Datasets:

ayz2
/

superwing_processed

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

SuperWing (processed)

This is a processed, downsampled version of SuperWing, 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 2.6 TB, therefore the volume field was downsampled by 3x to obtain a size of 0.7 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   (packed in shards/*.tar, see below)
volume_collated/          volume
  manifest.json
  norm_stats_volume*.npz
  samples/<stem>.npy   (packed in shards/*.tar, see below)
RELEASE.json              downsampling factors, columns and row totals

This dataset has ~29k samples per tree, so the per-sample files are packed into ~5 GB uncompressed tar shards to stay within Hub file-count limits. The member paths are relative to the dataset root, so this reproduces the per-sample layout above:

cd <download dir>
for f in collated/shards/*.tar volume_collated/shards/*.tar; do tar -xf "$f"; done

<tree>/shards/index.json maps each stem to its shard. A single sample can be pulled with tar -xf <shard> <tree>/samples/<stem>.npy.

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, T.

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

Dropped from this release: rho_tilde, layer.

Splits

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

train: 23,111 samples
val: 5,745 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.