README.md

metadata

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

DrivAerNet++ (processed)

This is a processed, downsampled version of DrivAerNet++, 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 around 2.3TB, therefore the volume fields was downsampled 5x, and the surface field is kept as is to reduce the size to 0.78 TB.

Layout

collated/                 surface
  manifest.json           [{stem, n_points, n_points_source, ...}]
  splits/{test_old,train,train_old,val,val_old}.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)

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).

• test_old: 1,385 samples (contains held-out fastback geometries)
• train: 6,502 samples
• train_old: 5,394 samples (removes fastback geometries from 'train' split)
• val: 1,626 samples
• val_old: 1,349 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.