FuncGenFoil: Airfoil Generation and Editing Model in Function Space

2025-02-15

AI Summary

High-fidelity airfoil generation and editing face an inherent trade-off between expressive power and resolution flexibility. To address this, we propose the first end-to-end generative paradigm operating directly in function space, which explicitly models the implicit function mapping of airfoil contours—unifying the analytical tractability of parametric methods with the geometric flexibility of point-cloud representations. Our approach enables arbitrary-resolution sampling, guarantees mathematical smoothness, supports controllable generation, and facilitates interactive geometric editing. Evaluated on the AFBench benchmark, our method reduces label error by 74.4% and improves diversity by 23.2% over state-of-the-art approaches. It achieves high-fidelity, multi-resolution-consistent, and editable airfoil synthesis, establishing a novel paradigm for aerodynamic shape optimization.

Technology Category

• Natural Language Processing: Generation
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Aircraft manufacturing is the jewel in the crown of industry, among which generating high-fidelity airfoil geometries with controllable and editable representations remains a fundamental challenge. While existing deep-learning-based methods rely on predefined parametric function families, e.g., B'ezier curves and discrete point-based representations, they suffer from inherent trade-offs between expressiveness and resolution flexibility. To tackle this challenge, we introduce FuncGenFoil, a novel function-space generative model that directly learns functional airfoil geometries. Our method inherits both the advantages of arbitrary resolution sampling and the smoothness of parametric functions, as well as the strong expressiveness of discrete point-based functions. Empirical evaluations on the AFBench dataset demonstrate that FuncGenFoil improves upon state-of-the-art methods in airfoil generation by achieving a relative -74.4 label error reduction and +23.2 diversity increase 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. Our code will be released.

Problem

• Generates high-fidelity airfoil geometries
• Enables controllable and editable representations
• Improves expressiveness and resolution flexibility

Innovation

• Function-space generative model
• Arbitrary resolution sampling
• High-fidelity airfoil design