Add model configuration

config.json

@@ -0,0 +1,128 @@
+{
+  "architectures": [
+    "FNO2d"
+  ],
+  "framework": "pytorch",
+  "model_name": "FNO",
+  "model_type": "fno",
+  "source_config": "config/config.yaml",
+  "paper": {
+    "title": "Fourier Neural Operator for Parametric Partial Differential Equations",
+    "arxiv": "2010.08895",
+    "experiment": "FNO-2D Navier-Stokes, nu=1e-5, T=20",
+    "viscosity": 1e-05,
+    "reference_relative_l2": 0.1556,
+    "reference_parameter_count": 414517,
+    "reference_epoch_seconds_v100": 127.8
+  },
+  "data": {
+    "root": "/public/share/sugonhpcapp01/onestore/onedatasets/FNO_data",
+    "file": "NavierStokes_V1e-5_N1200_T20.mat",
+    "key": "u",
+    "layout": "N,H,W,T",
+    "dtype": "float32",
+    "expected_shape": [
+      1200,
+      64,
+      64,
+      20
+    ],
+    "resolution": [
+      64,
+      64
+    ],
+    "ntrain": 1000,
+    "ntest": 200,
+    "train_start": 0,
+    "test_start": 1000,
+    "history": 10,
+    "horizon": 10,
+    "recording_interval": 1.0,
+    "future_times": [
+      11,
+      12,
+      13,
+      14,
+      15,
+      16,
+      17,
+      18,
+      19,
+      20
+    ],
+    "normalization": "none"
+  },
+  "model": {
+    "name": "FNO2d",
+    "input_channels": 10,
+    "output_channels": 1,
+    "use_grid": true,
+    "grid_channels": 2,
+    "grid_include_endpoint": false,
+    "width": 32,
+    "modes1": 12,
+    "modes2": 12,
+    "num_layers": 4,
+    "projection_width": 128,
+    "activation": "relu",
+    "normalization": "batch_norm",
+    "block_order": "relu(batch_norm(spectral_plus_pointwise))",
+    "fft_norm": "backward",
+    "spectral_init": "scaled_uniform_complex"
+  },
+  "training": {
+    "epochs": 500,
+    "batch_size": 20,
+    "optimizer": "adam",
+    "learning_rate": 0.001,
+    "weight_decay": 0.0,
+    "scheduler": "step_lr",
+    "scheduler_step_size": 100,
+    "scheduler_gamma": 0.5,
+    "seed": 0,
+    "dtype": "float32",
+    "amp": false,
+    "gradient_clipping": null,
+    "ema": false,
+    "distributed": "single",
+    "num_workers": 0,
+    "pin_memory": true,
+    "deterministic": true,
+    "relative_l2_epsilon": 1e-12,
+    "train_rollout_steps": 10,
+    "evaluation_rollout_steps": 10,
+    "checkpoint_monitor": "train_full_relative_l2",
+    "checkpoint_mode": "min",
+    "evaluate_test_every_epoch": true
+  },
+  "inference": {
+    "batch_size": 20,
+    "seed": 0,
+    "dtype": "float32",
+    "rollout_steps": 10
+  },
+  "paths": {
+    "checkpoint": "weight/best_model.pth",
+    "results_dir": "results",
+    "train_history": "results/train_history.json",
+    "predictions": "results/predictions.npz",
+    "metrics": "results/metrics.json",
+    "per_sample_metrics": "results/per_sample_metrics.csv",
+    "training_curves": "results/training_curves.png",
+    "rollout_figure": "results/sample_000_rollout.png",
+    "run_metadata": "results/run_metadata.json",
+    "summary": "results/summary.md"
+  },
+  "assumptions": [
+    "The paper does not specify a validation split; the best checkpoint is selected using train full-trajectory relative L2, never test error.",
+    "The paper does not specify batch size or seed; batch_size=20 and seed=0 are explicit engineering assumptions.",
+    "The paper does not define the exact relative-L2 reduction; ratios are computed per sample and then averaged with epsilon=1e-12.",
+    "The paper does not specify the projection hidden width; projection_width=128 is configurable.",
+    "Coordinate-grid input is configurable and enabled; the periodic grid excludes the duplicated endpoint.",
+    "The block ordering is ReLU(BatchNorm(spectral + pointwise)); the paper states ReLU and batch normalization but not their exact order.",
+    "Adam uses weight_decay=0.0 because the paper does not state an additional weight-decay regularizer."
+  ],
+  "conflicts": [
+    "The paper states d_v=32 but reports 414,517 parameters without enough connection details to reproduce both uniquely. Width 32 takes precedence and the actual parameter count must be reported."
+  ]
+}