)       
+
+    # make masks
+    mask_normal = (node_type == common.NodeType.NORMAL.value)   # predict x,y,z
+    mask_roller = (node_type == common.NodeType.ROLLER.value)   # predict x,y
+    valid_mask  = mask_normal | mask_roller              # exclude obstacles
+    
+    
+    # Extract the position prediction from the model output (first 3 dimensions: x, y, z)
+    pos_prediction = network_output[:,:3]
+    von_prediction = network_output[:,3:]
+    
+    # prepare an error vector of length B
+    diff_pos = target_pos_normalized - pos_prediction
+    diff_von = target_von_normalized - von_prediction
+    
+    error = torch.zeros(diff_pos.shape[0], device=device)
+    
+    # full-3D error for NORMAL nodes
+    if mask_normal.any():
+        err_N = torch.sum(diff_pos[mask_normal] ** 2, dim=1)
+        error[mask_normal] = err_N
+        
+    # x–y only error for ROLLER nodes
+    if mask_roller.any():
+        # dim 0=x, dim 1=y
+        err_R = diff_pos[mask_roller][:, 0]**2 + diff_pos[mask_roller][:, 1]**2
+        error[mask_roller] = err_R
+        
+    # final loss: mean over all non‐obstacle nodes
+    if valid_mask.any():
+        loss_pos = torch.mean(error[valid_mask])    
+    else:
+        loss_pos = torch.tensor(0.0, device=device)
+    
+    loss_von = torch.mean(diff_von ** 2)
+    
+    loss = loss_pos + loss_von
+    return  loss
+
+def prepare_files_and_directories(output_dir, model_num, train_data_path, experiment):
+    """
+    Creates a organized directory structure for saving training outputs.
+    
+    The structure will be:
+    output_dir/model_num/dataset_name/timestamp/
+    ├── checkpoint/  (saved model states)
+    ├── log/         (training metrics and logs)
+    └── rollout/     (evaluation results)
+    """
+    # Extract dataset name from the full path
+    train_data = train_data_path.split("/")[-1].split(".")[0]
+    output_dir = os.path.join(output_dir, str(model_num), train_data, f"EXPERIMENT_{experiment}")
+    
+    # Create a unique timestamp for this training run
+    run_create_time = time.time()
+    run_create_datetime = datetime.datetime.fromtimestamp(run_create_time).strftime('%c')
+    # Replace spaces and colons with dashes to make it filesystem-friendly
+    run_create_datetime_datetime_dash = run_create_datetime.replace(" ", "-").replace(":", "-")
+    
+    # Create the main run directory
+    run_dir = os.path.join(output_dir, run_create_datetime_datetime_dash)
+    Path(run_dir).mkdir(parents=True, exist_ok=True)
+    
+    # Create subdirectories for different types of outputs
+    checkpoint_dir = os.path.join(run_dir, 'checkpoint')  # For saving model weights
+    log_dir = os.path.join(run_dir, 'log')               # For training metrics
+    rollout_dir = os.path.join(run_dir, 'rollout')       # For evaluation results
+    
+    # Create all directories (parents=True creates intermediate dirs if needed)
+    Path(checkpoint_dir).mkdir(parents=True, exist_ok=True)
+    Path(log_dir).mkdir(parents=True, exist_ok=True)
+    Path(rollout_dir).mkdir(parents=True, exist_ok=True)
+    
+    return checkpoint_dir, log_dir, rollout_dir
+
+def squeeze_data(data):
+    transformed_data = {key: value.squeeze(0) for key, value in data.items()}
+    return transformed_data
+
+def save_checkpoint(checkpoint_dir, model, optimizer, scheduler, epoch, is_periodic = False):
+    """
+    Saves the current training state to checkpoint files.
+    """
+    try:
+        # Save epoch info
+        torch.save({'epoch': epoch}, Path(checkpoint_dir) / "epoch_checkpoint.pth")
+        
+        # Save model state with epoch number for periodic saves
+        if is_periodic:
+            model_checkpoint_name = f"epoch_{epoch + 1}_model_checkpoint"
+        else:
+            model_checkpoint_name = "epoch_model_checkpoint"
+            
+        model.save_model(str(Path(checkpoint_dir) / model_checkpoint_name))
+        
+        # Save optimizer and scheduler states
+        torch.save(optimizer.state_dict(), Path(checkpoint_dir) / "epoch_optimizer_checkpoint.pth")
+        torch.save(scheduler.state_dict(), Path(checkpoint_dir) / "epoch_scheduler_checkpoint.pth")
+        
+        print(f"Checkpoint saved for epoch {epoch+1}")
+        
+    except Exception as e:
+        print(f"Error saving checkpoint for epoch {epoch+1}: {e}")
+
+def main(args):
+    device = torch.device('cuda')
+
+    div_factor       = args.peak_lr / args.base_lr      # 5e-3 / 1e-3 = 5.0
+    final_div_factor = args.peak_lr / args.final_lr        # 5e-3 / 1e-4 = 50.0
+
+
+    start_epoch = 0
+    start_time = time.time()
+
+    end_epoch = args.epochs
+    print(f"starting training from epoch {start_epoch} to {end_epoch}")
+   
+    train_dataset = TrajectoryDataset(args.train_data, split='train')
+    val_dataset = TrajectoryDataset(args.val_data, split='val')
+ 
+    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)
+    val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=True)
+
+    params = dict(field='world_pos', output_size=args.output_size, model=press_model, evaluator=press_eval, k=args.neighbor_k, input_size=args.input_size)
+
+    core_model = 'regDGCNN_seg'
+    
+    model = press_model.Model(params,core_model_name=core_model).to(device)
+
+    optimizer = torch.optim.AdamW(model.parameters(), lr=args.base_lr, weight_decay=1e-3)
+    if args.scheduler == 'cosine':
+        scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0 = args.T_0, T_mult = args.T_mult)  
+    else:
+        scheduler = OneCycleLR(
+                optimizer,
+                max_lr=args.base_lr,
+                epochs=(end_epoch-start_epoch),
+                steps_per_epoch=len(train_dataloader),
+                pct_start=args.pct_start,
+                div_factor=div_factor,
+                final_div_factor=final_div_factor,
+                cycle_momentum=False
+            )
+  
+
+    checkpoint_dir, log_dir, rollout_dir = prepare_files_and_directories(args.output_dir,core_model,args.train_data, args.experiment_id)
+    
+    epoch_training_losses = []
+    epoch_learning_rate = []
+    step_training_losses = []
+    epoch_run_times = []
+    epoch_eval_losses = []
+
+    for epoch in range(start_epoch, end_epoch):
+        print(f"\n=== Epoch {epoch + 1}/{end_epoch} ===")
+        epoch_start_time = time.time()
+        epoch_training_loss = 0.0
+        print("---------------Training Started---------------")
+        model.train()
+        for data in train_dataloader:
+            frame = squeeze_data_frame(data)
+            output = model(frame,is_training=True)
+            loss = loss_fn(frame, output, model)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+            scheduler.step()   
+            step_loss = loss.detach().cpu()
+            step_training_losses.append(step_loss)
+            epoch_training_loss += step_loss
+
+        # Record metrics for this epoch
+        epoch_training_losses.append(epoch_training_loss)
+        epoch_learning_rate.append(optimizer.param_groups[0]['lr'])
+        epoch_run_time = time.time() - epoch_start_time
+        epoch_run_times.append(epoch_run_time)
+        
+        print(f"Epoch {epoch + 1} completed, Training loss: {epoch_training_loss:.6f},  Time taken: {epoch_run_time:.2f}s,  Learning rate: {optimizer.param_groups[0]['lr']:.2e}")        
+        
+        loss_record = {
+            'train_total_loss': torch.sum(torch.stack(epoch_training_losses)).item(),
+            'train_mean_epoch_loss': torch.mean(torch.stack(epoch_training_losses)).item(),
+            'train_max_epoch_loss': torch.max(torch.stack(epoch_training_losses)).item(),
+            'train_min_epoch_loss': torch.min(torch.stack(epoch_training_losses)).item(),
+            'train_epoch_losses': epoch_training_losses,
+            'all_step_train_losses': step_training_losses,
+            'learning_rate': epoch_learning_rate,
+            'epoch_run_times': epoch_run_times
+        }
+
+        should_evaluate = (epoch + 1) % 20 == 0 or epoch == start_epoch or (epoch + 1) == end_epoch
+        
+        if should_evaluate:
+            print(f"Saving checkpoint and evaluating at epoch {epoch + 1}...")
