forecasting-models / code /records /waves /fid_adapter.json
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{
"adapter": "heterogeneous_waves_augmented_coarse",
"field_kind": "signed",
"quantile": 0.9999,
"clip_c": 2.3817124402523167,
"range": [
-2.3817124402523167,
2.3817124402523167
],
"n_train_states": 1600,
"field_shape": [
128,
128
],
"pixel_stride": 1,
"n_pixel_values": 26214400,
"map": "uint8 = rint((clip(w,-c,c)+c)/(2c)*255)",
"channels": "3 identical (grayscale scalar field), no colormap",
"source": {
"packaged_dataset_path": "<dataset>",
"block": "vecs_train",
"ics": "canonical rows 0..79",
"members": "j < 20",
"snapshots": [
40
],
"pixel_stride": 1,
"n_states": 1600,
"rule": "training data only; validation, test and generated data never enter"
},
"state": "392-D augmented state (196-D coarse displacement u + 196-D finite-difference coarse velocity v_hat); this adapter reads ONLY the first 196 (displacement) coordinates -- identical reconstruction to heterogeneous_waves_coarse",
"reconstruction": "exact inverse FFT of the |k| <= 8 modes on a 128x128 grid (alias-free)",
"n_disp": 128,
"k_c": 8,
"clip": "symmetric [-c, c], c = Q_q(|u|) over the training pool",
"physical_diagnostic": "displacement_variance_ratio = mean_gen sum|u_hat_k|^2 / mean_truth sum|u_hat_k|^2; gradient_energy_ratio = mean_gen sum|k|^2|u_hat_k|^2 / mean_truth sum|k|^2|u_hat_k|^2"
}