FuncGenFoil: Airfoil Generation and Editing Model in Function Space

TL;DR

FuncGenFoil introduces a novel function-space generative model for airfoil geometry that combines the benefits of parametric and point-based representations, enabling high-fidelity, controllable, and editable airfoil design.

Contribution

It presents the first function-space model for airfoil generation, improving accuracy and diversity over existing methods.

Findings

74.4% reduction in label error

23.2% increase in diversity

Superior performance on AF-200K dataset

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.