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

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 computati...

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Bibliographic Details
Published in:arXiv.org 2023-04
Main Authors: Bai, Jinshuai, Jeong, Hyogu, Batuwatta-Gamage, C P, Xiao, Shusheng, Wang, Qingxia, Rathnayaka, C M, Alzubaidi, Laith, Liu, Gui-Rong, Gu, Yuantong
Format: Article
Language:English
Subjects:
Computer networks
Neural networks
Programming languages
Solid mechanics
Online Access:Get full text
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Summary: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. The programs for all the numerical examples presented in this work are available on https://github.com/JinshuaiBai/PINN_Comp_Mech.
ISSN:2331-8422