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

# WindsorML (processed)

This is a processed, downsampled version of WindsorML, 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](https://arxiv.org/abs/2407.19320). For dataloaders and processing details please see our [paper](https://arxiv.org/abs/2610.10398v1), or [Github](https://github.com/anthonyzhou-1/cross_domain_cfd/tree/main).

The original size was 2.1 TB, therefore we downsample the volume fields by 3x to obtain a size of 0.71 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`, 9 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 | `cp_var` | pressure-coefficient variance |
| 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

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

- `train`: 280 samples
- `val`: 70 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.