+            
+            # Save training metrics
+            temp_train_loss_file = Path(log_dir) / f'temp_train_loss_{epoch + 1}.pkl'
+            pickle_save(str(temp_train_loss_file), loss_record)
+            
+            # Save checkpoint
+            save_checkpoint(checkpoint_dir, model, optimizer, scheduler, epoch)
+            
+            print("Running evaluation...")
+            model.eval()
+            
+            trajectories = []  # Store predicted trajectories
+            mse_losses = []    # Mean Squared Error losses
+            l1_losses = []     # Mean Absolute Error losses
+            
+            # Set up loss functions for evaluation
+            mse_loss_fn = torch.nn.MSELoss()  # Mean Squared Error
+            l1_loss_fn = torch.nn.L1Loss()    # Mean Absolute Error
+            
+            with torch.no_grad():
+                for data in val_loader:
+                    data=squeeze_data(data)
+                    # print(data)
+                    _, prediction_trajectory = press_eval.evaluate(model, data)
+                    # Calculate different types of loss metrics
+                    target_pos = torch.squeeze(data['next_pos'].to(device), dim=0)
+                    target_von = torch.squeeze(data['next_von'].to(device), dim=0)
+
+                    # Extract predictions from the trajectory
+                    pred_pos = prediction_trajectory['pred_pos']
+                    pred_von = prediction_trajectory['pred_von']
+
+                    # Calculate losses
+                    mse_loss_pos = mse_loss_fn(target_pos, pred_pos)
+                    l1_loss_pos = l1_loss_fn(target_pos, pred_pos)
+                    mse_loss_von = mse_loss_fn(target_von, pred_von)
+                    l1_loss_von = l1_loss_fn(target_von, pred_von)
+                    
+                    # Combine losses
+                    mse_loss = mse_loss_pos + mse_loss_von
+                    l1_loss = l1_loss_pos + l1_loss_von
+                    
+                    # Store the results
+                    mse_losses.append(mse_loss.cpu())
+                    l1_losses.append(l1_loss.cpu())
+                    trajectories.append(prediction_trajectory)
+            epoch_eval_losses.append(torch.mean(torch.stack(mse_losses)).item())     
+            # Save evaluation trajectories
+            rollout_file = Path(rollout_dir) / f"rollout_epoch_{epoch + 1}.pkl"
+            pickle_save(str(rollout_file), trajectories)
+
+            # Create comprehensive evaluation metrics
+            eval_loss_record = {
+                'eval_total_mse_loss': torch.sum(torch.stack(mse_losses)).item(),
+                'eval_total_l1_loss': torch.sum(torch.stack(l1_losses)).item(),
+                'eval_mean_mse_loss': torch.mean(torch.stack(mse_losses)).item(),
+                'eval_max_mse_loss': torch.max(torch.stack(mse_losses)).item(),
+                'eval_min_mse_loss': torch.min(torch.stack(mse_losses)).item(),
+                'eval_mean_l1_loss': torch.mean(torch.stack(l1_losses)).item(),
+                'eval_max_l1_loss': torch.max(torch.stack(l1_losses)).item(),
+                'eval_min_l1_loss': torch.min(torch.stack(l1_losses)).item(),
+                'eval_mse_losses': mse_losses,
+                'eval_l1_losses': l1_losses,
+                'epoch_eval_losses': epoch_eval_losses
+            }
+            
+            # Save evaluation metrics
+            eval_loss_file = Path(log_dir) / f'eval_loss_epoch_{epoch + 1}.pkl'
+            pickle_save(str(eval_loss_file), eval_loss_record)
+
+            print(f"Evaluation completed,   Mean MSE Loss: {eval_loss_record['eval_mean_mse_loss']:.6f}, Mean L1 Loss: {eval_loss_record['eval_mean_l1_loss']:.6f}")
+
+    print("\nTraining completed! Saving final results...")
+    # Save final comprehensive training loss record
+    final_loss_record = {
+        'train_total_loss': torch.sum(torch.stack(epoch_training_losses)).item() if epoch_training_losses else 0,
+        'train_mean_epoch_loss': torch.mean(torch.stack(epoch_training_losses)).item() if epoch_training_losses else 0,
+        'train_max_epoch_loss': torch.max(torch.stack(epoch_training_losses)).item() if epoch_training_losses else 0,
+        'train_min_epoch_loss': torch.min(torch.stack(epoch_training_losses)).item() if epoch_training_losses else 0,
+        'train_epoch_losses': epoch_training_losses,
+        'all_step_train_losses': step_training_losses,
+        'learning_rate': epoch_learning_rate,
+        'epoch_run_times': epoch_run_times
+    }
+    
+    # Save final timing information
+    pickle_save(str(Path(log_dir) / 'epoch_run_times.pkl'), epoch_run_times)
+    pickle_save(str(Path(log_dir) / 'final_train_loss.pkl'), final_loss_record)
+    
+    # Save final model state
+    model.save_model(str(Path(checkpoint_dir) / "final_model_checkpoint"))
+    torch.save(optimizer.state_dict(), str(Path(checkpoint_dir) / "final_optimizer_checkpoint.pth"))
+    torch.save(scheduler.state_dict(), str(Path(checkpoint_dir) / "final_scheduler_checkpoint.pth"))
+    return
+    
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--train_data', type=str, default="/home/rachit/GlassForming/processed/train.h5")
+    parser.add_argument('--val_data', type=str, default="/home/rachit/GlassForming/processed/val.h5")
+    parser.add_argument('--output_dir', type=str, default="/home/rachit/GlassForming/new_final_code_output")
+    parser.add_argument('--batch_size', type=int, default=1)
+    parser.add_argument('--epochs', type=int, default=2000)
+    parser.add_argument('--base_lr', type=float, default=2e-4)
+    parser.add_argument('--peak_lr', type=float, default=8e-4)
+    parser.add_argument('--final_lr', type=float, default=5e-5)
+    parser.add_argument('--pct_start', type=float, default=0.1)
+    parser.add_argument('--T_0', type=int, default=10)
+    parser.add_argument('--T_mult', type=int, default=2)
+    parser.add_argument('--scheduler', type=str, default="cosine", choices=["cosine", "onecycle"])
+    parser.add_argument('--experiment_id', type=str, default="400")
+    parser.add_argument('--neighbor_k', type=int, default=80)
+    parser.add_argument('--model_name', type=str, default="regDGCNN_seg")
+    parser.add_argument('--input_size', type=int, default=11)
+    parser.add_argument('--output_size', type=int, default=4)
+    args = parser.parse_args()
+    main(args)
\ No newline at end of file
diff --git a/Fatigue_Life/surrogateAI/utilities/Test2Dataset b/Fatigue_Life/surrogateAI/utilities/Test2Dataset
new file mode 100644
index 0000000000000000000000000000000000000000..617a4ae2bece43a35ebb1e20fac57e9016c7de24
--- /dev/null
+++ b/Fatigue_Life/surrogateAI/utilities/Test2Dataset
@@ -0,0 +1,262 @@
+import torch
+from torch.utils.data import Dataset
+import h5py
+import os
+import numpy as np
+
+class ShaftFatigueDataset(Dataset):
+    def __init__(self, data_path, split='train', normalize=True):
+        self.data_path = data_path
+        self.split = split
+        self.normalize = normalize
+        self.is_processed = self._is_processed()
+
+        if not self.is_processed:
+            self.process_data()
+        self.data = self.load_data()
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        return self.data[idx]
+
+    def _is_processed(self):
+        return os.path.exists(self.data_path.replace('.h5', f'_{self.split}.pt'))
+
+    def process_data(self):
+        print("Processing shaft fatigue data...")
