Update README.md

README.md

@@ -7,9 +7,11 @@ size_categories: [10B<n<100B]
 
 # WindsorML (processed)
 
-> **This is a processed, downsampled derivative of WindsorML, not the original dataset.** Fields were converted to a common frame and non-dimensionalization, rows were randomly subsampled and some columns were dropped (details below). For the original data, see the upstream source.
+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) 
 
-A preprocessed, downsampled release of WindsorML. Total size is 0.72 TB. Every sample is a float32 point cloud `[n_points, n_cols]` stored as `.npy`, in a common frame and non-dimensionalization shared across the campaigns of this release.
+The original size was 2.1 TB, therefore we downsample the volume fields by 3x to obtain a size of 0.71 TB. 
 
 ## Layout
 
@@ -54,15 +56,6 @@ RELEASE.json              downsampling factors, columns and row totals
 | 5 | `uz` | velocity |
 | 6 | `p` | static pressure (raw solver units; Cp = (p - p_inf) / q) |
 
-## Downsampling
-
-| tree | samples | row factor | rows (release) | rows (collated source) |
-|---|---|---|---|---|
-| `collated` | 350 | 1/1 | 7.935e+08 | 7.935e+08 |
-| `volume_collated` | 350 | 1/3 | 2.472e+10 | 7.417e+10 |
-
-**Rows within each file are in random order**: they were permuted when the files were written. The release keeps the first `n // factor` rows of each collated sample, which is a uniform random subsample of the mesh points. For the same reason, **any contiguous window `arr[s:s+k]` is a uniform random crop**, so `np.load(..., mmap_mode='r')` plus a slice is the intended access pattern. The shipped `norm_stats*.npz` were computed on the full collated trees and remain valid for the subsample.
-
 ## Splits
 
 The volume tree uses the surface splits (keyed by stem).
@@ -70,44 +63,4 @@ The volume tree uses the surface splits (keyed by stem).
 - `train`: 280 samples
 - `val`: 70 samples
 
-## Source dataset and preprocessing
-
-### WindsorML
-
-**What it is.** 355 variants of the Windsor body (a squareback automotive reference geometry),
-released as time-averaged wall-modelled LES. It is the only campaign here with no RANS branch
-anywhere in the closure.
-
-**Generation.** Genuine **WMLES** with the constant-coefficient **Vreman** SGS model and an
-equilibrium wall model imposed as a shear-stress constraint with the outer state probed at a
-fixed 1.5Δ off the wall. Compressible and *explicit*: nominally fourth-order finite differences
-with SSP-RK3 time stepping on Cartesian octree grids (unstructured tree-of-cubes, leaf cubes of
-4³ or 8³ cells) with an immersed-boundary geometry representation, in Volcano ScaLES. Baseline
-grid 275 M cells at 0.75 mm minimum spacing. Fields are time-averaged.
-
-**Parameters.** U∞ = 40 m/s, Re = 2.9e6 on body length (1.044 m); pitching-moment reference
-length 0.6375 m (wheelbase), reference frontal area 0.112 m² for the baseline geometry.
-
-**Cost.** One AWS `g5.48xlarge` node per case (8 × NVIDIA A10G), ~2 min to mesh and ~28 h to
-solve = 224 GPU-hours per case, ≈ 7.8e4 GPU-hours for the campaign.
-
-**Processing.** 350 of the 355 runs are present. The surface file stores fields on *points*
-and already ships dimensionless coefficients, so no dynamic-pressure conversion is applied:
-`[x, y, z, cp, cfx, cfy, cfz, cpvar, yPlus]` (9 cols). The **native frame is rotated relative
-to every other campaign** — x is streamwise, **y is vertical** (floor at y ≈ 0) and z is the
-lateral/width axis — so WindsorML carries an `ORIENT` signed permutation
-(`x ← x, y ← −z, z ← y`) applied to positions, to the surface Cf vector and to the volume
-velocity vector, mapping it into the shared loader frame. The volume is a 21 GB base64/zlib
-inline VTU per run (~325 M points / 291 M hexahedra) whose fields live on *cells* with no
-stored centroids, so cell centres are computed explicitly; one read costs ~227 s and ~36 GB
-RSS. Crop margins in native axes are `[0.5, 0.0, 0.75]/[2.0, 0.5, 0.75] L`
-(stream/vertical/width, never extending below the floor), keeping ~79% of cells. Volume output
-`[x, y, z, ux, uy, uz, p]`, 74.2e9 cells total. One caveat worth recording: the volume tree
-stores pressure in Pa against a solver reference that matches no documented ambient; regressing
-wall-adjacent volume pressure on the paired surface Cp gives q = 1016.6 and p∞ = 37 340.5 at
-R² = 0.88, and these fitted values (not measured ones) are what the unified physical
-normalization uses for this campaign. Splits: random 80/20 (280/70).
-
-## Citation
-
-TODO: this work, and the upstream dataset's citation.
+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.