--- pretty_name: AeroZoo v1.2 viewer: false license: cc-by-4.0 tags: - cfd - computational-fluid-dynamics - rans - openfoam - physicsnemo - aerodynamics - mesh - point-cloud - physics-ai - arxiv:2611.17152 size_categories: - 1K Velocity slices across AeroZoo's seven geometry families and eight enclosure configurations

Most 3D aerodynamics datasets vary a single parametric shape family inside one fixed wind tunnel, allowing surrogate models to memorize coordinate-to-field shortcuts instead of solving boundary-value problems or generalizing out of distribution. **AeroZoo** poses 3,816 distinct 3D incompressible flow problems across Reynolds numbers from \\(10^3\\) to \\(10^8\\), solved with unsteady RANS (Langtry–Menter transition SST in OpenFOAM 14) and time-averaged over a fixed convective window.

One car geometry placed inside four different box enclosures at the same reference speed and Reynolds number, producing four distinct velocity fields and force coefficients

One geometry, four boundary-value problems: placing the same car at the same reference speed and Reynolds number into four enclosures shifts axial force from Cx = 0.025–0.029 to 0.554 and vertical force from near zero to Cz = +1.066.

Every sample is stored in dimensional SI units as a PhysicsNeMo `DomainMesh` (a memory-mapped PyTorch `TensorDict` tree) that bundles the posed boundary-value problem with its time-mean solution across: - **Seven geometry families:** Blobs, Blender bluff, Procedural cars, Zero-to-CAD, Airframe Foundry, Streamlined, and Blender streamlined (plus one mascot sample outside every split). - **Eight enclosure configurations** across four enclosure families: rectangular boxes with five boundary layouts (open, ground-effect, confined, crossflow, and dual-inlet), opposed inlet/outlet spheres, symmetry hemispheres, and internal ducts (with or without an internal body). - **Three body-wall layouts:** all no-slip, two contiguous slip/no-slip bands, or three bands. **v1.2 is the official release**, exported 2026-10-08 from a population frozen on 2026-10-02. Authors: Peter Sharpe\*, Carmelo Gonzales\*, Corey Adams, Mehdi Ataei, and Sanjay Choudhry (NVIDIA; \*lead authors). ## Links - This dataset: - PhysicsNeMo (`DomainMesh`): - The AeroZoo paper: ## Visual Overview Every body stores time-averaged pressure on its remeshed boundary triangles (`no_slip` and `slip`) and wall shear stress on `no_slip`, spanning high-genus organic blobs, bluff shapes, procedural cars, CAD parts, aircraft, streamlined bodies, wings, and the mascot:

3D surface renders of pressure coefficient and skin-friction coefficient across all seven geometry families and the mascot, showing slip bands in gray

At matched sample counts, AeroZoo's seven geometry families span 25 to 390 effectively distinct shapes out of 400 under the Vendi diversity score (compared with 13 for DrivAerML and 11 for SHIFT-SUV at 400 shapes, and 7 out of 126 for HiLiftAeroML). Across five decades of Reynolds number, boundary layers transition from fully laminar near \\(Re \approx 10^3\\) (tracking Blasius flat-plate friction) through transitional turbulent wedges near \\(Re \approx 10^5\\) to fully turbulent at \\(Re \ge 10^6\\) (tracking Prandtl turbulent friction):

Vendi shape diversity across AeroZoo geometry families compared with single-family datasets, alongside boundary-layer laminar fraction and skin-friction coefficient across five decades of Reynolds number

Five streamlined bodies from Re = 8.0e3 to Re = 3.7e7 colored by first-cell intermittency, showing laminar-to-turbulent transition

Alongside external enclosures, 303 released internal-duct samples span 10 passage geometries (161 body-free ducts and 142 ducts containing an internal body):

Mean velocity slices through one body-free internal-duct sample of each of the 10 passage geometries in AeroZoo

## Contents | Quantity | Value | |:---|:---| | Released samples | 3,816 (3,815 in splits + one mascot outside every split) | | Stability labels | 2,817 `mean_stable` / 999 `mean_unstable` (canonical benchmark uses `mean_stable`) | | Interior query points | 23.8 billion total; median 5.0M cells per sample (range 0.6M–24M) | | Total storage | 1.48 TB in uncompressed tars; median 347 MB per sample | | Benchmark splits and tasks | 22 frozen train / validation / test splits; two tasks (interior and boundary) | ```text README.md this card LICENSE the dataset license NOTICE third-party credits and terms: Zero-to-CAD 100K (Apache License 2.0) and UIUC Airfoil Coordinates Database zero-to-cad-provenance.csv one row per Zero-to-CAD body in a sample: the part, its source revision, and NVIDIA's changes to it assets/ dataset overview and sample-anatomy figures index.json one record per released sample: identity, hashes, stability label, quality diagnostics, and export lineage release-v1.2.json release manifest: status per planned case (complete / failed / timed_out), planned and released sizes per split part, and