+        
+        final_data = []
+        
+        with h5py.File(self.data_path, 'r') as h5_file:
+            # Get all group names (group_0 to group_15)
+            group_names = [f'group_{i}' for i in range(16)]  # Groups group_0 to group_15
+            total_groups = len(group_names)
+            
+            # Split data: 70% train, 15% val, 15% test
+            if self.split == 'train':
+                group_limit = round(total_groups * 0.7)  # 0 to 10 (11 groups)
+                selected_groups = group_names[:group_limit]
+            elif self.split == 'val':
+                group_limit_start = round(total_groups * 0.7)  # 11
+                group_limit_end = round(total_groups * 0.85)   # 13
+                if group_limit_end == group_limit_start:
+                    selected_groups = group_names[group_limit_start:]
+                else:
+                    selected_groups = group_names[group_limit_start:group_limit_end]
+            else:  # test
+                group_limit_start = round(total_groups * 0.85)  # 14
+                if group_limit_start == total_groups:
+                    group_limit_start -= 1
+                selected_groups = group_names[group_limit_start:]
+            
+            print(f"Selected groups for {self.split}: {selected_groups}")
+            
+            # Store normalization statistics for fatigue life
+            all_fatigue_life = []
+            
+            # First pass: collect all fatigue life data for normalization
+            if self.normalize:
+                print("Collecting fatigue life data for normalization...")
+                for group_name in group_names:
+                    if group_name in h5_file:
+                        group = h5_file[group_name]
+                        if 'fatigue_life' in group:
+                            fatigue_life = group['fatigue_life'][:]
+                            if fatigue_life.ndim == 2 and fatigue_life.shape[1] == 1:
+                                fatigue_life = fatigue_life.squeeze(-1)
+                            all_fatigue_life.append(fatigue_life)
+                
+                if all_fatigue_life:
+                    all_fatigue_life = np.concatenate(all_fatigue_life)
+                    self.fatigue_mean = np.mean(all_fatigue_life)
+                    self.fatigue_std = np.std(all_fatigue_life)
+                    print(f"Fatigue life normalization - Mean: {self.fatigue_mean:.4f}, Std: {self.fatigue_std:.4f}")
+                else:
+                    print("No fatigue life data found for normalization")
+            
+            # Second pass: process selected groups
+            for group_name in selected_groups:
+                if group_name not in h5_file:
+                    print(f"Warning: Group {group_name} not found in file")
+                    continue
+                    
+                print(f"Processing group {group_name}")
+                group = h5_file[group_name]
+                
+                # Extract data from each group
+                try:
+                    # Read datasets
+                    cells = torch.tensor(group['cells'][:], dtype=torch.float32)  # Changed to float32
+                    mesh_pos = torch.tensor(group['mesh_pos'][:], dtype=torch.float32)  
+                    node_type = torch.tensor(group['node_type'][:], dtype=torch.float32)  # Changed to float32
+                    fatigue_life = torch.tensor(group['fatigue_life'][:], dtype=torch.float32)  
+                    
+                    # Print shapes for debugging
+                    print(f"  cells: {cells.shape}")
+                    print(f"  mesh_pos: {mesh_pos.shape}")
+                    print(f"  node_type: {node_type.shape}")
+                    print(f"  fatigue_life: {fatigue_life.shape}")
+                    
+                    # Handle shape inconsistencies
+                    # Find the minimum number of nodes across all arrays
+                    num_nodes_mesh = mesh_pos.shape[0]
+                    num_nodes_type = node_type.shape[0]
+                    num_nodes_fatigue = fatigue_life.shape[0]
+                    
+                    # Use the minimum to ensure consistency
+                    min_nodes = min(num_nodes_mesh, num_nodes_type, num_nodes_fatigue)
+                    print(f"  Using {min_nodes} nodes (min of {num_nodes_mesh}, {num_nodes_type}, {num_nodes_fatigue})")
+                    
+                    # Trim arrays to consistent size
+                    mesh_pos = mesh_pos[:min_nodes]
+                    node_type = node_type[:min_nodes]
+                    fatigue_life = fatigue_life[:min_nodes]
+                    
+                    # Ensure node_type is 2D
+                    if node_type.dim() == 1:
+                        node_type = node_type.unsqueeze(-1)
+                    
+                    # Ensure fatigue_life is 1D
+                    if fatigue_life.dim() == 2 and fatigue_life.shape[1] == 1:
+                        fatigue_life = fatigue_life.squeeze(-1)
+                    
+                    # Normalize fatigue life if requested
+                    if self.normalize and hasattr(self, 'fatigue_std') and self.fatigue_std > 0:
+                        fatigue_life = (fatigue_life - self.fatigue_mean) / self.fatigue_std
+                    
+                    # Create data sample
+                    sample = {
+                        'group_id': int(group_name.split('_')[1]),  # Extract number from group_X
+                        'cells': cells,                    # (num_cells, 4)
+                        'mesh_pos': mesh_pos,              # (min_nodes, 3)
+                        'node_type': node_type,            # (min_nodes, 1)
+                        'fatigue_life': fatigue_life,      # (min_nodes,)
+                        'num_nodes': min_nodes,
+                        'num_cells': cells.shape[0]
+                    }
+                    
+                    final_data.append(sample)
+                    print(f"  Successfully processed group {group_name}")
+                    
+                except Exception as e:
+                    print(f"Error processing group {group_name}: {e}")
+                    import traceback
+                    traceback.print_exc()
+                    continue
+
+        # Save processed data
+        save_path = self.data_path.replace('.h5', f'_{self.split}.pt')
+        torch.save(final_data, save_path)
+        
+        # Save normalization statistics
+        if self.normalize and hasattr(self, 'fatigue_mean'):
+            stats_path = self.data_path.replace('.h5', '_normalization_stats.pt')
+            torch.save({
+                'fatigue_mean': self.fatigue_mean,
+                'fatigue_std': self.fatigue_std
+            }, stats_path)
+        
+        print(f"Data successfully saved to {save_path}")
+        print(f"Total samples in {self.split}: {len(final_data)}")
+
+    def load_data(self):
+        return torch.load(self.data_path.replace('.h5', f'_{self.split}.pt'))
+    
+    def get_normalization_stats(self):
+        """Get normalization statistics for fatigue life"""
+        if self.normalize:
+            stats_path = self.data_path.replace('.h5', '_normalization_stats.pt')
+            if os.path.exists(stats_path):
+                return torch.load(stats_path)
+        return None
+    
+    def denormalize_fatigue_life(self, normalized_fatigue_life):
+        """Convert normalized fatigue life back to original scale"""
+        stats = self.get_normalization_stats()
+        if stats is not None:
+            return normalized_fatigue_life * stats['fatigue_std'] + stats['fatigue_mean']
+        return normalized_fatigue_life
+
+
+def explore_h5_structure(data_path):
+    """Utility function to explore the structure of your H5 file"""
+    print(f"Exploring structure of {data_path}")
+    
+    with h5py.File(data_path, 'r') as f:
+        print(f"Root keys: {list(f.keys())}")
+        
+        # Check first few groups
+        available_groups = [key for key in f.keys() if key.startswith('group_')]
+        for i in range(min(3, len(available_groups))):
+            group_name = available_groups[i]
+            if group_name in f:
+                group = f[group_name]
+                print(f"\nGroup {group_name}:")
+                print(f"  Keys: {list(group.keys())}")
+                
+                # Check shapes of each dataset
+                for key in group.keys():
+                    try:
+                        shape = group[key].shape
+                        dtype = group[key].dtype
+                        print(f"  {key}: shape={shape}, dtype={dtype}")
+                    except:
+                        print(f"  {key}: Could not read shape/dtype")
+
+
+def main():
+    # Replace with your actual H5 file path
+    data_file = r"D:\AnK\Project Fatigue\Notch shaft\fatigue_net\PressNet\datasets\extracted_data\shaft_.h5"
+    
+    # First, explore the structure to understand the actual group names
+    print("=== EXPLORING H5 FILE STRUCTURE ===")
+    explore_h5_structure(data_file)
+    print("=" * 50)
+    
+    # Now create datasets with correct group names
+    print("Creating training dataset...")
