A framework based on symbolic regression coupled with eXtended Physics-Informed Neural Networks for gray-box learning of equations of motion from data-Reference-Cited by-同舟云学术

A framework based on symbolic regression coupled with eXtended Physics-Informed Neural Networks for gray-box learning of equations of motion from data

Published:2023-10 Issue: Volume:415 Page:116258
ISSN:0045-7825
Container-title:Computer Methods in Applied Mechanics and Engineering
language:en
Short-container-title:Computer Methods in Applied Mechanics and Engineering

Author:

Kiyani Elham,Shukla Khemraj,Karniadakis George Em,Karttunen Mikko^ORCID

Funder

U.S. Department of Energy

Canada Research Chairs

US Department of Energy Office of Science

Western University

Mitacs

Alliance de recherche numérique du Canada

Brown University

Natural Sciences and Engineering Research Council of Canada

Publisher

Elsevier BV

Subject

Computer Science Applications,General Physics and Astronomy,Mechanical Engineering,Mechanics of Materials,Computational Mechanics

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4. Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network;Qu;J. Comput. Phys.,2022

5. Solving and learning nonlinear PDEs with Gaussian processes;Chen;J. Comput. Phys.,2021

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