Abstract:Reconstructing PDE solutions from sparse observations is a core challenge in scientific computing. We present FM4PDE, a flow-matching generative framework that learns the joint distribution of PDE coefficients (or initial states) and solutions (or final states), enabling both forward simulation and inverse recovery with limited paired data. At inference, sampling is guided by a composite loss that enforces agreement with sparse measurements and reduces the PDE residual; we support deterministic, stochastic, and hybrid samplers. We provide error guarantees for these guided procedures. For the deterministic optimizer, a coercivity condition ensures trajectory boundedness and a phase-wise contraction yields logarithmic complexity in the target accuracy. For the stochastic sampler, we introduce adaptive guidance and assume dissipativity of the velocity field to obtain uniform moment bounds independent of the noise-floor parameter. This leads to polynomial-time error bounds, and a matching lower bound shows constant guidance induces an unavoidable positive bias, motivating adaptivity. A hybrid deterministic-stochastic analysis is also provided. Experiments on static and time-dependent benchmark PDEs demonstrate competitive accuracy and faster inference than diffusion-based generative models.
| Comments: | 50 pages, 8 figures, 4 tables |
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG) |
| MSC classes: | 65M32, 68T07, 35R30 |
| Cite as: | arXiv:2605.25509 [stat.ML] |
| (or arXiv:2605.25509v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2605.25509 arXiv-issued DOI via DataCite (pending registration) |
From: Jin Zhao [view email]
[v1]
Mon, 25 May 2026 07:14:11 UTC (2,073 KB)
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