+    train_dataset = ShaftFatigueDataset(data_file, split='train', normalize=True)
+    
+    print("\nCreating validation dataset...")
+    val_dataset = ShaftFatigueDataset(data_file, split='val', normalize=True)
+    
+    print("\nCreating test dataset...")
+    test_dataset = ShaftFatigueDataset(data_file, split='test', normalize=True)
+    
+    # Create dataloaders only if datasets are not empty
+    if len(train_dataset) > 0:
+        train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=2, shuffle=True)
+    if len(val_dataset) > 0:
+        val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=2, shuffle=False)
+    if len(test_dataset) > 0:
+        test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=2, shuffle=False)
+    
+    print(f"\nDataset sizes:")
+    print(f"Train: {len(train_dataset)}")
+    print(f"Val: {len(val_dataset)}")
+    print(f"Test: {len(test_dataset)}")
+    
+    # Examine first batch if training data exists
+    if len(train_dataset) > 0:
+        print("\nFirst training batch:")
+        for i, batch in enumerate(train_loader):
+            print(f"Batch {i+1}:")
+            for key, value in batch.items():
+                if isinstance(value, torch.Tensor):
+                    print(f"  {key}: {value.shape}")
+                else:
+                    print(f"  {key}: {value}")
+            break
+    
+    # Check normalization stats
+    stats = train_dataset.get_normalization_stats()
+    if stats:
+        print(f"\nNormalization stats:")
+        print(f"  Fatigue life mean: {stats['fatigue_mean']:.4f}")
+        print(f"  Fatigue life std: {stats['fatigue_std']:.4f}")
+
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/Fatigue_Life/surrogateAI/utilities/Testdataset.py b/Fatigue_Life/surrogateAI/utilities/Testdataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..55e6959fe030c3069731509f96cbc99cb90dc28e
--- /dev/null
+++ b/Fatigue_Life/surrogateAI/utilities/Testdataset.py
@@ -0,0 +1,310 @@
+import torch
+from torch.utils.data import Dataset
+import h5py
+import os
+import numpy as np
+
+class TrajectoryDataset(Dataset):
+    def __init__(self, data_path, split='train', normalize=True):
+        self.data_path = data_path
+        self.split = split
+        self.normalize = normalize
+        self.is_processed = self._is_processed()
+
+        if not self.is_processed:
+            self.process_data()
+        self.data = self.load_data()
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        return self.data[idx]
+
+    def _is_processed(self):
+        return os.path.exists(self.data_path.replace('.h5', f'_{self.split}.pt'))
+
+    def process_data(self):
+        print("Processing shaft fatigue data...")
+        
+        final_data = []
+        
+        with h5py.File(self.data_path, 'r') as h5_file:
+            # Get all group names (group_0 to group_15)
+            group_names = [f'group_{i}' for i in range(16)]  # Groups group_0 to group_15
+            total_groups = len(group_names)
+            
+            # Split data: 70% train, 15% val, 15% test
+            if self.split == 'train':
+                group_limit = round(total_groups * 0.7)  # 0 to 10 (11 groups)
+                selected_groups = group_names[:group_limit]
+            elif self.split == 'val':
+                group_limit_start = round(total_groups * 0.7)  # 11
+                group_limit_end = round(total_groups * 0.85)   # 13
+                if group_limit_end == group_limit_start:
+                    selected_groups = group_names[group_limit_start:]
+                else:
+                    selected_groups = group_names[group_limit_start:group_limit_end]
+            else:  # test
+                group_limit_start = round(total_groups * 0.85)  # 14
+                if group_limit_start == total_groups:
+                    group_limit_start -= 1
+                selected_groups = group_names[group_limit_start:]
+            
+            print(f"Selected groups for {self.split}: {selected_groups}")
+            
+            # Store normalization statistics for fatigue life
+            all_fatigue_life = []
+            
+            # First pass: collect all fatigue life data for normalization
+            if self.normalize:
+                for group_name in group_names:
+                    if group_name in h5_file:
+                        group = h5_file[group_name]
+                        if 'fatigue_life' in group:
+                            fatigue_life = group['fatigue_life'][:]
+                            all_fatigue_life.append(fatigue_life)
+                
+                if all_fatigue_life:
+                    all_fatigue_life = np.concatenate(all_fatigue_life)
+                    self.fatigue_mean = np.mean(all_fatigue_life)
+                    self.fatigue_std = np.std(all_fatigue_life)
+                    print(f"Fatigue life normalization - Mean: {self.fatigue_mean:.4f}, Std: {self.fatigue_std:.4f}")
+            
+            # Second pass: process selected groups
+            for group_name in selected_groups:
+                if group_name not in h5_file:
+                    print(f"Warning: Group {group_name} not found in file")
+                    continue
+                    
+                print(f"Processing group {group_name}")
+                group = h5_file[group_name]
+                
+                # Extract data from each group
+                try:
+                    # Read datasets
+                    cells = torch.tensor(group['cells'][:], dtype=torch.long)  # Shape: (num_cells, 4)
+                    mesh_pos = torch.tensor(group['mesh_pos'][:], dtype=torch.float32)  # Shape: (num_nodes, 3)
+                    node_type = torch.tensor(group['node_type'][:], dtype=torch.long)  # Shape: (num_nodes,) or (num_nodes, 1)
+                    fatigue_life = torch.tensor(group['fatigue_life'][:], dtype=torch.float32)  # Shape: (num_nodes,)
+                    
+                    # Ensure node_type is 2D
+                    if node_type.dim() == 1:
+                        node_type = node_type.unsqueeze(-1)
+                    
+                    # Normalize fatigue life if requested
+                    if self.normalize and hasattr(self, 'fatigue_std') and self.fatigue_std > 0:
+                        fatigue_life = (fatigue_life - self.fatigue_mean) / self.fatigue_std
+                    
+                    # Create data sample
+                    sample = {
+                        'group_id': int(group_name.split('_')[1]),  # Extract number from group_X
+                        'cells': cells,                    # (num_cells, 4)
+                        'mesh_pos': mesh_pos,              # (num_nodes, 3)
+                        'node_type': node_type,            # (num_nodes, 1)
+                        'fatigue_life': fatigue_life,      # (num_nodes,)
+                        'num_nodes': mesh_pos.shape[0],
+                        'num_cells': cells.shape[0]
+                    }
+                    
+                    final_data.append(sample)
+                    
+                except Exception as e:
+                    print(f"Error processing group {group_name}: {e}")
+                    continue
+
+        # Save processed data
+        save_path = self.data_path.replace('.h5', f'_{self.split}.pt')
+        torch.save(final_data, save_path)
+        
+        # Save normalization statistics
+        if self.normalize and hasattr(self, 'fatigue_mean'):
+            stats_path = self.data_path.replace('.h5', '_normalization_stats.pt')
+            torch.save({
+                'fatigue_mean': self.fatigue_mean,
+                'fatigue_std': self.fatigue_std
+            }, stats_path)
+        
+        print(f"Data successfully saved to {save_path}")
+        print(f"Total samples in {self.split}: {len(final_data)}")
+
+    def load_data(self):
+        return torch.load(self.data_path.replace('.h5', f'_{self.split}.pt'))
+    
+    def get_normalization_stats(self):
+        """Get normalization statistics for fatigue life"""
+        if self.normalize:
+            stats_path = self.data_path.replace('.h5', '_normalization_stats.pt')
+            if os.path.exists(stats_path):
+                return torch.load(stats_path)
+        return None
+    
+    def denormalize_fatigue_life(self, normalized_fatigue_life):
+        """Convert normalized fatigue life back to original scale"""
+        stats = self.get_normalization_stats()
+        if stats is not None:
+            return normalized_fatigue_life * stats['fatigue_std'] + stats['fatigue_mean']
+        return normalized_fatigue_life
+
+
+def explore_h5_structure(data_path):
+    """Utility function to explore the structure of your H5 file"""
+    print(f"Exploring structure of {data_path}")
+    
+    with h5py.File(data_path, 'r') as f:
+        print(f"Root keys: {list(f.keys())}")
+        
+        # Check first few groups
+        available_groups = [key for key in f.keys() if key.startswith('group_')]
+        for i in range(min(3, len(available_groups))):
+            group_name = available_groups[i]
+            if group_name in f:
+                group = f[group_name]
+                print(f"\nGroup {group_name}:")
+                print(f"  Keys: {list(group.keys())}")
+                
+                # Check shapes of each dataset
+                for key in group.keys():
+                    try:
+                        shape = group[key].shape
+                        dtype = group[key].dtype
+                        print(f"  {key}: shape={shape}, dtype={dtype}")
+                    except:
+                        print(f"  {key}: Could not read shape/dtype")
+
+
+def main():
+    data_file = r"D:\AnK\Project Fatigue\Notch shaft\fatigue_net\PressNet\datasets\extracted_data\shaft_.h5"
+    
+    # First, explore the structure to understand the actual group names
+    print("=== EXPLORING H5 FILE STRUCTURE ===")
+    explore_h5_structure(data_file)
+    print("=" * 50)
+    
+    # Now create datasets with correct group names
+    print("Creating training dataset...")
