Kavli Affiliate: Jing Wang
| First 5 Authors: Jinouwen Zhang, Junjie Ren, Aobo Yang, Yan Lu, Lu Chen
| Summary:
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.
| Search Query: ArXiv Query: search_query=au:”Jing Wang”&id_list=&start=0&max_results=3