Aircraft manufacturing is the jewel in the crown of industry, among which generating high-fidelity airfoil geometries with controllable and editable representations remains a fundamental challenge. While existing deep-learning-based methods rely on predefined parametric function families, e.g., Bézier curves and discrete point-based representations, they suffer from inherent trade-offs between expressiveness and resolution flexibility. To tackle this challenge, we introduce FuncGenFoil, a novel function-space generative model that directly learns functional airfoil geometries. Our method inherits both the advantages of arbitrary resolution sampling and the smoothness of parametric functions, as well as the strong expressiveness of discrete point-based functions. Empirical evaluations on the AFBench dataset demonstrate that FuncGenFoil improves upon state-of-the-art methods in airfoil generation by achieving a relative -74.4 label error reduction and +23.2 diversity increase on the AF-200K dataset. Our results highlight the advantages of function-space modeling for aerodynamic shape optimization, offering a powerful and flexible framework for high-fidelity airfoil design. Our code will be released.
View on arXiv@article{zhang2025_2502.10712,
title={ FuncGenFoil: Airfoil Generation and Editing Model in Function Space },
author={ Jinouwen Zhang and Junjie Ren and Aobo Yang and Yan Lu and Lu Chen and Hairun Xie and Jing Wang and Miao Zhang and Wanli Ouyang and Shixiang Tang },
journal={arXiv preprint arXiv:2502.10712},
year={ 2025 }
}