Datasets:

ayz2
/

drivaerml_processed

license: cc-by-sa-4.0
pretty_name: DrivAerML (processed)
tags:
  - cfd
  - aerodynamics
  - point-cloud
  - physics
  - surrogate-modeling
size_categories:
  - 10B<n<100B

DrivAerML (processed)

This is a processed, downsampled version of DrivAerML, 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 2.3 TB, so both the surface and volume fields are downsampled by 3x to reach a size of 0.75 TB.

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	`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)

Dropped from this release: Cp, pPrime2.

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

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

train: 386 samples
val: 97 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.