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Suppl. Mater.: 

CLC number: TU31; TP183

On-line Access: 2021-08-20

Received: 2020-08-23

Revision Accepted: 2021-01-04

Crosschecked: 2021-07-20

Cited: 0

Clicked: 3125

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Dung Nguyen Kien

https://orcid.org/0000-0002-6406-1158

Xiaoying Zhuang

https://orcid.org/0000-0001-6562-2618

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Journal of Zhejiang University SCIENCE A 2021 Vol.22 No.8 P.609-620

http://doi.org/10.1631/jzus.A2000380


A deep neural network-based algorithm for solving structural optimization


Author(s):  Dung Nguyen Kien, Xiaoying Zhuang

Affiliation(s):  Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China; more

Corresponding email(s):   xiaoying.zhuang@gmail.com, zhuang@iop.uni-hannover.de

Key Words:  Structural optimization, Deep learning, Artificial neural networks, Sensitivity analysis


Dung Nguyen Kien, Xiaoying Zhuang. A deep neural network-based algorithm for solving structural optimization[J]. Journal of Zhejiang University Science A, 2021, 22(8): 609-620.

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author="Dung Nguyen Kien, Xiaoying Zhuang",
journal="Journal of Zhejiang University Science A",
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year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2000380"
}

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%T A deep neural network-based algorithm for solving structural optimization
%A Dung Nguyen Kien
%A Xiaoying Zhuang
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%@ 1673-565X
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2000380

TY - JOUR
T1 - A deep neural network-based algorithm for solving structural optimization
A1 - Dung Nguyen Kien
A1 - Xiaoying Zhuang
J0 - Journal of Zhejiang University Science A
VL - 22
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SP - 609
EP - 620
%@ 1673-565X
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A2000380


Abstract: 
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.

一种基于深度神经网络的结构优化求解算法

目的:提出一种新的优化方法以解决结构优化问题.
创新点:不是通过灵敏度分析来解决优化问题,而是利用深度学习神经网络的优势来寻找优化函数的最优值.
方法:1. 采用基于拉格朗日对偶和深度神经网络的方法.2. 将输入数据用于训练神经网络,直到输出值与预测值非常接近为止.3. 通过深度学习插值求解拉格朗日min-max对偶问题,从而找到最小输入值.
结论:1. 该方法可以解决结构优化问题,但它限制了设计变量输入的数量.2. 该方法的准确性取决于输入的区间大小;因此,下一步工作是发展新方法以减少输入数据集的数量.

关键词:深度拉格朗日方法;结构优化;深度学习;拉格朗日对偶

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

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