DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Graph-Based Drag Prediction

The dataset

The dataset that started the DrivAerNet ecosystem

DrivAerNet introduced a large-scale, high-fidelity CFD dataset of 4,000 industry-standard car designs, each generated from a fully parametric model with 26 geometric parameters that completely describe the shape. It established the foundation for data-driven aerodynamic design at a scale the field previously lacked — and earned an ASME Papers of Distinction award at IDETC-CIE 2024, with an extended version published in the Journal of Mechanical Design.

Parametric design variation with corresponding aerodynamic response.

Contributions

What the paper delivers

Parametric, high-fidelity CFD at scale

Thousands of simulated DrivAer-family variants give ML models the breadth and fidelity needed to learn real aerodynamic structure instead of memorizing a handful of shapes.

4,000 designs · 26 parameters

RegDGCNN drag surrogate

A dynamic graph convolutional network that regresses drag directly from 3D geometry — no parametric encoding, no meshing pipeline — enabling near-instant evaluation during design exploration.

Open-sourced training pipeline

Open benchmark protocol

Public train/validation/test splits and drag annotations turned the dataset into a reproducible benchmark adopted by the surrogate-modeling community.

Basis for DrivAerNet++ & CarBench

Impact

From one paper to an ecosystem

DrivAerNet became the seed of a research line: DrivAerNet++ (NeurIPS 2024) scaled it to 8,150 multimodal designs, CarBench turned it into a community leaderboard, and integrations followed in NVIDIA PhysicsNeMo and Baidu PaddleScience.

Reference

Citation

@article{elrefaie2025drivaernet,
  title     = {DrivAerNet: A Parametric Car Dataset for Data-Driven
               Aerodynamic Design and Prediction},
  author    = {Elrefaie, Mohamed and Dai, Angela and Ahmed, Faez},
  journal   = {Journal of Mechanical Design},
  volume    = {147},
  number    = {4},
  year      = {2025},
  publisher = {American Society of Mechanical Engineers}
}

@proceedings{10.1115/DETC2024-143593,
  author = {Elrefaie, Mohamed and Dai, Angela and Ahmed, Faez},
  title  = {DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic
            Design and Graph-Based Drag Prediction},
  series = {IDETC-CIE},
  pages  = {V03AT03A019},
  year   = {2024},
  doi    = {10.1115/DETC2024-143593}
}

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