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env_id int64 | env_params dict | trajectories array 4D |
|---|---|---|
11 | {
"equation_label": 11,
"viscosity": 0.002154434690031882
} | [[[[9.988470077514648,8.740161895751953,7.508808612823486,6.164109706878662,5.2461724281311035,4.056(...TRUNCATED) |
0 | {
"equation_label": 0,
"viscosity": null
} | [[[[2.3870861530303955,2.1087801456451416,1.8405401706695557,1.5836067199707031,1.3389275074005127,1(...TRUNCATED) |
0 | {
"equation_label": 0,
"viscosity": null
} | [[[[-2.54067063331604,-2.7960293292999268,-3.050713539123535,-3.299489736557007,-3.537106513977051,-(...TRUNCATED) |
1 | {
"equation_label": 1,
"viscosity": 0.0001
} | [[[[9.941359519958496,10.20877456665039,7.601693630218506,7.622137069702148,12.2293701171875,13.1351(...TRUNCATED) |
0 | {
"equation_label": 0,
"viscosity": null
} | [[[[1.9609119892120361,2.330174684524536,2.726069688796997,3.1435673236846924,3.577378034591675,4.02(...TRUNCATED) |
5 | {
"equation_label": 5,
"viscosity": 0.0003414548873833601
} | [[[[0.3421672582626343,1.4588549137115479,3.4991164207458496,3.7289695739746094,3.568474054336548,3.(...TRUNCATED) |
0 | {
"equation_label": 0,
"viscosity": null
} | [[[[2.259415864944458,2.376446008682251,2.4604225158691406,2.509657621383667,2.522885322570801,2.499(...TRUNCATED) |
0 | {
"equation_label": 0,
"viscosity": null
} | [[[[-6.668506145477295,-6.376534461975098,-6.057285308837891,-5.714372158050537,-5.35155725479126,-4(...TRUNCATED) |
0 | {
"equation_label": 0,
"viscosity": null
} | [[[[4.064770698547363,3.8345541954040527,3.571230888366699,3.277848243713379,2.957813024520874,2.614(...TRUNCATED) |
0 | {
"equation_label": 0,
"viscosity": null
} | [[[[5.916979789733887,5.653343677520752,5.358790397644043,5.036681652069092,4.6905517578125,4.324075(...TRUNCATED) |
End of preview.
disco-ns
Trajectories used in the ICML 2026 paper Test-time Generalization for Physics through Neural Operator Splitting (Serrano, Han, Oyallon, Ho, Morel).
Format
Each HDF5 file contains:
trajectories: shape(N, T, C, *spatial), float32env_id: shape(N,), int64 — environment index for each trajectoryenv_params/*: optional metadata mapping env_id back to PDE coefficients
Load via the project's train_generic.py / test_generic.py scripts:
from train.train_generic import GenericHDF5Dataset
ds = GenericHDF5Dataset(["train.h5"])
Or using huggingface_hub:
from huggingface_hub import hf_hub_download
local = hf_hub_download(repo_id="sogeeking/disco-ns", filename="train.h5", repo_type="dataset")
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