ayz2/hiliftaeroml_processed

HiLiftAeroML (processed)

This is a processed, downsampled version of HiLiftAeroML, not the original dataset. Fields were converted to a common frame and non-dimensionalized, rows were randomly subsampled and some variables were dropped. For the original data, see the original paper

The original size was 10.4 TB, therefore surface fields were downsampled by a factor of 12x and volume fields were downsampled by a factor of 10x, for a final size of 0.88TB.

Layout

collated/                 surface
  manifest.json           [{stem, n_points, n_points_source, ...}]
  splits/{train,val}.json
  norm_stats*.npz         per-column normalization statistics
  samples/<stem>.npy
volume_collated/          volume
  manifest.json
  norm_stats_volume*.npz
  samples/<stem>.npy
RELEASE.json              downsampling factors, columns and row totals

Columns

Surface (collated, 7 columns):

col	name	meaning
0	x	position
1	y	position
2	z	position
3	cp	pressure coefficient
4	cf_x	skin-friction coefficient
5	cf_y	skin-friction coefficient
6	cf_z	skin-friction coefficient

Dropped from this release: rho_tilde, yPlus.

Volume (volume_collated, 7 columns):

col	name	meaning
0	x	position
1	y	position
2	z	position
3	ux	velocity
4	uy	velocity
5	uz	velocity
6	cp	pressure coefficient

Splits

The volume tree uses the surface splits (keyed by stem).

• train: 1,608 samples
• val: 179 samples

Either use norm_stats_centered.npz, or norm_stats_thinned.npz. The centered stats are computed on the simulation mesh (i.e., native discretization), while the thinned stats are computed after (roughly) uniform sampling.