#82 · Primary category： AI Design & Prototyping
DrivAerNet
A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
Project last updated：05/14/26
GitHub Stars
540
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85
Contributors
1
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Other
Why we included this project
DrivAerNet pairs thousands of car body designs with high-fidelity CFD results, so you don't have to run your own OpenFOAM simulations just to get training data. The project covers 8,150 configurations across fastback, notchback, and estateback styles, and it includes code and a leaderboard for training neural surrogates that predict drag in a fraction of the time a full simulation takes. That speed is what makes early shape exploration practical for teams without an HPC cluster. The newer CarBench benchmark adds a way to check how well a surrogate generalizes before you invest in training your own.
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