+    train_dataset = TrajectoryDataset(data_file, split='train', normalize=True)
+    
+    print("\nCreating validation dataset...")
+    val_dataset = TrajectoryDataset(data_file, split='val', normalize=True)
+    
+    print("\nCreating test dataset...")
+    test_dataset = TrajectoryDataset(data_file, split='test', normalize=True)
+    
+    # Create dataloaders only if datasets are not empty
+    if len(train_dataset) > 0:
+        train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=2, shuffle=True)
+    if len(val_dataset) > 0:
+        val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=2, shuffle=False)
+    if len(test_dataset) > 0:
+        test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=2, shuffle=False)
+    
+    print(f"\nDataset sizes:")
+    print(f"Train: {len(train_dataset)}")
+    print(f"Val: {len(val_dataset)}")
+    print(f"Test: {len(test_dataset)}")
+    
+    # Examine first batch if training data exists
+    if len(train_dataset) > 0:
+        print("\nFirst training batch:")
+        for i, batch in enumerate(train_loader):
+            print(f"Batch {i+1}:")
+            for key, value in batch.items():
+                if isinstance(value, torch.Tensor):
+                    print(f"  {key}: {value.shape}")
+                else:
+                    print(f"  {key}: {value}")
+            break
+    
+    # Check normalization stats
+    stats = train_dataset.get_normalization_stats()
+    if stats:
+        print(f"\nNormalization stats:")
+        print(f"  Fatigue life mean: {stats['fatigue_mean']:.4f}")
+        print(f"  Fatigue life std: {stats['fatigue_std']:.4f}")
+
+
+if __name__ == '__main__':
+    main()
+    
+    """The .pt file is saved automatically inside the TrajectoryDataset class — in the constructor 
+    (__init__) — via the call to self.process_data() if the data hasn't been processed yet.
+    You can load it later using the load_data() method.
+    This will load the processed data from the .pt file, which contains all the necessary tensors.
+    def __init__(self, data_path, split='train', normalize=True):
+    self.data_path = data_path
+    self.split = split
+    self.normalize = normalize
+    self.is_processed = self._is_processed()
+
+    if not self.is_processed:
+        self.process_data()     # <- THIS saves the .pt file
+    self.data = self.load_data()
+
+
+So when you do:
+
+train_dataset = TrajectoryDataset(data_file, split='train', normalize=True)
+Here’s what happens step-by-step:
+
+_is_processed() checks whether shaft__train.pt exists.
+
+If it doesn't, process_data() is called:
+
+It loads the HDF5 groups.
+
+Prepares the data.
+
+Saves it to shaft__train.pt via:
+
+torch.save(final_data, save_path)
+The saved .pt file is then loaded by self.load_data().
+
+Output Files Created:
+If your original file is named:
+
+plaintext
+shaft_.h5
+
+
+-------------------------------------------------------------------------------------------------------------------------------
+------------------------------------------------------------------------------------------------------------------------------
+def load_data(self):
+    return torch.load(self.data_path.replace('.h5', f'_{self.split}.pt'))
+    This method loads the processed data from the .pt file created during processing.
+    💡 What It Does:
+This method loads the preprocessed .pt file (a PyTorch object file) for the given split (train, val, or test) 
+and returns it as a Python object 
+
+
+— specifically, a list of dictionaries (your processed data samples).
+What is in shaft__train.pt?
+In your process_data() method, you saved a list of samples like this:
+
+sample = {
+    'group_id': int(group_name.split('_')[1]),
+    'cells': cells,                # Tensor of shape (num_cells, 4)
+    'mesh_pos': mesh_pos,          # Tensor of shape (num_nodes, 3)
+    'node_type': node_type,        # Tensor of shape (num_nodes, 1)
+    'fatigue_life': fatigue_life,  # Tensor of shape (num_nodes,)
+    'num_nodes': mesh_pos.shape[0],
+    'num_cells': cells.shape[0]
+}
+
+And then saved all of them in a list:
+final_data.append(sample)
+torch.save(final_data, save_path)
+So load_data() simply loads this list of dictionaries back into memory.
+
+After loading:
+
+self.data = load_data()
+You now have:
+self.data = [
+    { 'group_id': ..., 'cells': ..., 'mesh_pos': ..., ... },
+    { 'group_id': ..., 'cells': ..., 'mesh_pos': ..., ... },
+    ...
+]
+
+
+"""
\ No newline at end of file
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diff --git a/Fatigue_Life/surrogateAI/utilities/common.py b/Fatigue_Life/surrogateAI/utilities/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..63c598c016ed347d0b9a45514b854555a81b6ed1
--- /dev/null
+++ b/Fatigue_Life/surrogateAI/utilities/common.py
@@ -0,0 +1,75 @@
+# Lint as: python3
+# pylint: disable=g-bad-file-header
+# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Commonly used data structures and functions."""
+
+import enum
+# import tensorflow.compat.v1 as tf
+import torch
+
+
+class NodeType(enum.IntEnum):
+    NORMAL = 0
+    OBSTACLE = 1
+    ROLLER = 2
+    SIZE = 3
+    
+class ThermalNodeType(enum.IntEnum):
+    NON_CONVECTION = 0
+    CONVECTION = 1
+    SIZE = 2
+
+
+def triangles_to_edges(faces, deform=False):
+    """Computes mesh edges from triangles."""
+    if not deform:
+        # collect edges from triangles
+        edges = torch.cat((faces[:, 0:2],
+                           faces[:, 1:3],
+                           torch.stack((faces[:, 2], faces[:, 0]), dim=1)), dim=0)
+        # those edges are sometimes duplicated (within the mesh) and sometimes
+        # single (at the mesh boundary).
