Create a Dataset Adapter

Guide the user through adding a new CFD dataset to the benchmarking workflow by writing a DatasetAdapter subclass.

Reference files to read first

Before starting, read these files for context:

• physicsnemo/cfd/evaluation/datasets/adapter_registry.py — base class and registry
• physicsnemo/cfd/evaluation/datasets/schema.py — CanonicalCase and build_predictions_dict
• physicsnemo/cfd/evaluation/datasets/adapters/drivaerml.py — reference adapter implementation
• workflows/benchmarking/notebooks/adding_a_new_dataset.ipynb — end-to-end tutorial (writes a DrivAerStar adapter: format conversion, field renaming, WSS sign flip, STL creation)

Step 1: Explore the new dataset

Ask the user for the dataset path, then inspect one file. Report not just array names but their component count, dtype, and value range, plus mesh.bounds and any geometry arrays — the decision table below needs all of these:

import numpy as np
import pyvista as pv

mesh = pv.read("<path_to_one_file>")
print(f"Type: {type(mesh).__name__}, Points: {mesh.n_points}, Cells: {mesh.n_cells}")
print(f"Bounds (xmin,xmax,ymin,ymax,zmin,zmax): {mesh.bounds}")
for loc, data in [("cell", mesh.cell_data), ("point", mesh.point_data)]:
    for name in data.keys():
        arr = np.asarray(data[name])
        comps = arr.shape[1] if arr.ndim > 1 else 1
        print(f"  [{loc}] {name}: comps={comps}, dtype={arr.dtype}, "
              f"range=({arr.min():.3g}, {arr.max():.3g})")
# Explicit geometry arrays some datasets ship (DrivAerML has none):
print("Has Normals:", "Normals" in mesh.cell_data or "Normals" in mesh.point_data)
print("Has Area:", "Area" in mesh.cell_data or "Area" in mesh.point_data)

Identify these differences from the canonical schema:

Step 2: Write the adapter class

Subclass DatasetAdapter with these methods:

from pathlib import Path
from physicsnemo.cfd.evaluation.datasets.adapter_registry import DatasetAdapter, register_adapter
from physicsnemo.cfd.evaluation.datasets.schema import CanonicalCase

class MyDatasetAdapter(DatasetAdapter):
    def __init__(self, root: str, **kwargs):
        self._root = Path(root)

    @classmethod
    def inference_domain_from_kwargs(cls, kwargs=None):
        return "surface"  # or "volume"

    def list_cases(self):
        # Return list of case ID strings
        ...

    def load_case(self, case_id: str) -> CanonicalCase:
        # 1. Read the mesh file
        # 2. Build ground_truth dict with canonical keys:
        #    - "pressure": np.float32 array
        #    - "shear_stress": np.float32 array of shape (N, 3)
        #    For volume: "pressure", "velocity" (N,3), "turbulent_viscosity"
        # 3. Return CanonicalCase(case_id, mesh_path, mesh_type, ground_truth, inference_domain)
        ...

Map source arrays to canonical keys

ground_truth must use the framework's canonical keys, but source files rarely use those names. The canonical vocabulary (see schema.py / build_predictions_dict) is:

Build an explicit rename map from the source names you found in Step 1:

RENAME = {"pMean": "pressure", "wallShearStress": "shear_stress"}
ground_truth = {
    canon: np.asarray(mesh.cell_data[src], dtype=np.float32)
    for src, canon in RENAME.items()
}

When names are ambiguous, disambiguate by: component count (a 3-comp field is velocity or shear_stress), dtype/value range, and — decisively — what the model's training data called each field (see "Why conventions must match the training data"). Do not confuse this source→canonical map with the separate canonical→VTK-name map in output.mesh_field_names (Step 4), which controls the written arrays.

Common transformations in load_case

Reading non-PyVista source formats: pv.read handles VTK-family files, but CFD ground truth often ships as CGNS, OpenFOAM cases, Ensight, Tecplot, HDF5/.npz, or CSV point clouds. Only reading changes — the target is still a canonical .vtp/.vtu mesh plus a ground_truth dict:

# meshio covers many formats (CGNS, Ensight, ...); wrap to PyVista:
import meshio, pyvista as pv
mesh = pv.wrap(meshio.read(src_path))

# OpenFOAM case directory:
mesh = pv.OpenFOAMReader(case_foam_file).read()

# Raw arrays (HDF5 / npz / CSV): build the mesh, then attach fields:
cloud = pv.PolyData(points_xyz)          # (N, 3) float array
cloud["pressure"] = p_values             # attach source arrays

Format conversion (legacy .vtk → .vtp):

mesh = pv.read(vtk_path).extract_surface()
mesh.save(vtp_path)

Combining separate WSS scalars into a vector:

wss = np.stack([mesh.cell_data["WSSx"], mesh.cell_data["WSSy"], mesh.cell_data["WSSz"]], axis=1)

Removing explicit Normals/Area (DrivAerML convention):

for key in ["Normals", "Area"]:
    if key in mesh.cell_data:
        del mesh.cell_data[key]

Creating STL from surface mesh (when no STL is shipped):

mesh.extract_surface().triangulate().save(stl_path)

The STL must be named drivaer_{int(case_id)}.stl in the same directory as the VTP for the model wrappers to find it.

