用函数空间生成可编辑的高保真机翼剖面，提升设计精度与多样性。
FuncGenFoil: Airfoil Generation and Editing Model in Function Space
- 在函数空间直接建模机翼轮廓，实现任意分辨率采样与光滑性。
- 在AF-200K数据集上降低74.4%标签误差，提升23.2%生成多样性。
- 适合航空设计、气动优化领域研究人员使用，支持灵活编辑。
飞机制造是工业皇冠上的明珠，而生成具有可控性和可编辑性的高保真机翼剖面几何形状仍是基础挑战。现有深度学习方法通常依赖预定义的参数化表示（如Bézier）或离散点集，在表达能力与分辨率适应性之间存在固有权衡。为此，我们提出FuncGenFoil，一种新型函数空间生成模型，直接将机翼剖面重建为函数曲线。该方法兼具参数函数的任意分辨率采样和光滑性优势，以及基于离散点表示的强大表达能力。实验表明，FuncGenFoil在机翼生成任务中优于现有最先进方法：在AF-200K数据集上实现74.4%的标签误差相对降低，多样性提升23.2%。结果凸显函数空间建模在气动外形优化中的优势，为高保真机翼设计提供强大且灵活的框架。
原文摘要 · Abstract (English)
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.
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