ayz2/double_delta_processed

Double-Delta (processed)

This is a processed, downsampled version of Double-Delta, 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. For dataloaders and processing details please see our paper, or Github.

The original size was 0.23 TB, therefore there was no downsampling done for this dataset.

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, 14 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
7	n_x	outward normal
8	n_y	outward normal
9	n_z	outward normal
10	area	surface element area
11	yPlus	wall y+
12	rho_tilde	density / freestream density
13	t_tilde	temperature / freestream temperature

Volume (volume_collated, 9 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
7	rho_tilde	density / freestream density
8	t_tilde	temperature / freestream temperature

Splits

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

• train: 2,304 samples
• val: 144 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.