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: 3944
Citations: Bibtex RefMan EndNote GB/T7714
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.
@article{title="Physics-informed neural networks for estimating stress transfer mechanics in single lap joints",
author="Shivam Sharma, Rajneesh Awasthi, Yedlabala Sudhir Sastry, Pattabhi Ramaiah Budarapu",
journal="Journal of Zhejiang University Science A",
volume="22",
number="8",
pages="621-631",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2000403"
}
%0 Journal Article
%T Physics-informed neural networks for estimating stress transfer mechanics in single lap joints
%A Shivam Sharma
%A Rajneesh Awasthi
%A Yedlabala Sudhir Sastry
%A Pattabhi Ramaiah Budarapu
%J Journal of Zhejiang University SCIENCE A
%V 22
%N 8
%P 621-631
%@ 1673-565X
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2000403
TY - JOUR
T1 - Physics-informed neural networks for estimating stress transfer mechanics in single lap joints
A1 - Shivam Sharma
A1 - Rajneesh Awasthi
A1 - Yedlabala Sudhir Sastry
A1 - Pattabhi Ramaiah Budarapu
J0 - Journal of Zhejiang University Science A
VL - 22
IS - 8
SP - 621
EP - 631
%@ 1673-565X
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2000403
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.
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