SHA-256 digests of index.json and the split files splits/cases.json planned population: geometry family (`bank`), enclosure family, box layout (`profile`), body-wall layout, Reynolds decade, source geometry hash, and split placement per case splits/splits.json 22 named splits as "_" -> [case_id, ...] splits/body-free-ducts.json the 219 internal-duct case IDs posed without an internal body splits/splits-metadata.json placement scheme (geometry hash) and design seed (note: `full` is unstratified despite its description string here) checksums.sha256 SHA-256 of every sample tar SHA256SUMS SHA-256 of every data file of the unpacked release (see Verifying a copy) samples/shard-NNN/.tar one uncompressed tar per sample (shards are administrative only) ``` Each sample tar unpacks to one directory: ```text / domain.pdmsh/ PhysicsNeMo DomainMesh (memory-mapped TensorDict tree, schema 5) domain.pdmsh.validated.json reload-validation receipt of the artifact reexport-lineage.json validated solver attempt and exporter provenance smoke-audit.json structural audit of the exported artifact case-manifest.json the posed boundary-value problem status.json execution outcome and attempt history result.yaml solver summary: termination, cell count, and stability ``` ## Loading a sample Install PhysicsNeMo (`>=2.1.0` for `DomainMesh`, Python `>=3.11`) and the Hugging Face client: ```bash pip install "nvidia-physicsnemo>=2.1.0" "huggingface_hub>=1.7.1" ``` Load one sample lazily via memory mapping (tensors are read on first access): ```python import json, tarfile from pathlib import Path from huggingface_hub import hf_hub_download from physicsnemo.mesh import DomainMesh repo = "nvidia/AeroZoo" index = json.loads(Path(hf_hub_download(repo, "index.json", repo_type="dataset")).read_text()) case_id, record = next(iter(index["samples"].items())) tar_path = hf_hub_download(repo, f"samples/shard-{record['shard']:03d}/{case_id}.tar", repo_type="dataset") with tarfile.open(tar_path) as tar: tar.extractall(Path("aerozoo"), filter="data") sample = DomainMesh.load(Path("aerozoo") / case_id / "domain.pdmsh") interior = sample.interior # one point per finite-volume cell centroid x = interior.points # [N, 3] meters, query coordinates u_mean = interior.point_data["solution", "velocity"] # [N, 3] m/s, a target volume = interior.point_data["_target_quadrature_measure"] # [N] m^3, loss and metric weight inlet = sample.boundaries["inlet"] # triangle mesh; prescribed/* are inputs u_in = inlet.cell_data["prescribed", "dirichlet", "velocity"] # [F, 3] m/s nu = sample.global_data["physics", "kinematic_viscosity"] # () m^2/s, an input stable = sample.global_data["diagnostics", "solve", "mean_window", "stability_pass"] # () bool; True iff mean_stable ``` To download a specific split in parallel without duplicating tars in the global cache (for example `super_scarce_train`, 53 canonical samples, ~18 GB): ```python import json from pathlib import Path from huggingface_hub import hf_hub_download, snapshot_download repo = "nvidia/AeroZoo" index = json.loads(Path(hf_hub_download(repo, "index.json", repo_type="dataset")).read_text())["samples"] splits = json.loads(Path(hf_hub_download(repo, "splits/splits.json", repo_type="dataset")).read_text()) patterns = [ f"samples/shard-{index[c]['shard']:03d}/{c}.tar" for c in splits["super_scarce_train"] if c in index and index[c]["quality"]["solution_quality"] == "mean_stable" ] snapshot_download(repo, repo_type="dataset", allow_patterns=patterns, local_dir="aerozoo-raw", max_workers=8) ```

Anatomy of an AeroZoo DomainMesh sample: an aircraft with slip and no-slip bands inside a ground-effect box with inlet, outlet, slip, and no-slip boundary meshes and interior query points

All physical quantities use dimensional SI units. Nested `TensorDict` keys are indexed in Python with string tuples (for example `cell_data["prescribed", "dirichlet", "velocity"]`). A sample contains three top-level groups (example dimensions shown for a 4.55M-cell aircraft sample): ```text DomainMesh |-- interior (Mesh: point cloud of cell centroids, no interior connectivity) | |-- points [4550479, 3] float32 m | |-- cells [0, 1] int64 | `-- point_data | |-- solution | | |-- velocity [4550479, 3] float32 m/s | | |-- gauge_pressure [4550479] float32 Pa | | |-- turbulent_kinetic_energy [4550479] float32 m^2/s^2 | | |-- specific_dissipation_rate [4550479] float32 1/s | | |-- turbulent_kinematic_viscosity [4550479] float32 m^2/s | | |-- intermittency [4550479] float32 1 | | `-- momentum_thickness_reynolds_number [4550479] float32 1 | `-- _target_quadrature_measure [4550479] float32 m^3 (cell volume) |-- boundaries | |-- inlet (Mesh: triangle faces) | | |-- points [12800, 3] float32 m | | |-- cells [25122, 3] int64 | | `-- cell_data | | |-- solution/* [25122, ...] float32 (seven fields, as interior) | | `-- prescribed/dirichlet/{velocity, turbulent_kinetic_energy, | | specific_dissipation_rate, intermittency, momentum_thickness_reynolds_number} | |-- outlet (Mesh: triangle faces) | | |-- points / cells [17289, 3] / [34080, 3] | | `-- cell_data | | |-- solution/* [34080, ...] float32 (seven fields, as interior) | | |-- prescribed/dirichlet/gauge_pressure [34080] float32 Pa (zero) | | `-- prescribed/reverse_flow/* [34080, ...] float32 (five inlet fields) | |-- no_slip (Mesh: body no-slip bands and enclosure walls) | | |-- points / cells [55553, 3] / [110356, 3] | | `-- cell_data | | |-- solution/* [110356, ...] float32 (seven fields, as interior) | | |-- prescribed/dirichlet/velocity [110356, 3] float32 m/s (zero) | | |-- annotation/body_id [110356] int64 (0 enclosure, 1 primary, 2 secondary) | | `-- derived/{wall_shear_stress [F, 3] Pa, wall_y_plus [F]} | `-- slip (Mesh: body slip bands, slip walls, and symmetry planes) | |-- points / cells [68480, 3] / [135042, 3] | `-- cell_data | |-- solution/* [135042, ...] float32 (seven fields, as interior) | `-- annotation/body_id [135042] int64 `-- global_data |-- schema_version () int64 |-- physics/{density () kg/m^3, kinematic_viscosity () m^2/s, body_acceleration [3] m/s^2, closure_model_id ()} |-- reference/{velocity [3], secondary_inlet_velocities [M, 3], force_projection_direction [3], | speed (), length (), dynamic_pressure ()} `-- identity/*, solution_definition/*, diagnostics/* (provenance, averaging window, and stability metrics) ``` A sample stores more tensors than a benchmarked model is permitted to take as input; see [Tasks](#tasks) for the input contract. ## Tasks AeroZoo supports general physics-AI training and evaluation, with two benchmark tasks that map a posed boundary-value problem to its time-averaged solution. ### Inputs, Targets, and Withheld Keys Only data that define the governing boundary-value problem enter a model: | Role | Interior task | Boundary task | |:---|:---|:---| | Allowed inputs | The four boundary triangle meshes (`points` and `cells`, whose mesh names identify each condition), `cell_data["prescribed", ...]`, `global_data["physics", "kinematic_viscosity"]`, `density` (1.225 kg/m³ everywhere, to work in pascals), and `body_acceleration` (zero) | Same inputs (no `interior` points) | | Query coordinates | `interior.points` (cell centroids) | Boundary face centroids, computed from `points` and `cells` | | Predicted targets | `interior.point_data["solution", ...]` (the seven mean fields; predicting velocity and pressure alone is also valid when stated) | `cell_data["solution", ...]` on boundaries and `derived/wall_shear_stress` on `no_slip`, excluding Dirichlet-fixed fields (`inlet` velocity and turbulence, `outlet` pressure, `no_slip` velocity) and `slip` velocity on symmetry hemispheres | | Loss and metric weights (withheld) | `interior.point_data["_target_quadrature_measure"]` (cell volume) | Boundary triangle face areas | | Withheld from model inputs | `solution/*`, `derived/*`, `_target_quadrature_measure`, `annotation/body_id`, and all of `global_data` except `kinematic_viscosity`, `density`, and `body_acceleration` | Same withheld keys, plus `interior` | **Why reference scales and body annotations are withheld.** Keys under `global_data["reference", ...]` (reference length, speed, dynamic pressure, inlet vectors, and force direction) define scoring units rather than the PDE. Renaming which length is called the reference leaves the physical flow unchanged, so feeding `reference/length` or a Reynolds number built on it leaks convention into the inputs. Likewise, the PDE applies the same wall boundary condition regardless of `annotation/body_id` (`0` for enclosure walls, `1` or `2` for bodies); only losses and post-processing metrics use `body_id`. Nor does every problem have a single free-stream vector (for example a 90° duct bend or a dual-inlet box). Models that normalize coordinates or condition on a length, speed, or Reynolds scale derive it from one sample's boundary inputs and pass that derived scale (or Reynolds number) alongside rescaled coordinates. ### Benchmark Rules - **Canonical subset (`mean_stable`):** Train, validate, and test on `mean_stable` samples only. In the remaining 999 `mean_unstable` samples, averages over the two halves of the window differed beyond the stability tolerances (30% of crossflow boxes and 34% of symmetry hemispheres pass, and predicting the inlet velocity everywhere yields median \\(\varepsilon_U = 0.110\\) on `full`'s canonical test set versus 0.184 across all released test samples). Scores over all released samples, or models trained with `mean_unstable` samples included, can be reported beside the canonical result when labeled. - **Body-only test scoring on body-property splits:** The seven geometry-family splits (`bank_*`) and `layout` place body-free ducts according to a body or wall layout drawn from the case seed and never posed. Canonical test sets for those eight splits exclude body-free ducts (12 of 322 in `bank_cars`, 38 of 930 in `layout`). Training and validation parts, and all other splits, retain