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He et al. Microstructures 2023;3:2023037 Microstructures
DOI: 10.20517/microstructures.2023.29

Review Open Access

Machine learning assisted intelligent design of meta

structures: a review

1
1
2
Liangshu He , Yan Li , Daniel Torrent , Xiaoying Zhuang 3,4 , Timon Rabczuk 5 , Yabin Jin 1
1
School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China.
2
GROC-UJI, Institut de Noves Tecnologies de la Imatge, Universitat Jaume I, Castello 12080, Spain.
3
College of Civil Engineering, Tongji University, Shanghai 200092, China.
4
Institute of Photonics, Department of Mathematics and Physics, Leibniz University Hannover, Hannover 30167, Germany.
5
Institute of Structural Mechanics, Bauhaus-Universitat Weimar, Weimar 99423, Germany.
Correspondence to: Prof./Dr. Yan Li, School of Aerospace Engineering and Applied Mechanics, Tongji University, 100 Zhangwu
Road, Shanghai 200092, China. E-mail: liyan@tongji.edu.cn; Prof/Dr. Xiaoying Zhuang, College of Civil Engineering, Tongji
University, 1239, Siping Road, Shanghai 200092, China. E-mail: xiaoyingzhuang@tongji.edu.cn; Prof./Dr. Yabin Jin, School of
Aerospace Engineering and Applied Mechanics, Tongji University, 100 Zhangwu Road, Shanghai 200092, China. E-mail:
083623jinyabin@tongji.edu.cn
How to cite this article: He L, Li Y, Torrent D, Zhuang X, Rabczuk T, Jin Y. Machine learning assisted intelligent design of meta
structures: a review. Microstructures 2023;3:2023034. https://dx.doi.org/10.20517/microstructures.2023.29
Received: 1 Jun 2023 First Decision: 13 Jul 2023 Revised: 27 Jul 2023 Accepted: 4 Aug 2023 Published: 9 Oct 2023

Academic Editor: Jiamian Hu Copy Editor: Fangyuan Liu Production Editor: Fangyuan Liu

Abstract
In recent years, the rapid development of machine learning (ML) based on data-driven or environment interaction
has injected new vitality into the field of meta-structure design. As a supplement to the traditional analysis
methods based on physical formulas and rules, the involvement of ML has greatly accelerated the pace of
performance exploration and optimization for meta-structures. In this review, we focus on the latest progress of
ML in acoustic, elastic, and mechanical meta-structures from the aspects of band structures, wave propagation
characteristics, and static characteristics. We finally summarize and envisage some potential research directions of
ML in the field of meta-structures.

Keywords: Meta-structure, inverse design, machine learning, continuous fiber reinforced composite meta-
structure, additive manufacture

INTRODUCTION
[1]
Meta-structures are artificially designed functional structures that meet specific performance requirements

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