An introduction to programming Physics-Informed Neural Network-based computational solid mechanics
Abstract
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.
Cite
@article{arxiv.2210.09060,
title = {An introduction to programming Physics-Informed Neural Network-based computational solid mechanics},
author = {Jinshuai Bai and Hyogu Jeong and C. P. Batuwatta-Gamage and Shusheng Xiao and Qingxia Wang and C. M. Rathnayaka and Laith Alzubaidi and Gui-Rong Liu and Yuantong Gu},
journal= {arXiv preprint arXiv:2210.09060},
year = {2023}
}
Comments
32 pages, 20 figures are include in this manuscript