them. - **Single-sample input functions only:** Any deterministic function of one sample's allowed inputs (such as a boundary-derived length, speed, or Reynolds number) is permitted. Normalization constants or other statistics fitted across test samples are prohibited. - **Validation for selection only:** Select models and hyperparameters on the validation part, then fit scored weights on the training part alone without refitting on `train + val` (`super_scarce` has 53 canonical training samples and 290 validation samples). Hyperparameters chosen on `full`'s validation part can be reused across other splits when stated in the report (every validation case of one split belongs to the test set of another). ### Metrics and Reporting Metrics are evaluated per test sample in that case's reference units so flows spanning a 200-fold range in inlet speed contribute on a common dimensionless scale: | Metric | Task | Definition | |:---|:---|:---| | \\(\varepsilon_U,\;\varepsilon_p\\) | interior | Headline metrics: volume-weighted RMS errors of mean velocity and gauge pressure over the fluid, in units of \\(U_{\mathrm{ref}}\\) and \\(q_{\mathrm{ref}}\\) | | \\(\varepsilon_U^{\mathcal{N}},\;\varepsilon_p^{\mathcal{N}}\\) | interior | Same volume-weighted RMS errors within one \\(L_{\mathrm{ref}}\\) of a body face centroid (samples with a body) | | \\(r_U,\;r_p\\) | interior | Unweighted relative \\(L_2\\) errors \\(\lVert\hat{\mathbf{y}} - \mathbf{y}\rVert_2 / \lVert\mathbf{y}\rVert_2\\) across interior points, for comparison with unweighted ML baselines | | \\(\varepsilon_k,\;\varepsilon_\omega,\;\varepsilon_{\nu_t},\;\varepsilon_\gamma,\;\varepsilon_{\widetilde{\mathrm{Re}}_{\theta t}}\\) | interior | Auxiliary: volume-weighted RMS errors of the five turbulence fields, normalized by \\(U_{\mathrm{ref}}^2\\), \\(U_{\mathrm{ref}}/L_{\mathrm{ref}}\\), \\(U_{\mathrm{ref}}L_{\mathrm{ref}}\\) for \\(k, \omega, \nu_t\\), and by the target field's own volume-weighted RMS for \\(\gamma, \widetilde{\mathrm{Re}}_{\theta t}\\) | | \\(\varepsilon_{C_F}\\) | boundary | Error of the body force coefficient along the primary inlet direction \\(\mathbf{e}_F\\), divided by the larger of \\(C_F\\)'s magnitude and 0.05 | | \\(\varepsilon_{\mathbf{C}}\\) | boundary | Norm of the error of the full body force coefficient vector \\(\mathbf{C}\\), divided by \\(\max(\lVert\mathbf{C}\rVert_2,\,0.05)\\) | | \\(\varepsilon_{C_p},\;\varepsilon_\tau\\) | boundary | Area-weighted RMS errors of pressure on all body faces (\\(\mathcal{B}\\)) and wall shear stress on no-slip body faces (\\(\mathcal{W}\\)), in units of \\(q_{\mathrm{ref}}\\) | | \\(\varepsilon_K\\) | boundary | Error of an internal duct's mass-flow-weighted total-pressure loss coefficient \\(K\\), divided by \\(\max(K,\,0.05)\\) (a non-finite value or predicted outlet mass flux at most 1% of inlet mass flux counts as 1) | Specifically, velocity error, body force coefficient, and axial force error are defined as $$ \begin{aligned} \varepsilon_U &= \frac{1}{U_{\mathrm{ref}}}\left(\frac{\sum_i V_i\,\lVert\hat{\mathbf{u}}_i - \mathbf{u}_i\rVert_2^2}{\sum_i V_i}\right)^{1/2}, \\ \mathbf{C} &= \frac{1}{q_{\mathrm{ref}} L_{\mathrm{ref}}^2}\left(\sum_{j \in \mathcal{B}} p_j A_j\,\mathbf{n}_j - \sum_{j \in \mathcal{W}} A_j\,\boldsymbol{\tau}_j\right), \\ \varepsilon_{C_F} &= \frac{\lvert(\hat{\mathbf{C}} - \mathbf{C}) \cdot \mathbf{e}_F\rvert}{\max(\lvert\mathbf{C} \cdot \mathbf{e}_F\rvert,\,0.05)}, \end{aligned} $$ where \\(V_i\\) is cell \\(i\\)'s volume, \\(\mathcal{B}\\) is the set of body faces (`annotation/body_id >= 1` on `no_slip` and `slip`), \\(\mathcal{W}\\) is the no-slip subset of \\(\mathcal{B}\\), \\(A_j\\) and \\(\mathbf{n}_j\\) are face \\(j\\)'s area and outward normal (pointing out of the fluid), and \\(\mathbf{e}_F\\) is `reference/force_projection_direction`. Note that \\(\mathbf{C}\\) uses the reference area \\(L_{\mathrm{ref}}^2\\) rather than frontal area. **Free-stream reference (\\(\varepsilon_U^{\mathrm{fs}}\\)).** Predicting the area-weighted mean prescribed inlet velocity everywhere (with zero gauge pressure and zero wall shear) requires no training and achieves a median \\(\varepsilon_U\\) of 0.110 on `full`'s canonical test set, ranging from 0.03 in opposed spheres to 1.5 in dual-inlet boxes. Because a split's raw median depends strongly on its enclosure mix, every interior-task evaluation also reports three sample-paired comparisons using each test sample's own \\(\varepsilon_U^{\mathrm{fs}}\\): - the median of \\(\varepsilon_U / \varepsilon_U^{\mathrm{fs}}\\) across test samples, - the fraction of test samples with \\(\varepsilon_U < \varepsilon_U^{\mathrm{fs}}\\), and - the median \\(\varepsilon_U\\) within each of the eight enclosure configurations (`profile` for rectangular boxes and `family` otherwise in `splits/cases.json`). **Reporting checklist.** For every benchmark result, state: 1. Task (`interior` or `boundary`), split name, release version (`v1.2`), and `index.json` SHA-256 (`release-v1.2.json` records it). 