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Machine learning assisted intelligent design of meta structures: a review

Publikation: Beitrag in FachzeitschriftÜbersichtsarbeitForschungPeer-Review

Autorschaft

  • Liangshu He
  • Yan Li
  • Daniel Torrent
  • Xiaoying Zhuang

Organisationseinheiten

Externe Organisationen

  • Tongji University
  • Universitat Jaume I
  • Bauhaus-Universität Weimar
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Details

OriginalspracheEnglisch
Aufsatznummer2023037
FachzeitschriftMicrostructures
Jahrgang3
Ausgabenummer4
PublikationsstatusVeröffentlicht - 9 Okt. 2023

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.

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Zitieren

Machine learning assisted intelligent design of meta structures: a review. / He, Liangshu; Li, Yan; Torrent, Daniel et al.
in: Microstructures, Jahrgang 3, Nr. 4, 2023037, 09.10.2023.

Publikation: Beitrag in FachzeitschriftÜbersichtsarbeitForschungPeer-Review

He, L, Li, Y, Torrent, D, Zhuang, X, Rabczuk, T & Jin, Y 2023, 'Machine learning assisted intelligent design of meta structures: a review', Microstructures, Jg. 3, Nr. 4, 2023037. https://doi.org/10.20517/microstructures.2023.29
He, L., Li, Y., Torrent, D., Zhuang, X., Rabczuk, T., & Jin, Y. (2023). Machine learning assisted intelligent design of meta structures: a review. Microstructures, 3(4), Artikel 2023037. https://doi.org/10.20517/microstructures.2023.29
He L, Li Y, Torrent D, Zhuang X, Rabczuk T, Jin Y. Machine learning assisted intelligent design of meta structures: a review. Microstructures. 2023 Okt 9;3(4):2023037. doi: 10.20517/microstructures.2023.29
He, Liangshu ; Li, Yan ; Torrent, Daniel et al. / Machine learning assisted intelligent design of meta structures: a review. in: Microstructures. 2023 ; Jahrgang 3, Nr. 4.
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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.",
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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”.",
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T1 - Machine learning assisted intelligent design of meta structures: a review

AU - He, Liangshu

AU - Li, Yan

AU - Torrent, Daniel

AU - Zhuang, Xiaoying

AU - Rabczuk, Timon

AU - Jin, Yabin

N1 - 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”.

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N2 - 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.

AB - 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.

KW - additive manufacture

KW - continuous fiber reinforced composite meta-structure

KW - inverse design

KW - machine learning

KW - Meta-structure

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