| { |
| "model_name": "Deep Learning Weather Prediction Cubed-Sphere Compact", |
| "model_type": "dlwp-cs-compact", |
| "architectures": [ |
| "DLWPCubeSphereUNet" |
| ], |
| "framework": "PyTorch", |
| "domain": "atmosphere", |
| "task": "autoregressive-global-weather-field-prediction", |
| "implementation": { |
| "entry_point": "model/model.py", |
| "scope": "compact structural smoke implementation; synthetic channels have no physical-variable semantics" |
| }, |
| "architecture": { |
| "family": "cubed-sphere U-Net", |
| "input_channels": 2, |
| "output_channels": 2, |
| "base_channels": 4, |
| "cube_faces": 6, |
| "default_face_shape": [ |
| 8, |
| 8 |
| ], |
| "unet_levels": 1, |
| "activation": "capped_leaky_relu", |
| "negative_slope": 0.1, |
| "activation_cap": 10.0, |
| "cross_face_padding": true |
| }, |
| "workflow": { |
| "default_rollout_steps": 2, |
| "training_objective": "mean_squared_error", |
| "physical_variable_semantics": null |
| }, |
| "configuration_sources": [ |
| "model/model.py", |
| "model/dataset.py", |
| "scripts/train.py", |
| "README.md" |
| ] |
| } |
|
|
