Details
| Original language | English |
|---|---|
| Article number | 2023037 |
| Journal | Microstructures |
| Volume | 3 |
| Issue number | 4 |
| Publication status | Published - 9 Oct 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.
Keywords
- additive manufacture, continuous fiber reinforced composite meta-structure, inverse design, machine learning, Meta-structure
ASJC Scopus subject areas
- Materials Science(all)
- Materials Science (miscellaneous)
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Microstructures, Vol. 3, No. 4, 2023037, 09.10.2023.
Research output: Contribution to journal › Review article › Research › peer review
}
TY - JOUR
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”.
PY - 2023/10/9
Y1 - 2023/10/9
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
UR - http://www.scopus.com/inward/record.url?scp=85180183716&partnerID=8YFLogxK
U2 - 10.20517/microstructures.2023.29
DO - 10.20517/microstructures.2023.29
M3 - Review article
AN - SCOPUS:85180183716
VL - 3
JO - Microstructures
JF - Microstructures
IS - 4
M1 - 2023037
ER -