{"title":"Machine learning models in phononic metamaterials","authors":"Chen-Xu Liu , Gui-Lan Yu , Zhanli Liu","doi":"10.1016/j.cossms.2023.101133","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"28 ","pages":"Article 101133"},"PeriodicalIF":12.2000,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1359028623000785/pdfft?md5=f0802ffd6a7e6261dda409bdb7466cb4&pid=1-s2.0-S1359028623000785-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Opinion in Solid State & Materials Science","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1359028623000785","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍：
Title: Current Opinion in Solid State & Materials Science
Journal Overview:
Aims to provide a snapshot of the latest research and advances in materials science
Publishes six issues per year, each containing reviews covering exciting and developing areas of materials science
Each issue comprises 2-3 sections of reviews commissioned by international researchers who are experts in their fields
Provides materials scientists with the opportunity to stay informed about current developments in their own and related areas of research
Promotes cross-fertilization of ideas across an increasingly interdisciplinary field
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