2. Whether evaluation and training used the canonical `mean_stable` subset, and whether hyperparameters were reused from `full`'s validation part. 3. Number of random seeds and exact train and test sample counts. 4. Mean and median of each applicable metric across defined test samples (plus shares within 0.02 and 0.05 for \\(\varepsilon_{C_F}\\) and \\(\varepsilon_{\mathbf{C}}\\)), noting whether the five auxiliary turbulence fields were predicted. 5. For interior results, the three free-stream comparisons above. ## Splits The 22 frozen splits in `splits/splits.json` map `"_"` to lists of `case_id`s. **Split placement is a deterministic hash of each case's primary geometry**, so train, validation, and test share no primary body, and adding cases in a future release moves no existing case. (The second body that 3% of cases include is a scaled copy of that case's own primary body, so no geometry is shared across train, validation, and test.) Validation sets are drawn from the training distribution rather than held-out regimes. Of 4,360 planned split cases in `release-v1.2.json`, 3,815 completed (2,816 `mean_stable`), 208 failed mesh or solver checks, and 337 timed out when generation stopped. Uncompleted cases keep their split assignments and are never replaced. Canonical (`mean_stable`) / all-released sample counts per split (canonical test counts for the seven `bank_*` splits and `layout` exclude body-free ducts): | Split | Test regime | Train (canonical / all) | Val (canonical / all) | Test (canonical / all) | |:---|:---|---:|---:|---:| | **Random** | | | | | | `full` | Unstratified 70 / 10 / 20 baseline split | 2,011 / 2,713 | 290 / 386 | 515 / 716 | | `scarce` | Data efficiency: one-sixth of `full` train | 321 / 440 | 290 / 386 | 515 / 716 | | `super_scarce` | Data efficiency: one-thirty-sixth of `full` train | 53 / 74 | 290 / 386 | 515 / 716 | | **Held-out enclosure** | | | | | | `profile_open_opposed` | Open box | 1,943 / 2,775 | 293 / 405 | 580 / 635 | | `profile_ground_effect` | Ground-effect box | 2,011 / 2,837 | 294 / 400 | 511 / 578 | | `profile_confined_opposed` | Confined box | 2,114 / 2,963 | 301 / 418 | 401 / 434 | | `profile_crossflow` | Crossflow box | 2,298 / 2,820 | 345 / 417 | 173 / 578 | | `profile_dual_inlet` | Dual-inlet box | 2,044 / 2,875 | 313 / 424 | 459 / 516 | | `family_opposed_sphere` | Opposed sphere | 2,158 / 3,017 | 302 / 418 | 356 / 380 | | `family_opposed_hemisphere` | Symmetry hemisphere | 2,330 / 2,982 | 352 / 442 | 134 / 391 | | `family_duct` | Internal duct | 2,266 / 3,055 | 348 / 457 | 202 / 303 | | `family_non_box` | Spheres, hemispheres, and ducts (train on boxes) | 1,850 / 2,390 | 274 / 351 | 692 / 1,074 | | **Held-out geometry family** | | | | | | `bank_blobs` | Blobs | 1,662 / 2,156 | 237 / 292 | 872 / 1,367 | | `bank_blender_bluff` | Blender bluff | 2,149 / 2,935 | 331 / 441 | 326 / 439 | | `bank_cars` | Procedural cars | 2,173 / 2,965 | 321 / 434 | 310 / 416 | | `bank_zero_to_cad` | Zero-to-CAD | 2,250 / 3,040 | 332 / 442 | 224 / 333 | | `bank_airframes` | Airframe Foundry | 2,198 / 3,018 | 321 / 430 | 288 / 367 | | `bank_streamlined` | Streamlined | 2,132 / 2,940 | 332 / 441 | 337 / 434 | | `bank_blender_streamlined` | Blender streamlined | 2,148 / 2,938 | 310 / 418 | 346 / 459 | | **Reynolds range and body-wall layout** | | | | | | `reynolds` | Extrapolation: train \\(Re < 10^6\\), test \\(Re \ge 10^6\\) | 1,577 / 2,119 | 238 / 311 | 1,001 / 1,385 | | `reynolds_interior` | Interpolation: \\(10^5 \le Re < 10^6\\) decade held out | 1,935 / 2,614 | 292 / 380 | 589 / 821 | | `layout` | Three-band bodies (train on all-no-slip and two-band) | 1,647 / 2,233 | 239 / 311 | 892 / 1,271 | `full`, `scarce`, and `super_scarce` share identical validation and test sets. Every other split holds out one regime as its test set, so test-set sizes and difficulty vary across splits; compare models on the same split, starting with `full` and using the held-out splits to measure extrapolation. Resolve any canonical split part directly from the top-level metadata files before downloading sample tars: ```python import json from pathlib import Path from huggingface_hub import hf_hub_download repo = "nvidia/AeroZoo" fetch = lambda name: json.loads(Path(hf_hub_download(repo, name, repo_type="dataset")).read_text()) splits = fetch("splits/splits.json") release = fetch("release-v1.2.json") body_free = set(fetch("splits/body-free-ducts.json")["case_ids"]) quality = {c: r.get("solution_quality") for c, r in release["cases"].items()} def canonical(part: str) -> list[str]: split, kind = part.rsplit("_", 1) chosen = [c for c in splits[part] if quality[c] == "mean_stable"] if kind == "test" and (split.startswith("bank_") or split == "layout"): chosen = [c for c in chosen if c not in body_free] return chosen train_ids, test_ids = canonical("bank_cars_train"), canonical("bank_cars_test") ``` An unpacked