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"model_name": "Surya",
"model_type": "surya",
"architectures": [
"Surya"
],
"framework": "PyTorch",
"domain": "heliophysics",
"task": "solar-dynamics-forecasting",
"implementation": {
"entry_point": "model/surya.py",
"scope": "spectral-gated and long-short-attention autoregressive forecasting of aligned SDO AIA/HMI observations",
"train_script": "scripts/train.py",
"inference_script": "scripts/inference.py",
"evaluation_script": "scripts/result.py",
"synthetic_data_script": "scripts/fake_data.py"
},
"architecture": {
"family": "spectral-gated spatiotemporal transformer",
"image_size": 32,
"patch_size": 4,
"input_channels": 13,
"input_steps": 2,
"forecast_steps": 4,
"embed_dim": 64,
"depth": 4,
"spectral_blocks": 1,
"num_heads": 4,
"window_size": 3,
"global_tokens": 4,
"prediction_mode": "autoregressive"
},
"data": {
"datasets": [
"SDO/AIA",
"SDO/HMI"
],
"protocol": "synthetic_SDO_AIA_HMI",
"format": "NPZ",
"input_key": "inputs",
"input_shape": [
"N",
2,
13,
"H",
"W"
],
"target_key": "targets",
"target_shape": [
"N",
"S",
13,
"H",
"W"
],
"activity_key": "activity",
"activity_shape": [
"N",
"S"
],
"channel_names": [
"AIA_94",
"AIA_131",
"AIA_171",
"AIA_193",
"AIA_211",
"AIA_304",
"AIA_335",
"AIA_1600",
"HMI_magnetogram",
"HMI_continuum",
"HMI_doppler",
"HMI_vector_x",
"HMI_vector_y"
],
"normalization": "signum-log followed by configured per-channel affine normalization",
"default_train_file": "data/train.npz",
"default_test_file": "data/test.npz"
},
"configuration_sources": [
"conf/config.yaml",
"model/surya.py",
"scripts/fake_data.py",
"scripts/train.py",
"scripts/inference.py",
"scripts/result.py"
]
}
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