Comparative seismic design optimization of spatial steel dome structures through three recent metaheuristic algorithms

Çarbaş, Serdar; Artar, Musa

doi:10.1007/s11709-021-0784-y

Cited by 10 publications

(1 citation statement)

References 38 publications

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“…Furthermore, the deep learning neural network was used for damage detection and damage classification [29,30]. In addition, the estimation of the optimum design of structural systems was made using metaheuristic algorithms [31,32]. Furthermore, a digital image correlation based structural state and service life were detected using deep learning [33,34].…”

Section: Introductionmentioning

confidence: 99%

Surrogate-Based Physics-Informed Neural Networks for Elliptic Partial Differential Equations

Zhi,

Wu,

et al. 2023

Mathematics

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The purpose of this study is to investigate the role that a deep learning approach could play in computational mechanics. In this paper, a convolutional neural network technique based on modified loss function is proposed as a surrogate of the finite element method (FEM). Several surrogate-based physics-informed neural networks (PINNs) are developed to solve representative boundary value problems based on elliptic partial differential equations (PDEs). According to the authors’ knowledge, the proposed method has been applied for the first time to solve boundary value problems with elliptic partial differential equations as the governing equations. The results of the proposed surrogate-based approach are in good agreement with those of the conventional FEM. It is found that modification of the loss function could improve the prediction accuracy of the neural network. It is demonstrated that to some extent, the deep learning approach could replace the conventional numerical method as a significant surrogate model.

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