mlsanityData to 7 Oct 2026

AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions

arXiv 2212.07564 ↗First posted 15 Dec 2022cs.LG cs.CV physics.comp-phcited by 77

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Abstract (arXiv)

Surrogate models are necessary to optimize meaningful quantities in physical dynamics as their recursive numerical resolutions are often prohibitively expensive. It is mainly the case for fluid dynamics and the resolution of Navier-Stokes equations. However, despite the fast-growing field of data-driven models for physical systems, reference datasets representing real-world phenomena are lacking. In this work, we develop AirfRANS, a dataset for studying the two-dimensional incompressible steady-state Reynolds-Averaged Navier-Stokes equations over airfoils at a subsonic regime and for different angles of attacks. We also introduce metrics on the stress forces at the surface of geometries and visualization of boundary layers to assess the capabilities of models to accurately predict the meaningful information of the problem. Finally, we propose deep learning baselines on four machine learning tasks to study AirfRANS under different constraints for generalization considerations: big and scarce data regime, Reynolds number, and angle of attack extrapolation.

Key facts. Cited by 77 papers in the map (data through 7 Oct 2026). Builds on 37 of its 71 references that are in the map. First posted on arXiv 15 Dec 2022.

Builds on

37 of 71 references.

References · 37

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 12; 2024: 18; 2025: 23; 2026: 24.

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  2. 2024The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learningcited by 134
  3. 2024DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarkscited by 74
  4. 2023From Zero to Turbulence: Generative Modeling for 3D Flow Simulationcited by 46
  5. 2023Group Equivariant Fourier Neural Operators for Partial Differential Equationscited by 42
  6. 2023Geometric Clifford Algebra Networkscited by 37
  7. 2024DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Predictioncited by 33
  8. 2024FlowBench: A Large Scale Benchmark for Flow Simulation over Complex Geometriescited by 18
  9. 2023MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variabilitycited by 15
  10. 2023BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learningcited by 14
  11. 2024WindsorML: High-Fidelity Computational Fluid Dynamics Dataset For Automotive Aerodynamicscited by 14
  12. 2024AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamicscited by 13
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  3. 2025Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulationscited by 10
  4. 2023Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific MLcited by 8
  5. 2024NeurIPS 2024 ML4CFD Competition: Harnessing Machine Learning for Computational Fluid Dynamics in Airfoil Designcited by 8
  6. 2025PartSDF: Part-Based Implicit Neural Representation for Composite 3D Shape Parametrization and Optimizationcited by 8
  7. 2024Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernelscited by 7
  8. 2025SIMSHIFT: A Benchmark for Adapting Neural Surrogates to Distribution Shiftscited by 6
  9. 2025DrivAerStar: An Industrial-Grade CFD Dataset for Vehicle Aerodynamic Optimizationcited by 6
  10. 2023Training Deep Surrogate Models with Large Scale Online Learningcited by 5
  11. 2024Reference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformationscited by 5
  12. 2025Geometry aware inference of steady state PDEs using Equivariant Neural Fields representationscited by 5
  13. 2025Going with the Speed of Sound: Pushing Neural Surrogates into Highly-turbulent Transonic Regimescited by 5
  14. 2023INFINITY: Neural Field Modeling for Reynolds-Averaged Navier-Stokes Equationscited by 4
  15. 2023Learning to Predict Structural Vibrationscited by 4
  16. 2026Simple yet Effective: Low-Rank Spatial Attention for Neural Operatorscited by 4
  17. 2023Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD Simulationcited by 3
  18. 2024ML4PhySim : Machine Learning for Physical Simulations Challenge (The airfoil design)cited by 3
  19. 2025AutoHood3D: A Multi-Modal Benchmark for Automotive Hood Design and Fluid-Structure Interactioncited by 3
  20. 2025A Kernel-based Resource-efficient Neural Surrogate for Multi-fidelity Prediction of Aerodynamic Fieldcited by 3
  21. 2026ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learningcited by 3
  22. 2026GIST: Gauge-Invariant Spectral Transformers for Scalable Graph Neural Operatorscited by 3
  23. 2026Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulencecited by 3
  24. 2024Sampling-based Distributed Training with Message Passing Neural Networkcited by 2
  25. 2024EngineBench: Flow Reconstruction in the Transparent Combustion Chamber III Optical Enginecited by 2
  26. 2024A Geometry-Aware Message Passing Neural Network for Modeling Aerodynamics over Airfoilscited by 2
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  28. 2025AneumoBench: A Source-Linked Benchmark for Synthetic-Geometry Transfer in Aneurysm CFDcited by 2
  29. 2025A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modelingcited by 2
  30. 2025GFocal: A Global-Focal Neural Operator for Solving PDEs on Arbitrary Geometriescited by 2
  31. 2026Do Diffusion Models Dream of Electric Planes? Discrete and Continuous Simulation-Based Inference for Aircraft Designcited by 2
  32. 2023Packed-Ensemble Surrogate Models for Fluid Flow Estimation Arround Airfoil Geometriescited by 1
  33. 2024Physics-embedded Fourier Neural Network for Partial Differential Equationscited by 1
  34. 2024Implicit Neural Representation For Accurate CFD Flow Field Predictioncited by 1
  35. 2025Towards scientific machine learning for granular material simulations -- challenges and opportunitiescited by 1
  36. 2025Physics-informed, boundary-constrained Gaussian process regression for the reconstruction of fluid flow fieldscited by 1
  37. 2025From Cheap Geometry to Expensive Physics: Elevating Neural Operators via Latent Shape Pretrainingcited by 1
  38. 2026Adapting Automotive Aerodynamics Surrogates to New Vehicle Families via Transfer Learningcited by 1

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