A preprocessed, point-cloud version of the DrivAerML high-fidelity CFD dataset, ready for training point-based deep learning surrogates (PointNet, PCT, DGCNN, Graph Neural Operators, etc.) for automotive external aerodynamics.
The original DrivAerML release contains 500 scale-resolving CFD simulations of parametrically morphed DrivAer notchback geometries and ships as 31 TB of raw STL / VTP / VTU / OpenFOAM data. This release distills the surface boundary of… See the full description on the dataset page:
https://huggingface.co/datasets/Jrhoss/Drivaerml_point_clouds.
