FuncGenFoil: Airfoil Generation and Editing Model in Function Space

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. , Bézier curves) 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.

Authors

• Jinouwen Zhang
• Junjie Ren
• Ma Qianhong
• Jianyu Wu
• Aobo Yang
• Yan Lu Zhongnan University of Economics and Law
• Lu Chen Shanghai Jiao Tong University Shanghai Jiao Tong University (Wuxi) Blockchain Advanced Research Center
• Hairun Xie
• Jing Wang
• Miao Zhang
• Wanli Ouyang Shanghai Artificial Intelligence Laboratory
• Shixiang Tang The Chinese University of Hong Kong

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