Machine learning models in phononic metamaterials

被引：9

作者：

Liu, Chen-Xu ^{[1
,2
]}

Yu, Gui-Lan ^{[2
]}

Liu, Zhanli ^{[1
]}

机构：

[1] Tsinghua Univ, Sch Aerosp, Dept Engn Mech, Appl Mech Lab, Beijing, Peoples R China

[2] Beijing Jiaotong Univ, Sch Civil Engn, Beijing, Peoples R China

来源：

CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE | 2024年 / 28卷

基金：

北京市自然科学基金; 中国国家自然科学基金;

关键词：

Phononic crystals; Elastic metamaterials; Machine learning; Deep learning; Neural networks; Elastic wave manipulation; NETWORKS; DESIGN;

D O I：

10.1016/j.cossms.2023.101133

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Machine learning opens up a new avenue for advancing the development of phononic crystals and elastic metamaterials. Numerous learning models have been employed and developed to address various challenges in the field of phononic metamaterials. Here, we provide an overview of mainstream machine learning models applied to phononic metamaterials, discuss their capabilities as well as limitations, and explore potential directions for future research.

引用

页数：10

共 54 条

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