机器学习赋予的智能变形学

IF 15.3 1区 物理与天体物理 Q1 OPTICS
S. Krasikov, Aaron D Tranter, A. Bogdanov, Y. Kivshar
{"title":"机器学习赋予的智能变形学","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":"{\"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}","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

摘要

近年来,我们观察到在机器学习和人工智能概念的推动下,光子学的研究取得了巨大的进步。这种新方法所应用的相应光子系统可以从传统的光波导到纳米天线和超表面,这些新方法巩固了为智能光子器件的智能设计而开发的光-物质相互作用的基本原理。人工智能和机器学习的概念和方法迅速渗透到光的基础物理学中,它们为光感应电和磁共振驱动的变光子学领域的研究提供了有效的工具。在这里,我们介绍了这一新的领域及其在元光子学中的应用,并对机器学习的基本概念进行了总结,并给出了一些针对元系统和元表面开发和演示的具体示例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent metaphotonics empowered by machine learning
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
19.30
自引率
7.10%
发文量
128
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信