Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning

Physics-informed machine learning (PIML) as a means of solving partial differential equations (PDE) has garnered much attention in the Computational Science and Engineering (CS&E) world. This topic encompasses a broad array of methods and models aimed at solving a single or a collection of PDE problems, called multitask learning.

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Michael Penwarden, Houman Owhadi, Robert M. Kirby (2024). Dataset: Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning. https://doi.org/10.57702/yueh7g6f

DOI retrieved: December 16, 2024

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Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.48550/arXiv.2402.11126
Author Michael Penwarden
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Houman Owhadi
Robert M. Kirby