ayz2/ahmedml_processed

AhmedML (processed)

This is a processed, downsampled version of AhmedML, 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 0.28 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 (Channel dimension)

Surface (collated, 9 columns):

col	name	meaning
0	x	position
1	y	position
2	z	position
3	p	static pressure (raw solver units; Cp = (p - p_inf) / q)
4	tau_x	wall shear stress (raw)
5	tau_y	wall shear stress (raw)
6	tau_z	wall shear stress (raw)
7	Cp	pressure coefficient (as shipped upstream)
8	yPlus	wall y+

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	p	static pressure (raw solver units; Cp = (p - p_inf) / q)

Splits and Normalization

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

• train: 400 samples
• val: 100 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.