June 10, 2024, 4:45 a.m. | Tim Weiland, Marvin Pf\"ortner, Philipp Hennig

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.05020v1 Announce Type: new
Abstract: Modeling real-world problems with partial differential equations (PDEs) is a prominent topic in scientific machine learning. Classic solvers for this task continue to play a central role, e.g. to generate training data for deep learning analogues. Any such numerical solution is subject to multiple sources of uncertainty, both from limited computational resources and limited data (including unknown parameters). Gaussian process analogues to classic PDE simulation methods have recently emerged as a framework to construct fully …

abstract arxiv cs.lg cs.na data deep learning differential generate information machine machine learning math.na modeling multiple numerical real-world problems role scaling scaling up scientific solution training training data type world

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