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arXiv Machine Learning By Ryoichiro Agata, Tomohisa Okazaki

Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks

Read the original on arXiv Machine Learning →

arXiv:2605. 07060v3 Announce Type: replace-cross Abstract: 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 more naturally expressed in function space.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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