Full Text:   <2029>

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CLC number: TP183

On-line Access: 2021-08-20

Received: 2020-09-08

Revision Accepted: 2021-02-24

Crosschecked: 2021-07-20

Cited: 0

Clicked: 2700

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Shivam Sharma

https://orcid.org/0000-0003-4148-1624

Pattabhi Ramaiah Budarapu

https://orcid.org/0000-0001-9884-1622

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Journal of Zhejiang University SCIENCE A 2021 Vol.22 No.8 P.621-631

http://doi.org/10.1631/jzus.A2000403


Physics-informed neural networks for estimating stress transfer mechanics in single lap joints


Author(s):  Shivam Sharma, Rajneesh Awasthi, Yedlabala Sudhir Sastry, Pattabhi Ramaiah Budarapu

Affiliation(s):  School of Mechanical Sciences, Indian Institute of Technology, Bhubaneswar 752050, India; more

Corresponding email(s):   pattabhi@iitbbs.ac.in

Key Words:  Physics-informed neural networks (PINNs), Algorithmic differentiation, Artificial neural networks, Loss function, Single lap joint


Shivam Sharma, Rajneesh Awasthi, Yedlabala Sudhir Sastry, Pattabhi Ramaiah Budarapu. Physics-informed neural networks for estimating stress transfer mechanics in single lap joints[J]. Journal of Zhejiang University Science A, 2021, 22(8): 621-631.

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publisher="Zhejiang University Press & Springer",
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%T Physics-informed neural networks for estimating stress transfer mechanics in single lap joints
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Abstract: 
With the explosive growth of computational resources and data generation, deep machine learning has been successfully employed in various applications. One important and emerging scientific application of deep learning involves solving differential equations. Here, physics-informed neural networks (PINNs) are developed to solve the differential equations associated with a specific scientific problem. As such, algorithms for solving the differential equations by embedding their initial and boundary conditions in the cost function of the artificial neural networks using algorithmic differentiation must also be developed. In this study, various PINNs are adopted to estimate the stresses in the tablets and the interphase of a single lap joint. The proposed model is represented by two fourth-order non-homogeneous coupled partial differential equations, with the axial stresses in the upper and lower tablets adopted as the dependent variables. The axial stresses are a function of the tablet length, which presents the independent variable. Therefore, the axial stresses in the tablets are estimated by solving the coupled partial differential equations when subjected to the boundary conditions, whereas the remaining stress components are expressed in terms of axial stresses. The results obtained using the developed methodology are validated using the results obtained via MAPLE software.

用于评估单搭接接头应力传递的物理神经网络

目的:开发物理神经网络,研究单搭接接头的应力传递机理.
创新点:1. 创建了一种新的基于物理神经网络(PINN)的深度机器学习(DML)方法来求解两个非齐次耦合四阶偏微分方程.2. 通过将开发的方法和闭合解(由MAPLE软件获得)进行对比,验证了结果的可靠性.
方法:1. 通过包含1个输入层、2到3个隐藏层和1个输出层的人工神经网络(ANN)实现本文提出的基于PINN的DML方法.2. 将边界和初始条件以及搭接接头组成部分的材料特性提供给输入层,在隐藏层中计算损失函数,并从输出层提取满足边界条件的σ1σ3应力值.
结论:1. 通过基于DML框架的PINN方法研究单个搭接接头的力学,以及对受边界条件影响的耦合四阶非齐次偏微分方程的求解,所提方法可被扩展到多基板及其相间的各种应力分量的估计.2. 通过用所提方法估计界面剪切应力并将其与精确解对比发现,基于DML的方法获得的结果可有效表征物理行为.

关键词:物理信息神经网络;算法微分;人工神经网络;损失函数;单搭接接头

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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