+        # sort & pack edges as single tf.int64
+        receivers, _ = torch.min(edges, dim=1)
+        senders, _ = torch.max(edges, dim=1)
+
+        packed_edges = torch.stack((senders, receivers), dim=1)
+        unique_edges = torch.unique(packed_edges, return_inverse=False, return_counts=False, dim=0)
+        senders, receivers = torch.unbind(unique_edges, dim=1)
+        senders = senders.to(torch.int64)
+        receivers = receivers.to(torch.int64)
+
+        two_way_connectivity = (torch.cat((senders, receivers), dim=0), torch.cat((receivers, senders), dim=0))
+        return {'two_way_connectivity': two_way_connectivity, 'senders': senders, 'receivers': receivers}
+    else:
+        edges = torch.cat((faces[:, 0:2],
+                           faces[:, 1:3],
+                           faces[:, 2:4],
+                           torch.stack((faces[:, 3], faces[:, 0]), dim=1)), dim=0)
+        # those edges are sometimes duplicated (within the mesh) and sometimes
+        # single (at the mesh boundary).
+        # sort & pack edges as single tf.int64
+        receivers, _ = torch.min(edges, dim=1)
+        senders, _ = torch.max(edges, dim=1)
+
+        packed_edges = torch.stack((senders, receivers), dim=1)
+        unique_edges = torch.unique(packed_edges, return_inverse=False, return_counts=False, dim=0)
+        senders, receivers = torch.unbind(unique_edges, dim=1)
+        senders = senders.to(torch.int64)
+        receivers = receivers.to(torch.int64)
+
+        two_way_connectivity = (torch.cat((senders, receivers), dim=0), torch.cat((receivers, senders), dim=0))
+        return {'two_way_connectivity': two_way_connectivity, 'senders': senders, 'receivers': receivers}
\ No newline at end of file
diff --git a/Fatigue_Life/surrogateAI/utilities/dataset.py b/Fatigue_Life/surrogateAI/utilities/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..25dfd3ff233bbae894682117cda38642979f60b5
--- /dev/null
+++ b/Fatigue_Life/surrogateAI/utilities/dataset.py
@@ -0,0 +1,243 @@
+import torch
+from torch.utils.data import Dataset
+import h5py
+import os
+import numpy as np
+
+class TrajectoryDataset(Dataset):
+    def __init__(self, data_path, split='train', normalize=True):
+        self.data_path = data_path
+        self.split = split
+        self.normalize = normalize
+        self.is_processed = self._is_processed()
+
+        if not self.is_processed:
+            self.process_data()
+        self.data = self.load_data()
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        return self.data[idx]
+
+    def _is_processed(self):
+        return os.path.exists(self.data_path.replace('.h5', f'_{self.split}.pt'))
+
+    def process_data(self):
+        print("Processing shaft fatigue data...")
+        
+        final_data = []
+        
+        with h5py.File(self.data_path, 'r') as h5_file:
+            # Get all group names (0 to 15)
+            group_names = sorted(list(h5_file.keys())) # Groups 0 to 15
+            total_groups = len(group_names)
+            
+            # Split data: 70% train, 15% val, 15% test
+            if self.split == 'train':
+                group_limit = round(total_groups * 0.7)  # 0 to 10 (11 groups)
+                selected_groups = group_names[:group_limit]
+            elif self.split == 'val':
+                group_limit_start = round(total_groups * 0.7)  # 11
+                group_limit_end = round(total_groups * 0.85)   # 13
+                if group_limit_end == group_limit_start:
+                    selected_groups = group_names[group_limit_start:]
+                else:
+                    selected_groups = group_names[group_limit_start:group_limit_end]
+            else:  # test
+                group_limit_start = round(total_groups * 0.85)  # 14
+                if group_limit_start == total_groups:
+                    group_limit_start -= 1
+                selected_groups = group_names[group_limit_start:]
+            
+            print(f"Selected groups for {self.split}: {selected_groups}")
+            
+            """ 
+            # Store normalization statistics for fatigue life
+            all_fatigue_life = []
+            
+            # First pass: collect all fatigue life data for normalization
+            if self.normalize:
+                for group_name in group_names:
+                    if group_name in h5_file:
+                        group = h5_file[group_name]
+                        if 'fatigue_life' in group:
+                            fatigue_life = group['fatigue_life'][:]
+                            all_fatigue_life.append(fatigue_life)
+                
+                if all_fatigue_life:
+                    all_fatigue_life = np.concatenate(all_fatigue_life)
+                    self.fatigue_mean = np.mean(all_fatigue_life)
+                    self.fatigue_std = np.std(all_fatigue_life)
+                    print(f"Fatigue life normalization - Mean: {self.fatigue_mean:.4f}, Std: {self.fatigue_std:.4f}")
+            """
+            
+            # Second pass: process selected groups
+            for group_name in selected_groups:
+                if group_name not in h5_file:
+                    print(f"Warning: Group {group_name} not found in file")
+                    continue
+                    
+                print(f"Processing group {group_name}")
+                group = h5_file[group_name]
+                
+                # Extract data from each group
+                try:
+                    # Read datasets
+                    cells = torch.tensor(group['cells'][:], dtype=torch.long)  # Shape: (num_cells, 4)
+                    mesh_pos = torch.tensor(group['mesh_pos'][:], dtype=torch.float32)  # Sha((((pe: (num_nodes, 3)
+                    node_type = torch.tensor(group['in_out_node_type'][:], dtype=torch.long)  # Shape: (num_nodes,) or (num_nodes, 1)                    
+                    radial_distance = torch.tensor(group['radial_distance'][:], dtype=torch.float32)  # Shape: (num_nodes,)
+                    r_norm = torch.tensor(group['r_norm'][:], dtype=torch.float32)  # Shape: (num_nodes,)
+                    z_norm = torch.tensor(group['z_norm'][:], dtype=torch.float32)  # Shape: (num_nodes,)
+                    d_step = torch.tensor(group['d_step'][:], dtype=torch.float32)  # Shape: (num_nodes,)
+                    d_step_norm = torch.tensor(group['d_step_norm'][:], dtype=torch.float32)  # Shape: (num_nodes,)
+                    # Ensure node_type is 2D
+                    if node_type.dim() == 1:
+                        node_type = node_type.unsqueeze(-1)
+                        
+                    fatigue_life = torch.tensor(group['fatigue_life'][:], dtype=torch.float32)  # Shape: (num_nodes,)
+                    fatigue_life = torch.log10(fatigue_life.clamp(min=1e-3))  # Clamp to avoid log(0)
+                    
+                    '''
+                    # Convert to scientific notation components
+                    b = torch.floor(torch.log10(fatigue_life))  # exponent
+                    a = fatigue_life / (10 ** b)                # coefficient
+                    target_ab = torch.stack([a, b], dim=1)     # shape [N, 2]
+                    print(mesh_pos)
+                    print(target_ab)
+                    '''
+                    # Normalize fatigue life if requested
