# HiLiftAeroML: A High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics

arXiv: https://arxiv.org/abs/2605.19565 · HTML: https://mlsanity.com/paper/2605.19565/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 19 May 2026.

## Builds on (most cited first, 11 of 11)

- [Transolver: A Fast Transformer Solver for PDEs on General Geometries](https://mlsanity.com/paper/2402.02366/) (2024)
- [Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary Domains](https://mlsanity.com/paper/2505.18781/) (2025)
- [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)
- [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)
- [AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics](https://mlsanity.com/paper/2407.20801/) (2024)
- [Going with the Speed of Sound: Pushing Neural Surrogates into Highly-turbulent Transonic Regimes](https://mlsanity.com/paper/2511.21474/) (2025)
- [AB-UPT for Automotive and Aerospace Applications](https://mlsanity.com/paper/2510.15808/) (2025)
- [AeroJEPA: Learning Semantic Latent Representations for Scalable 3D Aerodynamic Field Modeling](https://mlsanity.com/paper/2605.05586/) (2026)
- [SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design](https://mlsanity.com/paper/2512.14397/) (2025)
- [BlendedNet++: A dataset and benchmark for field-resolved aerodynamics and inverse design of blended wing body aircraft](https://mlsanity.com/paper/2512.03280/) (2025)

## Built on by (most cited first, 1 of 1)

- [Cross-Domain Pretraining for Steady-State Neural CFD Surrogates](https://mlsanity.com/paper/2610.10398/) (2026)

## Abstract (arXiv)

HiLiftAeroML is, to our knowledge, the first open high-fidelity computational fluid dynamics dataset dedicated to high-lift aircraft aerodynamics. It contains 1,800 simulations spanning 180 variants of the NASA Common Research Model high-lift configuration and ten angles of attack from 4^circ to 22^circ. Each case was generated with a GPU-accelerated explicit wall-modeled large-eddy simulation approach on solution-adapted grids of 300--500 million cells, covering attached, separated, and post-stall flow conditions. Comparisons with wind-tunnel measurements for reference landing configurations show good agreement in integrated loads and sectional pressures, with grid adaptation substantially improving drag and pitching-moment predictions. The CC-BY-4.0 release includes geometries, time-averaged surface and volume fields, integrated loads, validation material, and deterministic benchmark splits. Initial GeoTransolver and Transolver baselines, evaluated on the complete native surface and volume support of every held-out case rather than on a sampled subset, reconstruct the fields well for several interpolation and held-out-geometry tests, while high-angle separated flow, limited-data training, and out-of-distribution flow-regime shifts remain substantially harder. The dataset and baselines provide a common resource for developing and assessing data-driven models for realistic high-lift aerodynamics.