June 10, 2024, 4:44 a.m. | Emilia Magnani, Marvin Pf\"ortner, Tobias Weber, Philipp Hennig

stat.ML updates on arXiv.org arxiv.org

arXiv:2406.05072v1 Announce Type: cross
Abstract: Modeling dynamical systems, e.g. in climate and engineering sciences, often necessitates solving partial differential equations. Neural operators are deep neural networks designed to learn nontrivial solution operators of such differential equations from data. As for all statistical models, the predictions of these models are imperfect and exhibit errors. Such errors are particularly difficult to spot in the complex nonlinear behaviour of dynamical systems. We introduce a new framework for approximate Bayesian uncertainty quantification in neural …

abstract arxiv climate cs.lg data differential engineering function gaussian processes learn linearization modeling networks neural networks operators predictions processes solution statistical stat.ml systems type

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