FuncGenFoil: Airfoil Generation and Editing Model in Function Space
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914196189347840 |
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| author | Zhang, Jinouwen Ren, Junjie Ma, Qianhong Wu, Jianyu Yang, Aobo Lu, Yan Chen, Lu Xie, Hairun Wang, Jing Zhang, Miao Ouyang, Wanli Tang, Shixiang |
| author_facet | Zhang, Jinouwen Ren, Junjie Ma, Qianhong Wu, Jianyu Yang, Aobo Lu, Yan Chen, Lu Xie, Hairun Wang, Jing Zhang, Miao Ouyang, Wanli Tang, Shixiang |
| contents | 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) 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10712 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FuncGenFoil: Airfoil Generation and Editing Model in Function Space Zhang, Jinouwen Ren, Junjie Ma, Qianhong Wu, Jianyu Yang, Aobo Lu, Yan Chen, Lu Xie, Hairun Wang, Jing Zhang, Miao Ouyang, Wanli Tang, Shixiang Machine Learning Artificial Intelligence 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) 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. |
| title | FuncGenFoil: Airfoil Generation and Editing Model in Function Space |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.10712 |