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

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2021年第8期22卷 621-631页

作者： Shivam SHARMA Rajneesh AWASTHI yedlabala sudhir.sastr. Pattabhi Ramaiah BUDARAPU School of Mechanical Sciences Indian Institute of TechnologyBhubaneswar 752050India Department of Aeronautical Engineering Institute of Aeronautical EngineeringHyderabad 500043India

With the explosive growth of computational resources and data generation,deep machine learning has been successfully employed in various *** important and emerging scientific application of deep learning involves solving differential ***,physics-informed neural networks(PINNs)are developed to solve the differential equations associated with a specific scientific *** 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 *** this study,various PINNs are adopted to estimate the stresses in the tablets and the interphase of a single lap *** 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 *** axial stresses are a function of the tablet length,which presents the independent ***,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 *** results obtained using the developed methodology are validated using the results obtained via MAPLE software.

关键词： Physics-informed neural networks(PINNs) Algorithmic differentiation Artificial neural networks Loss function Single lap joint

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