{
  "model_name": "StableNN-Phys",
  "model_type": "stablenn_phys",
  "architectures": ["StableNNPhys"],
  "framework": "PyTorch",
  "domain": "atmospheric physics",
  "task": "single-column prognostic rollout",
  "implementation": {
    "entry_point": "model/stablenn_phys.py",
    "scope": "Core-method, full-window and full-vertical-dimension reduced-sample engineering reproduction",
    "train_script": "scripts/train.py",
    "inference_script": "scripts/inference.py",
    "evaluation_script": "scripts/result.py",
    "synthetic_data_script": "scripts/fake_data.py"
  },
  "architecture": {
    "levels": 34,
    "input_features": 71,
    "output_features": 68,
    "engineering_hidden_size": 32,
    "paper_hidden_size": 128,
    "activation": "ReLU",
    "linear_bypass": true
  },
  "integration": {
    "step_hours": 3,
    "training_window_steps": 20,
    "rollout_steps": 64,
    "forcing": "trapezoidal horizontal-advection update before neural-network Euler physics update",
    "teacher_forcing": false
  },
  "checkpoint": {
    "path": "result/checkpoints/stablenn_phys.pt",
    "required_fields": ["model", "model_config", "format_version"],
    "format_version": "stablenn_phys_checkpoint_v1"
  },
  "loss_modes": {
    "default": "paper",
    "paper": "layer-mass-weighted MAD of prognostic state errors over all rollout steps",
    "official_v0_3": "equal-level normalized MAD of prognostic state errors over all rollout steps"
  }
}