sample records the same body-free indicator locally: `geometry.body_count == 0` in `case-manifest.json`, and zero faces with `annotation/body_id >= 1` on `no_slip` and `slip`. ## What to know before training - **Time-averaged URANS targets:** Stored fields are time means over an 80-convective-time window (following 15 convective times of spin-up) of unsteady RANS with the Langtry–Menter transition SST closure in OpenFOAM 14. Internal ducts that did not settle in the first window ran one fresh 80-convective-time window. `mean_stable` marks samples whose two 40-convective-time half-window averages agree within 0.02 of \\(U_{\mathrm{ref}}\\) in velocity, 0.05 of \\(q_{\mathrm{ref}}\\) in pressure, and 0.02 times \\(\max(\lvert C_F\rvert,\,0.05)\\) in axial force coefficient. - **High-Reynolds attrition:** Of the 337 planned cases still running when generation stopped, 230 lie in the \\(10^7\\) to \\(10^8\\) Reynolds decade (`release-v1.2.json` records every planned case's status). - **Sign and pressure conventions:** `derived/wall_shear_stress` follows OpenFOAM's convention (traction exerted by the wall on the fluid, giving the minus sign in \\(\mathbf{C}\\)). `gauge_pressure` is in pascals relative to the outlet (0 Pa), and boundary face normals point out of the fluid domain. - **Enclosed volumes:** Compute fluid or body volumes from a sample's boundary triangle meshes (for example via the divergence theorem over `boundaries/*`). Those match the sum of cell volumes across all released samples, including three Zero-to-CAD cases (550, 2273, and 3538) whose upstream CAD surfaces self-intersected before exterior meshing. ## Geometry sources Six of the seven geometry families (plus the EasterEggs mascot meshes) were created in-house at NVIDIA. Two of the 11 airfoil sections used in Blender streamlined wings (Eppler E387 and SD7003-085-88) come from coordinate files in the UIUC Airfoil Coordinates Database (M. S. Selig, UIUC), and the remaining nine sections were evaluated from the analytic NACA equations. The Zero-to-CAD family is a farthest-point sample of 500 parts drawn across three bluffness strata from the 52,408 watertight, single-body parts of Autodesk AI Lab's Zero-to-CAD 100K dataset (`ADSKAILab/Zero-To-CAD-100k`); 320 of them appear in released samples, and the family's other 13 released samples are body-free ducts. That dataset is released under the Apache License 2.0, which applies to those parts. `LICENSE` and `NOTICE` at the top of this repository state the terms, attributions, and modifications, and `zero-to-cad-provenance.csv` maps each Zero-to-CAD sample body to its upstream part and transform. ## License **GOVERNING DOWNLOAD TERMS:** Use of the dataset is governed by the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en), and includes 500 parts selected from Autodesk AI Lab's Zero-to-CAD 100k dataset which is governed by [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). Origin of every input: | Input | Origin | Terms | |---|---|---| | Blobs, Blender bluff, Blender streamlined, Streamlined, Procedural cars, Airframe Foundry geometry families | generated in-house at NVIDIA | the dataset license | | Two of the 11 airfoil sections of the Blender streamlined wings (Eppler E387, SD7003-085-88) | coordinate files from the UIUC Airfoil Coordinates Database (M. S. Selig, UIUC); the other nine were computed from the published NACA equations | the dataset license; the coordinate files are not included (credited in NOTICE) | | Zero-to-CAD parts (500 selected; 320 appear in released samples) | Autodesk AI Lab, Zero-to-CAD 100K | Apache License 2.0; see `NOTICE` and `zero-to-cad-provenance.csv` | | Two mascot meshes (unicorn, jackalope; the EasterEggs family, outside every split, one released sample) | modeled in-house at NVIDIA by Carmelo Gonzales | the dataset license | | All flow solutions, meshes, the stability label and metadata | computed by NVIDIA | the dataset license | ## Verifying a copy `SHA256SUMS` lists the SHA-256 of every data file of the release as stored on NVIDIA's clusters: the top-level files alongside each sample tar unpacked into its shard directory. It was added on 2026-10-16 with no change to any data file. To reconstruct that layout from a download and verify every file: ```bash for t in samples/shard-*/*.tar; do s=$(basename "$(dirname "$t")"); mkdir -p "$s"; tar -xf "$t" -C "$s" done grep -v ' README.md$' SHA256SUMS | sha256sum --quiet -c - ``` The second command prints nothing when every listed file matches (it skips the cluster-tree `README.md`, and extra repository files such as `LICENSE`, `NOTICE`, `zero-to-cad-provenance.csv`, `assets/`, or `splits/body-free-ducts.json` are ignored by `sha256sum -c`). Running `sha256sum SHA256SUMS` prints a single digest covering the entire release: `f63dc064ca4c8dc5e03d6668c4e60efdb197d4b6cd0d965ec3fe83a2ba77bb38`. ## Provenance Generated with OpenFOAM 14 (mesh policy `snappyhexmesh_external_v14` for every case, ducts