AI RESEARCH
Functional-prior-based Bayesian PDE-constrained inversion using PINNs
arXiv CS.LG
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ArXi:2605.07060v1 Announce Type: cross Physics-informed neural networks (PINNs) provide a mesh-free framework for solving PDE-constrained inverse problems, but their extension to Bayesian inversion still faces a fundamental difficulty: prior distributions are typically defined in the weight space of neural networks, whereas physically meaningful prior assumptions are naturally expressed in function space. In this study, we