Mapping the global design space of nanophotonic components using machine learning pattern recognition

Machine learning is increasingly used in nanophotonics for designing novel classes of complex devices but the general parameter behavior is often neglected. Here, the authors report a new methodology to discover and visualize optimal design spaces with respect to multiple performance objectives.

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Autores principales:	Daniele Melati, Yuri Grinberg, Mohsen Kamandar Dezfouli, Siegfried Janz, Pavel Cheben, Jens H. Schmid, Alejandro Sánchez-Postigo, Dan-Xia Xu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/2abb231657a342b8a35368c0295add15
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spelling	oai:doaj.org-article:2abb231657a342b8a35368c0295add152021-12-02T16:58:16ZMapping the global design space of nanophotonic components using machine learning pattern recognition10.1038/s41467-019-12698-12041-1723https://doaj.org/article/2abb231657a342b8a35368c0295add152019-10-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-12698-1https://doaj.org/toc/2041-1723Machine learning is increasingly used in nanophotonics for designing novel classes of complex devices but the general parameter behavior is often neglected. Here, the authors report a new methodology to discover and visualize optimal design spaces with respect to multiple performance objectives.Daniele MelatiYuri GrinbergMohsen Kamandar DezfouliSiegfried JanzPavel ChebenJens H. SchmidAlejandro Sánchez-PostigoDan-Xia XuNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-9 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Daniele Melati Yuri Grinberg Mohsen Kamandar Dezfouli Siegfried Janz Pavel Cheben Jens H. Schmid Alejandro Sánchez-Postigo Dan-Xia Xu Mapping the global design space of nanophotonic components using machine learning pattern recognition
description	Machine learning is increasingly used in nanophotonics for designing novel classes of complex devices but the general parameter behavior is often neglected. Here, the authors report a new methodology to discover and visualize optimal design spaces with respect to multiple performance objectives.
format	article
author	Daniele Melati Yuri Grinberg Mohsen Kamandar Dezfouli Siegfried Janz Pavel Cheben Jens H. Schmid Alejandro Sánchez-Postigo Dan-Xia Xu
author_facet	Daniele Melati Yuri Grinberg Mohsen Kamandar Dezfouli Siegfried Janz Pavel Cheben Jens H. Schmid Alejandro Sánchez-Postigo Dan-Xia Xu
author_sort	Daniele Melati
title	Mapping the global design space of nanophotonic components using machine learning pattern recognition
title_short	Mapping the global design space of nanophotonic components using machine learning pattern recognition
title_full	Mapping the global design space of nanophotonic components using machine learning pattern recognition
title_fullStr	Mapping the global design space of nanophotonic components using machine learning pattern recognition
title_full_unstemmed	Mapping the global design space of nanophotonic components using machine learning pattern recognition
title_sort	mapping the global design space of nanophotonic components using machine learning pattern recognition
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/2abb231657a342b8a35368c0295add15
work_keys_str_mv	AT danielemelati mappingtheglobaldesignspaceofnanophotoniccomponentsusingmachinelearningpatternrecognition AT yurigrinberg mappingtheglobaldesignspaceofnanophotoniccomponentsusingmachinelearningpatternrecognition AT mohsenkamandardezfouli mappingtheglobaldesignspaceofnanophotoniccomponentsusingmachinelearningpatternrecognition AT siegfriedjanz mappingtheglobaldesignspaceofnanophotoniccomponentsusingmachinelearningpatternrecognition AT pavelcheben mappingtheglobaldesignspaceofnanophotoniccomponentsusingmachinelearningpatternrecognition AT jenshschmid mappingtheglobaldesignspaceofnanophotoniccomponentsusingmachinelearningpatternrecognition AT alejandrosanchezpostigo mappingtheglobaldesignspaceofnanophotoniccomponentsusingmachinelearningpatternrecognition AT danxiaxu mappingtheglobaldesignspaceofnanophotoniccomponentsusingmachinelearningpatternrecognition
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Mapping the global design space of nanophotonic components using machine learning pattern recognition

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