Machine learning assisted intelligent design of meta structures: a review
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comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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Título
Machine learning assisted intelligent design of meta structures: a reviewFecha de publicación
2023-10Editor
OAE PublishingCita bibliográfica
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.2023.29Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.oaepublish.com/articles/microstructures.2023.29Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Publicado en
Microstructures 2023; 3.Entidad financiadora
National Key Research and Development Program of China | National Natural Science Foundation of China | The Young Elite Scientists Sponsorship Program | Shanghai Science and Technology Committee | Fundamental Research Funds for the Central Universities | Agencia Estatal de Investigación
Código del proyecto o subvención
2022YFB4602000 | 12272267 | 52278411 | 2021QNRC001 | 22JC1404100 | 21JC1405600 | PID2021-124814NB-C22 | MCIN/AEI/10.13039/501100011033
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info:eu-repo/semantics/openAccess
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