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configs/__init__.py

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+import json
+import os
+
+
+def load_config(config_name='default'):
+    config_dir = os.path.dirname(os.path.abspath(__file__))
+    config_path = os.path.join(config_dir, f'{config_name}.json')
+
+    with open(config_path, 'r') as f:
+        config = json.load(f)
+
+    return config
+
+
+def get_model_config(config_name='default'):
+    config = load_config(config_name)
+    return config.get('model', {})
+
+
+def get_training_config(config_name='default'):
+    config = load_config(config_name)
+    return config.get('training', {})
+
+
+def get_loss_weights(config_name='default'):
+    config = load_config(config_name)
+    return config.get('loss_weights', {})

configs/default.json

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+{
+    "model": {
+        "input_dim": 5,
+        "output_dim": 72,
+        "hidden_dims": [128, 256, 256, 128],
+        "dropout": 0.2,
+        "n_heads": 8,
+        "n_layers": 4
+    },
+    "training": {
+        "epochs": 300,
+        "batch_size": 8,
+        "learning_rate": 0.001,
+        "weight_decay": 0.0001,
+        "patience": 50
+    },
+    "loss_weights": {
+        "data": 1.0,
+        "physics": 0.5,
+        "smooth": 0.1,
+        "damage": 0.3,
+        "mogi_coulomb": 0.2,
+        "regularization": 0.0001
+    },
+    "damage_params": {
+        "freeze_thaw": {
+            "a1": 0.002,
+            "b1": 1.0,
+            "c1": 0.02
+        },
+        "chemical": {
+            "a2": 0.01,
+            "b2": 1.5
+        },
+        "thermal": {
+            "T0": 100.0,
+            "a3": 0.0003,
+            "b3": 1.2
+        }
+    },
+    "data": {
+        "train_ratio": 0.64,
+        "val_ratio": 0.16,
+        "test_ratio": 0.20,
+        "random_seed": 42
+    }
+}

configs/fast_train.json

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+{
+    "model": {
+        "input_dim": 5,
+        "output_dim": 72,
+        "hidden_dims": [64, 128, 128, 64],
+        "dropout": 0.1,
+        "n_heads": 4,
+        "n_layers": 2
+    },
+    "training": {
+        "epochs": 100,
+        "batch_size": 16,
+        "learning_rate": 0.001,
+        "weight_decay": 0.0001,
+        "patience": 20
+    },
+    "loss_weights": {
+        "data": 1.0,
+        "physics": 0.3,
+        "smooth": 0.1,
+        "damage": 0.2,
+        "mogi_coulomb": 0.1,
+        "regularization": 0.0001
+    }
+}

configs/large.json

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+{
+    "model": {
+        "input_dim": 5,
+        "output_dim": 72,
+        "hidden_dims": [256, 512, 512, 256],
+        "dropout": 0.3,
+        "n_heads": 16,
+        "n_layers": 6
+    },
+    "training": {
+        "epochs": 500,
+        "batch_size": 4,
+        "learning_rate": 0.0005,
+        "weight_decay": 0.001,
+        "patience": 100
+    },
+    "loss_weights": {
+        "data": 1.0,
+        "physics": 0.8,
+        "smooth": 0.15,
+        "damage": 0.5,
+        "mogi_coulomb": 0.3,
+        "regularization": 0.0001
+    },
+    "damage_params": {
+        "freeze_thaw": {
+            "a1": 0.002,
+            "b1": 1.0,
+            "c1": 0.02
+        },
+        "chemical": {
+            "a2": 0.01,
+            "b2": 1.5
+        },
+        "thermal": {
+            "T0": 100.0,
+            "a3": 0.0003,
+            "b3": 1.2
+        }
+    },
+    "data": {
+        "train_ratio": 0.64,
+        "val_ratio": 0.16,
+        "test_ratio": 0.20,
+        "random_seed": 42
+    },
+    "scheduler": {
+        "type": "cosine",
+        "warmup_epochs": 20,
+        "min_lr": 1e-6
+    }
+}

configs/lightweight.json

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+{
+    "model": {
+        "input_dim": 5,
+        "output_dim": 72,
+        "hidden_dims": [64, 128, 64],
+        "dropout": 0.1,
+        "n_heads": 4,
+        "n_layers": 2
+    },
+    "training": {
+        "epochs": 50,
+        "batch_size": 32,
+        "learning_rate": 0.002,
+        "weight_decay": 0.0001,
+        "patience": 10
+    },
+    "loss_weights": {
+        "data": 1.0,
+        "physics": 0.2,
+        "smooth": 0.05,
+        "damage": 0.1,
+        "mogi_coulomb": 0.05,
+        "regularization": 0.0001
+    }
+}