A deep neural network-based algorithm for solving structural optimization

被引：6

作者：

Dung Nguyen Kien ^{[1
]}

Zhuang, Xiaoying ^{[1
,2
,3
]}

机构：

[1] Tongji Univ, Coll Civil Engn, Dept Geotech Engn, Shanghai 200092, Peoples R China

[2] Leibniz Univ Hannover, Dept Math & Phys, Inst Photon, D-30167 Hannover, Germany

[3] Leibniz Univ Hannover, Hannover Ctr Opt Technol, D-30167 Hannover, Germany

来源：

JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A | 2021年 / 22卷 / 08期

基金：

欧洲研究理事会;

关键词：

Structural optimization; Deep learning; Artificial neural networks; Sensitivity analysis; TU31; TP183; TOPOLOGY OPTIMIZATION; EVOLUTION;

D O I：

10.1631/jzus.A2000380

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

We propose the deep Lagrange method (DLM), which is a new optimization method, in this study. It is based on a deep neural network to solve optimization problems. The method takes the advantage of deep learning artificial neural networks to find the optimal values of the optimization function instead of solving optimization problems by calculating sensitivity analysis. The DLM method is non-linear and could potentially deal with nonlinear optimization problems. Several test cases on sizing optimization and shape optimization are performed, and their results are then compared with analytical and numerical solutions.

引用

页码：609 / 620

页数：12

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