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Compressible flow over plane/config.yaml

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+#data
+dataset_name: plane_transonic
+test_name: "baseline_ep1000_v2_100"
+gpu_id: 0
+epochs: 300
+data_dir: /raid/ansysai/udbhav/alpha_Xdata/data_prep_transformer/${dataset_name}_v2/1_VTK_surface/
+
+json_file: ${data_dir}/params.json
+
+splits_file: ${data_dir}/
+data_folder: ${dataset_name}
+
+input_normalization: "none" #"shift_axis" # options: "min_max", "std_norm", "none"
+normalization: "std_norm"   
+norm_vars: "pressure"
+physical_scale_for_test: True
+diff_input_velocity: False # If true, inlet_x_velocity is added as an input feature
+
+
+# num_points: 40000
+num_points: 30000 #30000
+num_workers: 1
+
+#model
+indim: 3
+outdim: 1
+model: ansysLPFMs
+hidden_dim: 256
+n_heads: 8
+n_decoder: 8
+mlp_ratio: 2
+
+
+#training
+val_iter: 1
+lr: 0.001
+batch_size: 1
+
+optimizer: 
+    type: AdamW
+scheduler: OneCycleLR #OneCycleLR
+loss_type: huber  # options: mse, mae, huber
+
+# scheduler: LinearWarmupCosineAnnealingLR
+num_processes: 1
+max_grad_norm: 1.0
+mixed_precision: True #currently default fp16 is selected by torch.autocast(). Fp16 gave the best results for Transformer based models.
+eval: False
+
+
+chunked_eval: True # Default with True is evaluation of max chunks of size num_points that can fit in a data sample, to avoid small last chunks 
+train_ckpt_load: False ## Will load best model if ckpt_load is false
+
+#logging
+# test_name: "Final_surface_only_OCLR_3p9M_float32_A100"
+
+pos_embed_sincos: False
+use_rope : False
+use_xavier_init: False  # Set to true for Xavier uniform initialization, false for truncated normal
+
+# sigma for confidence score
+sigma: 0.05
+
+project_name: ${dataset_name}

Compressible flow over plane/full_transform_params.json

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+{
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+  "boundary_conditions_max": {
+    "fluid_density": 1.184,
+    "inlet_velocity": 30.0,
+    "viscosity_dynamic": 1.847e-05
+  }
+}

Compressible flow over plane/latent_features.npy

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Compressible flow over plane/pca_embedding.npy

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Compressible flow over plane/pca_reducer.pkl

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Compressible flow over plane/pca_scaler.pkl

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Compressible flow over plane/train_dist.npz

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Incompressible flow inside artery/config.yaml

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+dataset_name: artery
+test_name: baseline_w_sincos_v1
+gpu_id: 0
+data_dir: /raid/ansysai/udbhav/alpha_Xdata/xgm_data/data_prep_transformer/0_Incompressible_internal_flow/artery/1_VTK_surface
+json_file: ${data_dir}/../full_transform_params.json
+splits_file: ${data_dir}/../
+data_folder: ${dataset_name}
+
+normalization: "std_norm"
+norm_vars: "pressure"
+physical_scale_for_test: True
+diff_input_velocity: false
+
+# num_points: 40000
+num_points: 10000 #15600
+num_workers: 1
+indim: 3
+outdim: 4
+model: ansysLPFMs
+hidden_dim: 256
+n_heads: 8
+n_decoder: 8
+mlp_ratio: 2
+val_iter: 1
+lr: 0.001
+batch_size: 1
+epochs: 300
+optimizer:
+  type: AdamW
+scheduler: OneCycleLR
+loss_type: huber
+num_processes: 1
+max_grad_norm: 1.0
+mixed_precision: true
+eval: false
+chunked_eval: true
+train_ckpt_load: false
+pos_embed_sincos: true
+use_rope : False
+use_xavier_init: False  # Set to true for Xavier uniform initialization, false for truncated normal
+project_name: ${dataset_name}

Incompressible flow inside artery/full_transform_params.json

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+{
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+}

Incompressible flow inside artery/train_dist.npz

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Incompressible flow over car/config.yaml

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+#data
+dataset_name: cadillac
+test_name: "baseline-variable-velocity-axis_v2_long_test"
+gpu_id: 0
+epochs: 1000
+data_dir: Data/${dataset_name}/1_VTK_surface/
+
+json_file: ${data_dir}/params.json
+
+splits_file: ${data_dir}/
+data_folder: ${dataset_name}
+input_normalization: shift_axis
+normalization: "std_norm"
+norm_vars: "pressure"
+physical_scale_for_test: True
+
+diff_input_velocity: True # If true, inlet_x_velocity is added as an input feature
+
+# num_points: 40000
+num_points: 30000 #30000 #30000
+num_workers: 1
+
+#model
+indim: 4
+outdim: 1
+model: ansysLPFMs
+hidden_dim: 256
+n_heads: 8
+n_decoder: 8
+mlp_ratio: 2
+
+
+#training
+val_iter: 1
+lr: 0.001
+batch_size: 1
+
+optimizer: 
+    type: AdamW
+scheduler: OneCycleLR #OneCycleLR
+loss_type: huber  # options: mse, mae, huber
+
+# scheduler: LinearWarmupCosineAnnealingLR
+num_processes: 1
+max_grad_norm: 1.0
+mixed_precision: True #currently default fp16 is selected by torch.autocast(). Fp16 gave the best results for Transformer based models.
+eval: False
+
+
+chunked_eval: True # Default with True is evaluation of max chunks of size num_points that can fit in a data sample, to avoid small last chunks 
+train_ckpt_load: False ## Will load best model if ckpt_load is false
+
+#logging
+# test_name: "Final_surface_only_OCLR_3p9M_float32_A100"
+
+pos_embed_sincos: False
+use_rope : False
+use_xavier_init: False  # Set to true for Xavier uniform initialization, false for truncated normal
+# sigma for confidence score
+sigma: 3
+
+project_name: ${dataset_name}

