forecasting-models / code /records /nlkg /fid_adapter.json
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{
"adapter": "nlkg_coarse_u",
"field_kind": "signed",
"quantile": 0.9999,
"clip_c": 0.5955045945108044,
"range": [
-0.5955045945108044,
0.5955045945108044
],
"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 coarse state (196-D u + 196-D v/4, house coarse operator |k| <= 8 on each); the image reads ONLY the first component u -- identical reconstruction to euler_coarse_vorticity",
"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_coarse|) over the training pool's final-time fields",
"physical_diagnostic": "coarse_u_variance_ratio, gradient_energy_ratio (on u), coarse_v_variance_ratio (on v/4)"
}