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arXiv · 2502.10712

FuncGenFoil: Airfoil Generation and Editing Model in Function Space

Jinouwen Zhang·Junjie Ren·Qianhong Ma·Jianyu Wu·Aobo Yang·Yan Lu·Lu Chen·Hairun Xie·Jing Wang·Miao Zhang·Wanli Ouyang·Shixiang Tang
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Abstract

Aircraft manufacturing is the jewel in the crown of industry, in which generating high-fidelity airfoil geometries with controllable and editable representations remains a fundamental challenge. Existing deep learning methods, which typically rely on predefined parametric representations (e.g., B\'ezier) or discrete point sets, face an inherent trade-off between expressive power and resolution adaptability. To tackle this challenge, we introduce FuncGenFoil, a novel function-space generative model that directly reconstructs airfoil geometries as function curves. Our method inherits the advantages of arbitrary-resolution sampling and smoothness from parametric functions, as well as the strong expressiveness of discrete point-based representations. Empirical evaluations demonstrate that FuncGenFoil improves upon state-of-the-art methods in airfoil generation, achieving a relative 74.4% reduction in label error and a 23.2% increase in diversity 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.

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Jinouwen Zhang, Junjie Ren, Qianhong Ma, Jianyu Wu, Aobo Yang, Yan Lu, Lu Chen, Hairun Xie, Jing Wang, Miao Zhang, Wanli Ouyang, Shixiang Tang. 2025-02-15. FuncGenFoil: Airfoil Generation and Editing Model in Function Space. https://arxiv.org/abs/2502.10712

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