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Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning

Part of: Partial differential equations, initial value and time-dependent initial-boundary value problems Partial differential equations

Published online by Cambridge University Press:  04 September 2024

Tim De Ryck Open the ORCID record for Tim De Ryck [Opens in a new window]  and
Siddhartha Mishra
Tim De Ryck
Affiliation:
Seminar for Applied Mathematics, ETH Zürich, Rämistrasse 101, 8092 Zürich, Switzerland E-mail: tim.deryck@math.ethz.ch
Siddhartha Mishra
Affiliation:
Seminar for Applied Mathematics & ETH AI Center, ETH Zürich, Rämistrasse 101, 8092 Zürich, Switzerland E-mail: siddhartha.mishra@math.ethz.ch
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Abstract

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Physics-informed neural networks (PINNs) and their variants have been very popular in recent years as algorithms for the numerical simulation of both forward and inverse problems for partial differential equations. This article aims to provide a comprehensive review of currently available results on the numerical analysis of PINNs and related models that constitute the backbone of physics-informed machine learning. We provide a unified framework in which analysis of the various components of the error incurred by PINNs in approximating PDEs can be effectively carried out. We present a detailed review of available results on approximation, generalization and training errors and their behaviour with respect to the type of the PDE and the dimension of the underlying domain. In particular, we elucidate the role of the regularity of the solutions and their stability to perturbations in the error analysis. Numerical results are also presented to illustrate the theory. We identify training errors as a key bottleneck which can adversely affect the overall performance of various models in physics-informed machine learning.

MSC classification

Primary: 65M15: Error bounds
Secondary: 35A35: Theoretical approximation to solutions

Information

Type
Research Article
Information
Acta Numerica , Volume 33 , July 2024 , pp. 633 - 713
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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