Machine learning assisted intelligent design of meta structures: a review

authored by: Liangshu He, Yan Li, Daniel Torrent, Xiaoying Zhuang, Timon Rabczuk, Yabin Jin
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.
Organisation(s): Institute of Photonics
External Organisation(s): Tongji University
Universitat Jaume I
Bauhaus-Universität Weimar
Type: Review article
Journal: Microstructures
Volume: 3
Publication date: 09.10.2023
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Materials Science (miscellaneous)
Electronic version(s): https://doi.org/10.20517/microstructures.2023.29 (Access: Open)
: Details in the research portal "Research@Leibniz University"

BibTeX

@article{128996c978ff45b09ba33a774a54e236,
title = "Machine learning assisted intelligent design of meta structures: a review",
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 = "additive manufacture, continuous fiber reinforced composite meta-structure, inverse design, machine learning, Meta-structure",
author = "Liangshu He and Yan Li and Daniel Torrent and Xiaoying Zhuang and Timon Rabczuk and Yabin Jin",
note = "Funding Information: This work was supported by the National Key R&D Program of China (Grant No. 2022YFB4602000), the National Natural Science Foundation of China (No.12272267 and No. 52278411), the Young Elite Scientists Sponsorship Program by CAST (2021QNRC001), the Shanghai Science and Technology Committee (No. 22JC1404100 and No. 21JC1405600), the Fundamental Research Funds for the Central Universities. This publication is part of Project No. PID2021-124814NB-C22, funded by MCIN/AEI/10.13039/501100011033, “FEDER, A way of making Europe”.",
year = "2023",
month = oct,
day = "9",
doi = "10.20517/microstructures.2023.29",
language = "English",
volume = "3",
journal = "Microstructures",
issn = "2770-2995",
publisher = "OAE Publishing Inc.",
number = "4",
}

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