SOURCE-LINKED INTELLIGENCE
Self-Augmented Diffusion Guidance for Physics-Informed Generation
Diffusion models can be used to generate spatiotemporal signals of physical phenomena, such as time-series images of fluid dynamics. However, a major limitation of standard diffusion models is that they do not incorporate constraints derived from the underlying physical laws. Consequently, generated samples may appear visually plausible while deviating substantially from the true dynamics. In this study, we propose a simple yet effective physics-informed approach based on diffusion guidance with self-generated data augmentation. The proposed method learns the data distribution conditioned on t
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- arXiv · AI, language, vision and robotics · 2026-08-27T07:44:03.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.