{
  "model_name": "Spherical Fourier Neural Operator",
  "model_type": "sfno",
  "architectures": [
    "OfficialSFNOAdapter"
  ],
  "framework": "PyTorch with torch-harmonics",
  "domain": "spherical-dynamics",
  "task": "autoregressive-spherical-field-prediction",
  "implementation": {
    "entry_point": "model/sfno_adapter.py",
    "scope": "operator-level smoke configuration using the official torch-harmonics SFNO class"
  },
  "architecture": {
    "family": "spherical Fourier neural operator",
    "input_grid_shape": [
      17,
      32
    ],
    "input_grid": "equiangular",
    "internal_grid": "legendre-gauss",
    "input_channels": 2,
    "output_channels": 2,
    "embedding_size": 8,
    "operator_layers": 2,
    "spectral_scale_factor": 2,
    "normalization": "none",
    "residual_prediction": false,
    "positional_embedding": "none"
  },
  "workflow": {
    "synthetic_time_steps": 6,
    "autoregressive_rollout_steps": 3,
    "physical_variable_semantics": null
  },
  "configuration_sources": [
    "model/default_config.json",
    "model/config.py",
    "model/sfno_adapter.py",
    "README.md"
  ]
}