S. Krasikov, Aaron D Tranter, A. Bogdanov, Y. Kivshar
{"title":"Intelligent metaphotonics empowered by machine learning","authors":"S. Krasikov, Aaron D Tranter, A. Bogdanov, Y. Kivshar","doi":"10.29026/oea.2022.210147","DOIUrl":null,"url":null,"abstract":"In the recent years, we observe a dramatic boost of research in photonics empowered by the concepts of machine learning and artificial intelligence. The corresponding photonic systems, to which this new methodology is applied, can range from traditional optical waveguides to nanoantennas and metasurfaces, and these novel approaches underpin the fundamental principles of light-matter interaction developed for a smart design of intelligent photonic devices. Concepts and approaches of artificial intelligence and machine learning penetrate rapidly into the fundamental physics of light, and they provide effective tools for the study of the field of metaphotonics driven by optically-induced electric and magnetic resonances. Here, we introduce this new field with its application to metaphotonics and also present a summary of the basic concepts of machine learning with some specific examples developed and demonstrated for metasystems and metasurfaces.","PeriodicalId":19611,"journal":{"name":"Opto-Electronic Advances","volume":"1 1","pages":""},"PeriodicalIF":15.3000,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"54","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Opto-Electronic Advances","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.29026/oea.2022.210147","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 54
Abstract
In the recent years, we observe a dramatic boost of research in photonics empowered by the concepts of machine learning and artificial intelligence. The corresponding photonic systems, to which this new methodology is applied, can range from traditional optical waveguides to nanoantennas and metasurfaces, and these novel approaches underpin the fundamental principles of light-matter interaction developed for a smart design of intelligent photonic devices. Concepts and approaches of artificial intelligence and machine learning penetrate rapidly into the fundamental physics of light, and they provide effective tools for the study of the field of metaphotonics driven by optically-induced electric and magnetic resonances. Here, we introduce this new field with its application to metaphotonics and also present a summary of the basic concepts of machine learning with some specific examples developed and demonstrated for metasystems and metasurfaces.
期刊介绍:
Opto-Electronic Advances (OEA) is a distinguished scientific journal that has made significant strides since its inception in March 2018. Here's a collated summary of its key features and accomplishments:
Impact Factor and Ranking: OEA boasts an impressive Impact Factor of 14.1, which positions it within the Q1 quartiles of the Optics category. This high ranking indicates that the journal is among the top 25% of its field in terms of citation impact.
Open Access and Peer Review: As an open access journal, OEA ensures that research findings are freely available to the global scientific community, promoting wider dissemination and collaboration. It upholds rigorous academic standards through a peer review process, ensuring the quality and integrity of the published research.
Database Indexing: OEA's content is indexed in several prestigious databases, including the Science Citation Index (SCI), Engineering Index (EI), Scopus, Chemical Abstracts (CA), and the Index to Chinese Periodical Articles (ICI). This broad indexing facilitates easy access to the journal's articles by researchers worldwide.