arXiv Machine Learning June 11, 2026 By Handi Zhang, Adrienne M. Propp, Brooks Kinch, Houman Owhadi, Nathaniel Trask

Structure-Preserving Neural Surrogates with Tractable Uncertainty Quantification

Read the original on arXiv Machine Learning →

arXiv:2606. 11650v1 Announce Type: new Abstract: Recent advances in scientific machine learning provide a means of near-real-time solution to partial differential equations (PDEs), but lack the theoretical underpinnings of conventional simulators that support contemporary verification and validation.

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

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