included, and solver policy `openfoam14-external-transition-sst-mean-co10-w80-duct-two-stage-v10`, artifact schema 5) on NVIDIA's compute clusters (8.1 million CPU core-hours of solver time); population frozen 2026-10-02 and exported 2026-10-08. The `index.json` SHA-256 recorded in `release-v1.2.json` is `c8e9508e8a9d8291…`. ## Citation ```bibtex @misc{sharpe2026aerozoo, title = {{AeroZoo}: A Benchmark of Well-Posed Flow Problems for Foundation Models of Aerodynamics}, author = {Sharpe, Peter and Gonzales, Carmelo and Adams, Corey and Ataei, Mehdi and Choudhry, Sanjay}, year = {2026}, howpublished = {arXiv:2611.17152}, url = {https://arxiv.org/abs/2611.17152} } ``` Corresponding contact: Peter Sharpe (`psharpe@nvidia.com`). ## NVIDIA dataset card ### Dataset Description AeroZoo is a dataset of computed fluid flows for physics-AI research. It holds 3,816 samples. Each sample is one solved flow problem: one body (or none, in some ducts) inside its own enclosure, at its own size, speed and Reynolds number. The Reynolds number ranges from 10³ to 10⁸, which covers laminar and turbulent flow. No two planned samples share the same primary geometry. Each sample stores three things: - the problem that was posed: the boundary surfaces, the conditions set on them, and the fluid's viscosity; - the solution: seven time-averaged flow fields (velocity, pressure and five turbulence-model fields) at the center of every cell of the solver's mesh; - the volume of every cell, used to weight a training loss. All samples were computed by NVIDIA with the solver OpenFOAM 14. The solver ran unsteady Reynolds-averaged Navier–Stokes (URANS) simulations with the Langtry–Menter transition SST turbulence model. Each stored field is a time average over a fixed window, not an instantaneous snapshot. The bodies come from seven geometry families: Blobs, Blender bluff, Blender streamlined, Streamlined, Procedural cars, Airframe Foundry and Zero-to-CAD. They sit in eight enclosure configurations: a rectangular box with five boundary layouts (open, ground-effect, confined, crossflow and dual-inlet), an opposed sphere, a symmetry hemisphere and an internal duct. Each body uses one of three body-wall layouts (all no-slip, two slip/no-slip bands, three bands). The dataset is meant for training and testing machine-learning models that predict fluid flow ("surrogate models"). It ships with 22 fixed train / validation / test splits and two benchmark tasks with defined metrics. This dataset is ready for commercial or non-commercial uses. ### Dataset Owner(s) NVIDIA Corporation. ### Dataset Creation Date 2026-10-08 (v1.2 export). The simulations behind the released samples completed between 2026-09-13 and 2026-10-02, when generation was stopped and the population was frozen. ### Versioning v1.2 (2026-10-08 export). ### Intended Usage AeroZoo is intended for researchers and engineers who build machine-learning models of fluid flow. Intended uses are: - training and evaluating surrogate models of steady or time-averaged incompressible flow; - pretraining such models on many unrelated flows; - measuring how well a model generalizes to unseen geometry families, enclosures, boundary layouts and Reynolds numbers, using the 22 frozen splits; - comparing models on the two benchmark tasks. The interior task predicts the mean fields at every cell center. The boundary task predicts the mean fields on every boundary face and the wall shear stress on no-slip walls. The canonical benchmark uses only `mean_stable` samples. What the dataset does not support: - Forecasting a flow over time. Only time averages are stored. - Compressible, heated, multiphase or non-Newtonian flow; moving or rotating walls; porous media. - Use as measurements of real flows. Each sample solves one turbulence model's equations on one mesh. Each solution is converged at the recipe's placement of the first wall cell, not independently of it: on meshes 1.5 times finer per direction the force changes by a median of 3.7% (at most 19%), and a first cell at y⁺ ≈ 1 changes it by a median of 6.9% (at most 36%) and can move transition. ### Dataset Characterization **Data Collection Method** - Synthetic Every sample was generated by computer simulation; nothing was measured or collected from people. For each case, the generator drew a geometry, its size (2 cm to 20 m), inlet speed (0.25 to 50 m/s) and Reynolds number (10³ to 10⁸) from a seed and a frozen recipe. It then built a mesh with OpenFOAM's `snappyHexMesh` (mesh policy `snappyhexmesh_external_v14`). The solver ran OpenFOAM 14 with the Langtry–Menter transition SST model (solver policy `openfoam14-external-transition-sst-mean-co10-w80-duct-two-stage-v10`). Each solve ran 15 convective times to let the start-up wash out, then averaged every field over the next 80 convective times. An internal duct whose first window did not settle averaged one fresh 80-convective-time window; there was no further extension. (A convective time is