Research on structure topology optimization design empowered by deep learning method

CHEN Xiaoqian; ZHANG Zeyu; LI Yu; YAO Wen; ZHOU Weien

doi:10.6052/1000-0992-23-052

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Article Navigation > Advances in Mechanics > 2024 > Corrected proof

Chen X Q, Zhang Z Y, Li Y, Yao W, Zhou W E. Research on structure topology optimization design empowered by deep learning method. Advances in Mechanics, 2024, 54(2): 1-46 doi: 10.6052/1000-0992-23-052

Citation:

Chen X Q, Zhang Z Y, Li Y, Yao W, Zhou W E. Research on structure topology optimization design empowered by deep learning method. Advances in Mechanics, 2024, 54(2): 1-46 doi: 10.6052/1000-0992-23-052

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Research on structure topology optimization design empowered by deep learning method

doi: 10.6052/1000-0992-23-052

CHEN Xiaoqian^{1, 4},
ZHANG Zeyu^{2, 3, 4},
LI Yu^{2, 4},
YAO Wen^{2, 4
,
,},
ZHOU Weien^{2, 4}

1.
Chinese Academy of Military Science, Beijing 100195, China
2.
Defense Innovation Institute, Chinese Academy of Military Science, Beijing 100071, China
3.
College of Aerospace Science and Engineering, National University of Defense Technology,Changsha 410073, China
4.
Intelligent Game and Decision Laboratory, Beijing 100071, China

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Corresponding author: wendy0782@126.com
Received Date: 2023-12-12
Accepted Date: 2024-01-31

Available Online: 2024-02-05

Abstract

Abstract

This article comprehensively discusses the relevant research progress in the field of structural topology optimization and the cross-integration development of deep learning technology in recent years. Focusing on the core methods and key modules of structural topology optimization design, two major types of empowerment methods are systematically sorted out from the perspective of deep learning empowerment. The study points out that the global surrogate model construction method for structural optimization design based on deep learning technology, as a direct mapping structural design method, has been widely studied because of its simple and typical design ideas. However, the global surrogate model has limitations in computation and generalization. The limitations and deficiencies in performance are also particularly obvious. The structural optimization design method with local sub-link acceleration and replacement integrated with deep learning technology is a more flexible and diverse form of local empowerment, with good universality and unique advantages. The article looks forward to the future development of intelligently empowered structural optimization. Further research work would focus on the organic combination of deep learning and structural design, as well as the co-driven design paradigm of data and knowledge.
- topology optimization,
- deep learning,
- artificial neural network,
- surrogate model

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References(234)

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