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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

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Citations:  Bibtex RefMan EndNote GB/T7714


Dung Nguyen Kien


Xiaoying Zhuang


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


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",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T A deep neural network-based algorithm for solving structural optimization
%A Dung Nguyen Kien
%A Xiaoying Zhuang
%J Journal of Zhejiang University SCIENCE A
%V 22
%N 8
%P 609-620
%@ 1673-565X
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2000380

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
IS - 8
SP - 609
EP - 620
%@ 1673-565X
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2000380

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.


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.


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


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