# DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics

arXiv: https://arxiv.org/abs/2408.11969 · HTML: https://mlsanity.com/paper/2408.11969/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2024.

## Built on by (most cited first, 30 of 35)

- [AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive Aerodynamics Simulations via Anchored-Branched Universal Physics Transformers](https://mlsanity.com/paper/2502.09692/) (2025)
- [DoMINO: A Decomposable Multi-scale Iterative Neural Operator for Modeling Large Scale Engineering Simulations](https://mlsanity.com/paper/2501.13350/) (2025)
- [GeoTransolver: Learning Physics on Irregular Domains Using Multi-scale Geometry Aware Physics Attention Transformer](https://mlsanity.com/paper/2512.20399/) (2025)
- [X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics Simulation](https://mlsanity.com/paper/2411.17164/) (2024)
- [Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries](https://mlsanity.com/paper/2602.04940/) (2026)
- [CarBench: A Comprehensive Benchmark for Neural Surrogates on High-Fidelity 3D Car Aerodynamics](https://mlsanity.com/paper/2512.07847/) (2025)
- [GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training](https://mlsanity.com/paper/2602.20399/) (2026)
- [FLARE: Fast Low-rank Attention Routing Engine](https://mlsanity.com/paper/2508.12594/) (2025)
- [AB-UPT for Automotive and Aerospace Applications](https://mlsanity.com/paper/2510.15808/) (2025)
- [A Benchmarking Framework for AI models in Automotive Aerodynamics](https://mlsanity.com/paper/2507.10747/) (2025)
- [Point-wise Diffusion Models for Physical Systems with Shape Variations: Application to Spatio-temporal and Large-scale system](https://mlsanity.com/paper/2508.01230/) (2025)
- [A Mixture of Experts Gating Network for Enhanced Surrogate Modeling in External Aerodynamics](https://mlsanity.com/paper/2508.21249/) (2025)
- [Accelerating Transient CFD through Machine Learning-Based Flow Initialization](https://mlsanity.com/paper/2503.15766/) (2025)
- [RETO: A Rotary-Enhanced Transformer Operator for High-Fidelity Prediction of Automotive Aerodynamics](https://mlsanity.com/paper/2605.00062/) (2026)
- [ShardTensor: Domain Parallelism for Scientific Machine Learning](https://mlsanity.com/paper/2605.11111/) (2026)
- [ShapeBench: A Scalable Benchmark and Diagnostic Suite for Standardized Evaluation in Aerodynamic Shape Optimization](https://mlsanity.com/paper/2605.20763/) (2026)
- [The Kuramoto Neural Operator: Learning to Solve PDEs via Coupled Oscillator Dynamics](https://mlsanity.com/paper/2608.10234/) (2026)
- [Learning Nonlinear Responses in PET Bottle Buckling with a Hybrid DeepONet-Transolver Framework](https://mlsanity.com/paper/2509.13520/) (2025)
- [Towards Multi-Fidelity Scaling Laws of Neural Surrogates in CFD](https://mlsanity.com/paper/2511.01830/) (2025)
- [GLOBE: Accurate and Generalizable PDE Surrogates using Domain-Inspired Architectures and Equivariances](https://mlsanity.com/paper/2511.15856/) (2025)
- [WAKESET: A Large-Scale, High-Reynolds Number Flow Dataset for Machine Learning of Turbulent Wake Dynamics](https://mlsanity.com/paper/2602.01379/) (2026)
- [Towards a Foundation-Model Paradigm for Aerodynamic Prediction in Three-dimensional Design](https://mlsanity.com/paper/2604.18062/) (2026)
- [M3: Reframing Training Measures for Discretized Physical Simulations](https://mlsanity.com/paper/2605.08843/) (2026)
- [Physics Transformer: Tailoring Transformer for General PDE Prediction](https://mlsanity.com/paper/2607.24513/) (2026)
- [Large language models for partial differential equation workflows](https://mlsanity.com/paper/2608.03600/) (2026)
- [FLARE++: Low-rank attention with dynamic attention routing](https://mlsanity.com/paper/2608.11519/) (2026)
- [PhysicsBench: A Unified Leaderboard for Generative and Predictive Models in Engineering Design and Simulation](https://mlsanity.com/paper/2608.24056/) (2026)
- [From Numerical Simulators of PDEs to Neural Emulators and Back](https://mlsanity.com/paper/2608.24547/) (2026)
- [FSAN: Flow State Attention Network for Aerodynamic Prediction](https://mlsanity.com/paper/2609.06660/) (2026)
- [Distance-Aware Attention and Wall-Distance Expert Routing for Transformer-Based 3D Flow Prediction](https://mlsanity.com/paper/2609.07222/) (2026)

## Abstract (arXiv)

Machine Learning (ML) has the potential to revolutionise the field of automotive aerodynamics, enabling split-second flow predictions early in the design process. However, the lack of open-source training data for realistic road cars, using high-fidelity CFD methods, represents a barrier to their development. To address this, a high-fidelity open-source (CC-BY-SA) public dataset for automotive aerodynamics has been generated, based on 500 parametrically morphed variants of the widely-used DrivAer notchback generic vehicle. Mesh generation and scale-resolving CFD was executed using consistent and validated automatic workflows representative of the industrial state-of-the-art. Geometries and rich aerodynamic data are published in open-source formats. To our knowledge, this is the first large, public-domain dataset for complex automotive configurations generated using high-fidelity CFD.