An introduction to programming Physics-Informed Neural Network-based computational solid mechanics

Jinshuai Bai; Hyogu Jeong; Chanaka Batuwatta Gamage; Shusheng  Xiao; Qingxia  Wang; Charith Rathnayaka; Laith  Alzubaidi; Gui-Rong  Liu; Yuantong Gu

doi:10.48550/arXiv.2210.09060

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An introduction to programming Physics-Informed Neural Network-based computational solid mechanics

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An introduction to programming Physics-Informed Neural Network-based computational solid mechanics

Jinshuai Bai, Hyogu Jeong, Chanaka Batuwatta Gamage, Shusheng Xiao, Qingxia Wang, Charith Rathnayaka, Laith Alzubaidi, Gui-Rong Liu and Yuantong Gu

arXiv , Vol.17 October 2022

Cornell University

2022

DOI: https://doi.org/10.48550/arXiv.2210.09060

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Abstract

Physics-informed neural network

computational mechanics

deep learning

Physics informed neural network (PINN) has recently gained increasing interest in computational mechanics. In this work, we present a detailed introduction to programming PINN based computational solid mechanics. Besides, two prevailingly used physics informed loss functions for PINN based computational solid mechanics are summarised. Moreover, numerical examples ranging from 1D to 3D solid problems are presented to show the performance of PINN based computational solid mechanics. The programs are built via Python coding language and TensorFlow library with step by step explanations. It is worth highlighting that PINN based computational mechanics is easy to implement and can be extended for more challenging applications. This work aims to help the researchers who are interested in the PINN based solid mechanics solver to have a clear insight into this emerging area.

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Title: An introduction to programming Physics-Informed Neural Network-based computational solid mechanics
Authors: Jinshuai Bai (Author) - Queensland University of Technology
Hyogu Jeong (Author) - Queensland University of Technology
Chanaka Batuwatta Gamage (Author) - Queensland University of Technology
Shusheng Xiao (Author) - Queensland University of Technology
Qingxia Wang (Author) - The University of Queensland
Charith Rathnayaka (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Laith Alzubaidi (Author) - Queensland University of Technology
Gui-Rong Liu (Author) - University of Cincinnati
Yuantong Gu (Corresponding Author) - Queensland University of Technology
Publication details: arXiv , Vol.17 October 2022
Publisher: Cornell University
Date published: 2022
DOI: 10.48550/arXiv.2210.09060
ISSN: 2331-8422
Organisation Unit: University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering
Language: English
Record Identifier: 99681396402621
Output Type: Preprint

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