基本信息
- 来源: arxiv
- 原始来源: https://arxiv.org/abs/2601.23262v1
- 作者: Andrew Millard, Fredrik Lindsten, Zheng Zhao
- 分类: cs.LG
- 论文时间: 2026-01-30T18:30:24Z
- 论文 PDF: https://arxiv.org/pdf/2601.23262v1.pdf
来源摘要/节选
We introduce a guided stochastic sampling method that augments sampling from diffusion models with physics-based guidance derived from partial differential equation (PDE) residuals and observational constraints, ensuring generated samples remain physically admissible. We embed this sampling procedure within a new Sequential Monte Carlo (SMC) framework, yielding a scalable generative PDE solver. Across multiple benchmark PDE systems as well as multiphysics and interacting PDE systems, our method produces solution fields with lower numerical error than existing state-of-the-art generative methods.
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