Incompressible flow over car/full_transform_params.json

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+  "mean_num_points": 662953.6781609196,
+  "min_num_points": 469670
+}

Incompressible flow over car/latent_features.npy

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Incompressible flow over car/pca_embedding.npy

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Incompressible flow over car/pca_reducer.pkl

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Incompressible flow over car/pca_scaler.pkl

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Incompressible flow over car/train_dist.npz

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Structural analysis of bracket/config.yaml

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+#data
+dataset_name: deepjeb
+test_name: "baseline"
+gpu_id: 0
+epochs: 300
+data_dir:  /raid/ansysai/udbhav/alpha_Xdata/xgm_data/data_prep_transformer/2_Structural_linear_static/deepjeb/1_VTK_surface/
+
+json_file: ${data_dir}/../full_transform_params.json
+
+splits_file: ${data_dir}/../
+data_folder: ${dataset_name}
+
+normalization: "std_norm"
+norm_vars: "von_mises_stress"
+physical_scale_for_test: True
+
+
+# num_points: 40000
+num_points: 15000
+num_workers: 1
+
+#model
+indim: 3
+outdim: 4
+model: ansysLPFMs
+hidden_dim: 256
+n_heads: 8
+n_decoder: 8
+mlp_ratio: 2
+
+
+#training
+val_iter: 1
+lr: 0.001
+batch_size: 1
+
+optimizer: 
+    type: AdamW
+scheduler: OneCycleLR #OneCycleLR
+loss_type: huber  # options: mse, mae, huber
+
+# scheduler: LinearWarmupCosineAnnealingLR
+num_processes: 1
+max_grad_norm: 1.0
+mixed_precision: True #currently default fp16 is selected by torch.autocast(). Fp16 gave the best results for Transformer based models.
+eval: False
+
+
+chunked_eval: True # Default with True is evaluation of max chunks of size num_points that can fit in a data sample, to avoid small last chunks 
+train_ckpt_load: False ## Will load best model if ckpt_load is false
+
+#logging
+# test_name: "Final_surface_only_OCLR_3p9M_float32_A100"
+
+pos_embed_sincos: True
+
+
+project_name: ${dataset_name}

Structural analysis of bracket/full_transform_params.json

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Structural analysis of bracket/train_dist.npz

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Vehicle crash analysis/config.yaml

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+#data
+dataset_name: deepjeb
+test_name: "baseline"
+gpu_id: 0
+epochs: 300
+data_dir:  /raid/ansysai/udbhav/alpha_Xdata/xgm_data/data_prep_transformer/2_Structural_linear_static/deepjeb/1_VTK_surface/
+
+json_file: ${data_dir}/../full_transform_params.json
+
+splits_file: ${data_dir}/../
+data_folder: ${dataset_name}
+
+normalization: "std_norm"
+norm_vars: "von_mises_stress"
+physical_scale_for_test: True
+
+
+# num_points: 40000
+num_points: 2000 #15000
+num_workers: 1
+
+#model
+indim: 3
+outdim: 4
+model: ansysLPFMs
+hidden_dim: 256
+n_heads: 8
+n_decoder: 8
+mlp_ratio: 2
+
+
+#training
+val_iter: 1
+lr: 0.001
+batch_size: 1
+
+optimizer: 
+    type: AdamW
+scheduler: OneCycleLR #OneCycleLR
+loss_type: huber  # options: mse, mae, huber
+
+# scheduler: LinearWarmupCosineAnnealingLR
+num_processes: 1
+max_grad_norm: 1.0
+mixed_precision: True #currently default fp16 is selected by torch.autocast(). Fp16 gave the best results for Transformer based models.
+eval: False
+
+
+chunked_eval: True # Default with True is evaluation of max chunks of size num_points that can fit in a data sample, to avoid small last chunks 
+train_ckpt_load: False ## Will load best model if ckpt_load is false
+
+#logging
+# test_name: "Final_surface_only_OCLR_3p9M_float32_A100"
+
+pos_embed_sincos: True
+
+
+project_name: ${dataset_name}

Vehicle crash analysis/full_transform_params.json

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Vehicle crash analysis/train_dist.npz

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