| { |
| "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" |
| } |
| } |
|
|
