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Computer Science > Computational Engineering, Finance, and Science

arXiv:2210.09060 (cs)
[Submitted on 17 Oct 2022 (v1), last revised 10 Apr 2023 (this version, v4)]

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

Authors:Jinshuai Bai, Hyogu Jeong, C. P. Batuwatta-Gamage, Shusheng Xiao, Qingxia Wang, C.M. Rathnayaka, Laith Alzubaidi, Gui-Rong Liu, Yuantong Gu
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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 this https URL.
Comments: 32 pages, 20 figures are include in this manuscript
Subjects: Computational Engineering, Finance, and Science (cs.CE)
Cite as: arXiv:2210.09060 [cs.CE]
  (or arXiv:2210.09060v4 [cs.CE] for this version)
  https://doi.org/10.48550/arXiv.2210.09060

Submission history

From: Jinshuai Bai [view email]
[v1] Mon, 17 Oct 2022 13:01:37 UTC (1,499 KB)
[v2] Fri, 11 Nov 2022 02:06:42 UTC (1,686 KB)
[v3] Wed, 7 Dec 2022 01:43:22 UTC (1,509 KB)
[v4] Mon, 10 Apr 2023 08:46:02 UTC (2,077 KB)
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