Match geometry orientation and scale

Geometry-referenced models (e.g. DoMINO) normalize the mesh/STL coordinates against a fixed bounding box baked into the checkpoint from its training dataset: DoMINO reads cfg.data.bounding_box_surface.min/max (and bounding_box.min/max for volume) and maps every coordinate into that box. If the new dataset's geometry sits in a different frame, origin, or unit scale, it lands in the wrong normalized space — predictions are wrong even when field names and signs are correct.

This only matters when the checkpoint was trained on a specific geometry-referenced dataset (e.g. DrivAerML). For scale/translation-invariant models, or when the model was trained on this same dataset, skip it.

Match three things to the training dataset (DrivAerML reference bounds below, in meters, from the DoMINO config):

• Orientation / axes: same convention — x streamwise (length), y width, z up. Permute or rotate if the new data uses a different up-axis or flipped sign.
• Origin / position: the bounding box should start near the same (x, y, z) minimum, so the geometry falls inside the model's domain box.
• Scale / units: extents must be the same order of magnitude. Millimetre data must be scaled to meters (×0.001).

Check mesh.bounds and transform in load_case before saving the prepared VTP/STL:

b = mesh.bounds  # (xmin, xmax, ymin, ymax, zmin, zmax)
# ~1000x larger extents => mm; scale to meters. A swapped axis range => reorient.
mesh.points *= 0.001
mesh.points += np.array([x_off, y_off, z_off], dtype=np.float32)  # translate to match origin

Caching pattern

Do expensive conversions lazily and cache:

def _prepare_case(self, case_id):
    prepared_path = self._root / "_prepared" / f"{case_id}.vtp"
    if not prepared_path.exists():
        # ... convert and save
    return str(prepared_path)

Step 3: Register and test

register_adapter("my_dataset", MyDatasetAdapter)

adapter = MyDatasetAdapter(root="/path/to/data")
cases = adapter.list_cases()
case = adapter.load_case(cases[0])
assert case.ground_truth is not None
assert "pressure" in case.ground_truth

Step 4: Run inference and benchmark

Build a config and run:

from physicsnemo.cfd.evaluation.config import Config
from physicsnemo.cfd.evaluation.benchmarks.engine import run_benchmark

config = Config.from_dict({
    "run": {"device": "cuda:0", "output_dir": "results"},
    "model": {"name": "<model_name>", "inference_domain": "<surface|volume>", ...},
    "dataset": {"name": "my_dataset", "root": "/path/to/data", "case_ids": cases[:2]},
    "output": {
        "ground_truth_mesh_field_names": {"pressure": "<vtk_gt_name>", "shear_stress": "<vtk_gt_name>"},
        "mesh_field_names": {"pressure": "<vtk_pred_name>", "shear_stress": "<vtk_pred_name>"},
    },
    "metrics": ["l2_pressure", "l2_shear_stress", "drag", "lift"],
    "reports": {"enabled": False},
})
results = run_benchmark(config)

Step 5: Make permanent (optional)

Save the adapter to physicsnemo/cfd/evaluation/datasets/adapters/<name>.py and register in adapters/__init__.py:

from physicsnemo.cfd.evaluation.datasets.adapters.<name> import MyDatasetAdapter
register_adapter("my_dataset", MyDatasetAdapter)

Why conventions must match the training data

The field name mappings, sign conventions, and format conversions in the adapter exist because the model checkpoint was trained on a specific dataset (e.g., DrivAerML) with specific conventions. The adapter bridges the gap between the new dataset's conventions and the training data's conventions — not some abstract standard. If a model is retrained directly on the new dataset, the adapter would not need these transformations. When writing an adapter, always ask: "What conventions did the model's training data use?" and map to those.

Gotchas

• DistributedManager: Model wrappers call DistributedManager.initialize(). In notebooks without torchrun, set env vars first: WORLD_SIZE=1, RANK=0, LOCAL_RANK=0, MASTER_ADDR=localhost, MASTER_PORT=12355.
• STL naming: DoMINO looks for drivaer_{tag}.stl, GeoTransolver looks for drivaer_{tag}_single_solid.stl then *.stl. Both now fall back to any *.stl in the directory.
• VTP vs VTK: Model wrappers use VTK XML readers internally. Legacy .vtk files must be converted to .vtp/.vtu.
• Checkpoint loading: Some wrappers need trusted_torch_load_context() for PyTorch 2.6+ checkpoint compatibility.
• Domain-scoped metrics: l2_pressure resolves to different implementations for surface vs volume based on inference_domain. Use the same metric name for both.
• Geometry frame: geometry-referenced checkpoints (DoMINO) assume the training dataset's coordinate frame and scale. A mm-vs-m or flipped-axis mismatch produces wrong predictions with no error raised. See "Match geometry orientation and scale".