+                    #if self.normalize and hasattr(self, 'fatigue_std') and self.fatigue_std > 0:
+                    #    fatigue_life = (fatigue_life - self.fatigue_mean) / self.fatigue_std
+                    
+                    # Create data sample
+                    sample = {
+                        'group_id': group_name,
+                        'cells': cells,                    # (num_cells, 4)
+                        'mesh_pos': mesh_pos,              # (num_nodes, 3)
+                        'node_type': node_type,            # (num_nodes, 1)
+                        'fatigue_life': fatigue_life,      # (num_nodes,)
+                        'num_nodes': mesh_pos.shape[0],
+                        'num_cells': cells.shape[0],
+                        'radial_distance': radial_distance,  # (num_nodes,)
+                        'r_norm': r_norm,                    # (num_nodes,)
+                        'z_norm': z_norm,                    # (num_nodes,)
+                        'd_step': d_step,                    # (num_nodes,)
+                        'd_step_norm': d_step_norm           # (num_nodes,)
+                    }
+                    
+                    final_data.append(sample)
+                    
+                except Exception as e:
+                    print(f"Error processing group {group_name}: {e}")
+                    continue
+
+        # Save processed data
+        save_path = self.data_path.replace('.h5', f'_{self.split}.pt')
+        torch.save(final_data, save_path)
+        
+        # Save normalization statistics
+        if self.normalize and hasattr(self, 'fatigue_mean'):
+            stats_path = self.data_path.replace('.h5', '_normalization_stats.pt')
+            torch.save({
+                'fatigue_mean': self.fatigue_mean,
+                'fatigue_std': self.fatigue_std
+            }, stats_path)
+        
+        print(f"Data successfully saved to {save_path}")
+        print(f"Total samples in {self.split}: {len(final_data)}")
+
+    def load_data(self):
+        return torch.load(self.data_path.replace('.h5', f'_{self.split}.pt'))
+    
+    def get_normalization_stats(self):
+        """Get normalization statistics for fatigue life"""
+        if self.normalize:
+            stats_path = self.data_path.replace('.h5', '_normalization_stats.pt')
+            if os.path.exists(stats_path):
+                return torch.load(stats_path)
+        return None
+    
+    def denormalize_fatigue_life(self, normalized_fatigue_life):
+        """Convert normalized fatigue life back to original scale"""
+        stats = self.get_normalization_stats()
+        if stats is not None:
+            return normalized_fatigue_life * stats['fatigue_std'] + stats['fatigue_mean']
+        return normalized_fatigue_life
+
+
+def explore_h5_structure(data_path):
+    """Utility function to explore the structure of your H5 file"""
+    print(f"Exploring structure of {data_path}")
+    
+    with h5py.File(data_path, 'r') as f:
+        print(f"Root keys: {list(f.keys())}")
+        
+        # Check first few groups
+        for i in range(min(3, len(f.keys()))):
+            group_name = str(i)
+            if group_name in f:
+                group = f[group_name]
+                print(f"\nGroup {group_name}:")
+                print(f"  Keys: {list(group.keys())}")
+                
+                # Check shapes of each dataset
+                for key in group.keys():
+                    try:
+                        shape = group[key].shape
+                        dtype = group[key].dtype
+                        print(f"  {key}: shape={shape}, dtype={dtype}")
+                    except:
+                        print(f"  {key}: Could not read shape/dtype")
+
+
+def main():
+    # Replace with your actual H5 file path
+    data_file = "/home/gd_user1/AnK/project_PINN/Project_Fatigue/Fatigue_Life/datasets/extracted_data/shaft_low_extra2_.h5"
+    
+    # First, explore the structure (optional)
+    # explore_h5_structure(data_file)
+    
+    # Create datasets
+    print("Creating training dataset...")
+    train_dataset = TrajectoryDataset(data_file, split='train', normalize=True)
+    
+    print("\nCreating validation dataset...")
+    val_dataset = TrajectoryDataset(data_file, split='val', normalize=True)
+    
+    print("\nCreating test dataset...")
+    test_dataset = TrajectoryDataset(data_file, split='test', normalize=True)
+    
+    # Create dataloaders
+    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=1, shuffle=True)
+    val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=False)
+    test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False)
+    
+    print(f"\nDataset sizes:")
+    print(f"Train: {len(train_dataset)}")
+    print(f"Val: {len(val_dataset)}")
+    print(f"Test: {len(test_dataset)}")
+    
+    # Examine first batch
+    print("\nFirst training batch:")
+    for i, batch in enumerate(train_loader):
+        print(f"Batch {i+1}:")
+        for key, value in batch.items():
+            if isinstance(value, torch.Tensor):
+                print(f"  {key}: {value.shape}")
+            else:
+                print(f"  {key}: {value}")
+        break
+    
+    # Check normalization stats
+    stats = train_dataset.get_normalization_stats()
+    if stats:
+        print(f"\nNormalization stats:")
+        print(f"  Fatigue life mean: {stats['fatigue_mean']:.4f}")
+        print(f"  Fatigue life std: {stats['fatigue_std']:.4f}")
+
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/Fatigue_Life/surrogateAI/utilities/plot_eval.py b/Fatigue_Life/surrogateAI/utilities/plot_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..1efeb345b213057139676c100e6bc956a14ae316
--- /dev/null
+++ b/Fatigue_Life/surrogateAI/utilities/plot_eval.py
@@ -0,0 +1,42 @@
+import os
+import argparse
+import pandas as pd
+import matplotlib.pyplot as plt
+
+def plot_evaluation_loss(exp_dir, epoch):
+   
+    # Build paths
+    log_dir = os.path.join(exp_dir, 'log')
+    eval_pkl = os.path.join(log_dir, f'eval_loss_epoch_{epoch}.pkl')
+    if not os.path.isfile(eval_pkl):
+        raise FileNotFoundError(f"Evaluation pickle not found: {eval_pkl}")
+
+    # Load data
+    df = pd.read_pickle(eval_pkl)
+    eval_losses = df['epoch_eval_losses']
+    epochs = list(range(1, len(eval_losses) * 20, 20))
+
+    # Plot
+    plt.figure(figsize=(10, 5))
+    plt.plot(epochs, eval_losses, marker='o', linestyle='-', linewidth=1.5, markersize=5)
+    plt.xlabel('Epoch')
+    plt.ylabel('Evaluation Loss')
+    plt.title(f'Evaluation Loss (epoch {epoch})')
+    plt.grid(True)
+
+    # Save
+    save_dir = os.path.join(exp_dir, 'plots')
+    os.makedirs(save_dir, exist_ok=True)
+    out_path = os.path.join(save_dir, f'eval_loss_epoch_{epoch}.png')
+    plt.tight_layout()
+    plt.savefig(out_path)
+    plt.close()
+    print(f"Saved evaluation plot to {out_path}")
+
+def main():
+    exp_dir = '/home/bipinshrestha228/GlassForming/new_data_12/original/k_120/Fri-Jun-27-18-00-56-2025'
+    epoch = 460
+    plot_evaluation_loss(exp_dir, epoch)
+    
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/Fatigue_Life/surrogateAI/utilities/plot_lr.py b/Fatigue_Life/surrogateAI/utilities/plot_lr.py
new file mode 100644
index 0000000000000000000000000000000000000000..8dcef591dce9ffc19fb3998c9a109e55a03c360d
--- /dev/null
+++ b/Fatigue_Life/surrogateAI/utilities/plot_lr.py
@@ -0,0 +1,66 @@
