Official WindsorML train, validation and test splits are now available. The release defines eight deterministic benchmark regimes: an approximately 80/10/10 random baseline, three nested data-efficiency subsets, and four out-of-distribution evaluations based on geometry, drag and image-derived wake structure. Download the JSON manifest, read the methodology, or browse the complete reproducibility files on Hugging Face. Partition sizes, provenance and a usage example are included in the WindsorML dataset record.
Official AhmedML train, validation and test splits are now available. The release defines eight deterministic benchmark regimes: a random baseline, three nested data-efficiency subsets, and four out-of-distribution evaluations based on geometry, drag and image-derived wake structure. Download the JSON manifest, read the methodology, or browse the complete reproducibility files on Hugging Face. Partition sizes and a usage example are included in the AhmedML dataset record.
Official DrivAerML train, validation and test splits are now available. The release provides eight deterministic benchmark regimes for a random public baseline, nested data-efficiency studies, geometry extrapolation, drag-regime extrapolation and rear-separation evaluation. Download the JSON manifest, read the methodology, or browse the complete reproducibility files on Hugging Face. The partition sizes and usage example are documented in the DrivAerML dataset record.
“WindsorML has been accepted at NeurIPS 2024! The preprint will be updated to reflect the latest version submitted to the conference”
“DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics” paper is now available on arxiv
“WindsorML: High-Fidelity Computational Fluid Dynamics Dataset for Automotive Aerodynamics” paper is now available on arxiv
“AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics” paper is now available on arxiv