Physics-guided generative design with gradient-based source-space optimization on flow matching
File(s)
Published version
Author(s)
Yang, Aobo
Wei, Zhen
Liem, Rhea P
Fua, Pascal
Type
Journal Article
Abstract
Generative design can rapidly propose diverse, constraint-satisfying candidates for real-time analysis and closed-loop optimization under expensive physics evaluations. However, reliable generation requires effective incorporation of physical constraints by means of physics-guided generation. In many state-of-the-art physics-guided flow-based methods, physical optimization cannot fully converge within the generative time schedule, leading to limited constraint-satisfaction accuracy in the generated samples. We refer to this issue as the asynchronicity. This phenomenon arises when an energy-based objective is used as the physical loss in denoising generative paradigms, such as diffusion models and flow matching models. To address it, we propose Dflow-SUR, a new physics-guided flow matching framework that performs surrogate-based, gradient-driven optimization directly in the initial source space used to initialize the flow inference. Compared with existing physics-guided schemes, Dflow-SUR improves physical-loss minimization by four to six orders of magnitude, yielding samples that better satisfy physical constraints and, for 3D wing design, produce smoother spanwise pressure distributions. Moreover, Dflow-SUR reduces wall-clock time by 74% on a 2D airfoil case and improves the mean lift-to-drag ratio by 11.8% over Latin hypercube sampling on a 3D wing case. Overall, Dflow-SUR offers three practical advantages: superior guidance controllability, reduced sensitivity to surrogate uncertainty induced by noisy samples, and robustness to hyperparameter choices. The efficiency and robustness demonstrated by the results presented in this paper demonstrate that Dflow-SUR can enable accurate, efficient, and physically consistent generative design, which can effectively support design and analysis processes that require expeditious evaluations of a physical system, such as optimization and digital twins.
Date Issued
2026-05-16
Date Acceptance
2026-03-21
Citation
Structural and Multidisciplinary Optimization, 2026, 69 (5)
ISSN
1615-147X
Publisher
Springer Science and Business Media LLC
Journal / Book Title
Structural and Multidisciplinary Optimization
Volume
69
Issue
5
Copyright Statement
© The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Publication Status
Published
Article Number
136
Date Publish Online
2026-05-16