{
  "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"
  ]
}