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+def plot_training_loss(exp_dir, epoch):
+
+    # Build paths
+    log_dir = os.path.join(exp_dir, 'log')
+    train_pkl = os.path.join(log_dir, f'temp_train_loss_{epoch}.pkl')
+    if not os.path.isfile(train_pkl):
+        raise FileNotFoundError(f"Training pickle not found: {train_pkl}")
+
+    # Load data
+    df = pd.read_pickle(train_pkl)
+
+    epoch_losses = [float(x) for x in df['train_epoch_losses']]
+    # epoch_losses_temp = [float(x) for x in df['train_epoch_losses_temp']]
+    # epoch_losses_pos = [float(x) for x in df['train_epoch_losses_pos']]
+    # epoch_losses_von = [float(x) for x in df['train_epoch_losses_von']]
+    lrs = df['learning_rate']
+    epochs = list(range(1, len(epoch_losses) + 1))
+    print("min training loss", df['train_min_epoch_loss'])
+
+    # Plot
+    fig, ax1 = plt.subplots(figsize=(10,5))
+    ax1.plot(epochs, epoch_losses, marker='o', linestyle='-', label='Total Train Loss', color = 'blue')
+    # ax1.plot(epochs, epoch_losses_temp, marker='o', linestyle='-', label='Temp Train Loss', color = 'yellow')
+    # ax1.plot(epochs, epoch_losses_pos, marker='o', linestyle='-', label='Pos Train Loss', color = 'green')
+    # ax1.plot(epochs, epoch_losses_von, marker='o', linestyle='-', label='Von Train Loss', color = 'pink')    
+    ax1.set_xlabel('Epoch')
+    ax1.set_ylabel('Loss', color='blue')
+    ax1.tick_params(axis='y', labelcolor='blue')
+
+    ax2 = ax1.twinx()
+    ax2.plot(epochs, lrs, linestyle='--', label='Learning Rate', color='red')
+    ax2.set_ylabel('Learning Rate', color='red')
+    ax2.tick_params(axis='y', labelcolor='red')
+
+    # Combine legends
+    lines, labels = ax1.get_legend_handles_labels()
+    lines2, labels2 = ax2.get_legend_handles_labels()
+    ax1.legend(lines + lines2, labels + labels2, loc='upper right')
+
+    ax1.set_title(f'Training Loss & Learning Rate (epoch {epoch})')
+    ax1.grid(True)
+
+    # Save
+    save_dir = os.path.join(exp_dir, 'plots_lr')
+    os.makedirs(save_dir, exist_ok=True)
+    out_path = os.path.join(save_dir, f'training_loss_epoch_{epoch}.png')
+    plt.tight_layout()
+    plt.savefig(out_path)
+    plt.close(fig)
+    print(f"Saved training plot to {out_path}")
+
+
+
+
+def main():
+    exp_dir = '/home/rachit/GlassForming/t_dest_output/regDGCNN_seg/train/EXPERIMENT_2/Mon-Jul-14-10-01-31-2025/'
+    epoch = 40
+    plot_training_loss(exp_dir, epoch)
+    
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/Fatigue_Life/surrogateAI/utilities/rollout_vis.py b/Fatigue_Life/surrogateAI/utilities/rollout_vis.py
new file mode 100644
index 0000000000000000000000000000000000000000..27d41ce54135b34ee73bca171960b16c0b2ae247
--- /dev/null
+++ b/Fatigue_Life/surrogateAI/utilities/rollout_vis.py
@@ -0,0 +1,104 @@
+import pickle
+import numpy as np
+import pandas as pd
+import os
+import re
+from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
+
+# --- 1. Define the base directory for all evaluation results ---
+base_results_dir = "Evaluation_results"
+
+# --- 2. Manually set the k-value and epoch number for this run ---
+k_value = 60
+epoch_num = 500
+# --- You can change the two values above for each run ---
+
+
+# Load the trajectory operations dictionary from the .pkl file
+pkl_path = '/home/gd_user1/AnK/project_PINN/Project_Fatigue/Fatigue_Life/output_shaft_extra/regDGCNN_seg/shaft_low_extra2_/EXPERIMENT_multi_feat_exponent_k60_500_shaft_low_extra2/Thu-Oct--9-10-04-58-2025/rollout/rollout_epoch_500.pkl'
+
+# Create a unique, descriptive folder name based on the manual inputs
+run_name = f"k{k_value}_epoch{epoch_num}_exponent_multi_metrics_2"
+run_dir = os.path.join(base_results_dir, run_name)
+os.makedirs(run_dir, exist_ok=True)
+
+print(f"Results for this run will be saved in: {run_dir}")
+
+
+try:
+    with open(pkl_path, 'rb') as f:
+        traj_ops = pickle.load(f)
+except FileNotFoundError:
+    print(f"Error: The file was not found at {pkl_path}")
+    exit()  # Exit the script if the file doesn't exist
+
+total_trajectories = len(traj_ops)
+print(f"Loaded {total_trajectories} trajectory operations from the file.")
+all_metrics=[]
+for i in range(total_trajectories):
+    # Extract tensors (log10 values)
+    gt_life_log = traj_ops[i]['gt_fatigue_life'].cpu().numpy().flatten()
+    pred_life_log = traj_ops[i]['pred_fatigue_life'].cpu().numpy().flatten()
+    
+    cells= traj_ops[i]['cells'].cpu().numpy()
+    mesh_pos= traj_ops[i]['mesh_pos'].cpu().numpy()
+
+    # Save the geometry to a separate, efficient .npz file
+    geometry_path = os.path.join(run_dir, f"geometry_sample_{i+1}.npz")
+    np.savez_compressed(geometry_path, mesh_pos=mesh_pos, cells=cells)
+    print(f"  -> Saved geometry to {geometry_path}")
+
+    # Convert back to non-log values
+    gt_life_real = np.power(10.0, gt_life_log)
+    pred_life_real = np.power(10.0, pred_life_log)
+
+    # --- CALCULATE ERRORS ---
+    abs_error = np.abs(gt_life_real - pred_life_real)
+    percentage_error = (abs_error / (gt_life_real + 1e-9)) * 100
+
+    # Create a DataFrame for easy analysis and saving
+    df = pd.DataFrame({
+        'node': np.arange(len(gt_life_log)),
+
+        # Log values
+        'gt_fatigue_life_log10': gt_life_log,
+        'pred_fatigue_life_log10': pred_life_log,
+
+        # Real (non-log) values
+        'gt_fatigue_life_real': gt_life_real,
+        'pred_fatigue_life_real': pred_life_real,
+
+        # Errors in real values
+        'absolute_error': abs_error,
+        'percentage_error': percentage_error
+    })
+    
+    # Metrics on real (non-log) fatigue life
+    mse = mean_squared_error(gt_life_real, pred_life_real)
+    rmse = np.sqrt(mse)
+    mae = mean_absolute_error(gt_life_real, pred_life_real)
+    mape = np.mean(percentage_error)  # Mean of percentage error
+    r2 = r2_score(gt_life_real, pred_life_real)
+
+    metrics_dict = {
+        "test_sample": i+1,
+        "MSE": mse,
+        "RMSE": rmse,
+        "MAE": mae,
+        "MAPE": mape,
+        "R2": r2
+    }
+    all_metrics.append(metrics_dict)
+
+    # Save each trajectory to a CSV
+    csv_filename = f"prediction_sample{i+1}.csv"
+    csv_path = os.path.join(run_dir, csv_filename)
+
+    df.to_csv(csv_path, index=False)
+
+    print(f"Saved fatigue life evaluation to {csv_path}")
+    
+metrics_df = pd.DataFrame(all_metrics)
+metrics_csv_path = os.path.join(run_dir, "evaluation_summary.csv")
+metrics_df.to_csv(metrics_csv_path, index=False)
+print(f"Saved summary metrics to {metrics_csv_path}")
diff --git a/Fatigue_Life/surrogateAI/utilities/run_vis.py b/Fatigue_Life/surrogateAI/utilities/run_vis.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d6146a6efb4ef9e65f1db8c5a033f307be330cf
--- /dev/null
+++ b/Fatigue_Life/surrogateAI/utilities/run_vis.py
@@ -0,0 +1,18 @@
+#!/usr/bin/env python
+import os
+# Import existing plotting and visualization utilities
+from plot_lr import plot_training_loss
+from plot_eval import plot_evaluation_loss
+
+def main():
+    exp_dir = '/home/gd_user1/AnK/project_PINN/Project_Fatigue/Fatigue_Life/output_shaft_extra/regDGCNN_seg/shaft_low_extra2_/EXPERIMENT_multi_feat_exponent_k60_500_shaft_low_extra2/Thu-Oct--9-10-57-48-2025'
+    epoch = 500
+ 
+    # Plot training loss & learning rate
+    plot_training_loss(exp_dir, epoch)
+
+    # Plot evaluation loss
+    plot_evaluation_loss(exp_dir, epoch)
+
+if __name__ == '__main__':
+    main()