the reference length, the body's largest extent, divided by the inlet speed.) The exporter converted each result to a PhysicsNeMo `DomainMesh`, reloaded it and checked it before release. The posed problem of every case was fixed before any mesh existed. Cases that failed a mesh or solver check (208) or were still running when generation stopped (337) are listed in the release manifest and are not replaced. The runs used NVIDIA's compute clusters. The released samples took 8.1 million CPU core-hours of solver time. Geometry origin: six of the seven geometry families were generated in-house at NVIDIA. The seventh, Zero-to-CAD, is a sample of 500 parts from Autodesk AI Lab's Zero-to-CAD 100K, chosen from the 52,408 parts that pass watertightness, single-body and shape checks. Two of the 11 airfoil sections of the Blender streamlined wings (Eppler E387 and SD7003-085-88) come from coordinate files of the UIUC Airfoil Coordinates Database; the other nine were computed from the published NACA equations. The two mascot meshes were modeled in-house at NVIDIA. The dataset contains no personal data. **Labeling Method** - Automated The dataset has one label: the stability label, `mean_stable` or `mean_unstable`. It says whether the time average had settled. The software compares the averages over the two halves of the 80-convective-time window, with three fixed tests: velocity change at most 0.02 of the inlet speed, pressure change at most 0.05 of the reference dynamic pressure, and a change in the body-force coefficient of at most 0.02 times its mean value (or times 0.05, if that is larger). A sample is `mean_stable` if it passes all three. The thresholds were fixed before the studies that tested them and were never adjusted. All three measured changes are stored, so users can apply another rule. No person labeled any sample. ### Dataset Format - **Modality:** 3D physics simulation data: point clouds (interior cell centers with field values) and triangle meshes (boundary surfaces), with scalar metadata. Units are SI throughout. - **Format:** one uncompressed `.tar` file per sample. Each unpacks to a PhysicsNeMo `DomainMesh` (`domain.pdmsh`, a memory-mapped TensorDict tree; artifact schema 5) and small JSON / YAML files. Floating-point tensors are `float32`. Top-level catalog, split and checksum files are JSON and text. - **Structure of one sample (`domain.pdmsh`):** - `interior`: one point per mesh cell, at the cell center. Each point holds 13 `float32` values: 3 coordinates, the seven mean fields (9 components) and the cell volume. There is no interior connectivity. - `boundaries`: four triangle meshes, `inlet`, `outlet`, `no_slip` and `slip`, grouped by boundary condition. They carry the prescribed values, the mean fields on every face, and wall shear stress and y⁺ on `no_slip`. - `global_data`: fluid properties, reference scales, how the mean was defined, identity hashes and quality diagnostics, including the stability label. - **Files beside the samples:** `index.json` (one record per released sample), `release-v1.2.json` (status of every planned case), `splits/cases.json`, `splits/splits.json` (22 named splits), `splits/splits-metadata.json`, `checksums.sha256` and `SHA256SUMS`. ### Dataset Quantification - **Record count:** 3,816 samples: 3,815 of the 4,360 planned cases plus one mascot outside every split (`release-v1.2.json`). Of these, 2,817 are `mean_stable` and 999 `mean_unstable`. - **Feature count:** per interior point, 13 values (3 coordinates, 9 mean-field components, 1 cell volume). Per sample, four boundary meshes and the `global_data` scalars (artifact schema 5). - **Size of the samples:** 23.8 billion interior points in total; median 5.0 million cells per sample, range 0.6 to 24 million. - **Total data storage:** 1.48 TB: 1,480,746,807,800 bytes of sample artifacts, median 347 MB per sample. The Hugging Face repository stores 1,482,788,671,963 bytes in all (Hub API, 2026-10-30). Planned and released cases per geometry family: | Geometry family | Planned cases | Released samples | |---|---:|---:| | Blobs | 1,600 | 1,367 | | Blender bluff | 500 | 439 | | Blender streamlined | 500 | 459 | | Streamlined | 480 | 434 | | Procedural cars | 480 | 416 | | Zero-to-CAD | 400 | 333 | | Airframe Foundry | 400 | 367 | | **Total** | **4,360** | **3,815** | | EasterEggs mascots (outside every split) | 2 | 1 | ### Reference(s) - AeroZoo on Hugging Face: https://huggingface.co/datasets/nvidia/AeroZoo - NVIDIA PhysicsNeMo (the `DomainMesh` format): https://github.com/NVIDIA/physicsnemo - OpenFOAM 14: https://openfoam.org/version/14/ - Zero-to-CAD 100K (source of the Zero-to-CAD geometry family): https://huggingface.co/datasets/ADSKAILab/Zero-To-CAD-100k - Ataei, Askari, Rahimi Malekshan and Jayaraman, "Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data", 2026, https://arxiv.org/abs/2604.24479 - The AeroZoo paper: ### Ethical Considerations NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).