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Computing Optical Properties of Photonic Crystals by Using Multilayer Perceptron and Extreme Learning Machine

Bibliographic Details
Main Author: Da Silva Ferreira, Adriano
Publication Date: 2018
Other Authors: Malheiros-Silveira, Gilliard Nardel [UNESP], Hernandez-Figueroa, Hugo Enrique
Format: Article
Language: eng
Source: Repositório Institucional da UNESP
Download full: http://dx.doi.org/10.1109/JLT.2018.2856364
http://hdl.handle.net/11449/171222
Summary: In this paper, dispersion relations (DRs) of photonic crystals (PhCs) are computed by multilayer perceptron (MLP) and extreme learning machine (ELM) artificial neural networks (ANNs). Bi- and tri-dimensional optimized structures presenting distinct DRs and photonic band gaps (PBGs) were selected for case studies. Optical properties of a set of PhCs with similar geometries and different dimensions were calculated by an electromagnetic solver in order to provide input data for ANN training and testing. We demonstrate that simple- and fast-training ANN models are capable of providing accurate DRs' curves in a very short time.
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spelling Computing Optical Properties of Photonic Crystals by Using Multilayer Perceptron and Extreme Learning MachineDispersion relationextreme learning machinemultilayer perceptronphotonic band gapphotonic crystalIn this paper, dispersion relations (DRs) of photonic crystals (PhCs) are computed by multilayer perceptron (MLP) and extreme learning machine (ELM) artificial neural networks (ANNs). Bi- and tri-dimensional optimized structures presenting distinct DRs and photonic band gaps (PBGs) were selected for case studies. Optical properties of a set of PhCs with similar geometries and different dimensions were calculated by an electromagnetic solver in order to provide input data for ANN training and testing. We demonstrate that simple- and fast-training ANN models are capable of providing accurate DRs' curves in a very short time.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)School of Electrical and Computer Engineering University of CampinasSão Paulo State University (UNESP) Campus of São João da BoaCentro de Tecnologia da Informacao Renato ArcherSão Paulo State University (UNESP) Campus of São João da BoaCNPq: 300594/2017-8Universidade Estadual de Campinas (UNICAMP)Universidade Estadual Paulista (Unesp)Centro de Tecnologia da Informacao Renato ArcherDa Silva Ferreira, AdrianoMalheiros-Silveira, Gilliard Nardel [UNESP]Hernandez-Figueroa, Hugo Enrique2018-12-11T16:54:28Z2018-12-11T16:54:28Z2018-09-15info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article4066-4073application/pdfhttp://dx.doi.org/10.1109/JLT.2018.2856364Journal of Lightwave Technology, v. 36, n. 18, p. 4066-4073, 2018.0733-8724http://hdl.handle.net/11449/17122210.1109/JLT.2018.28563642-s2.0-850499989332-s2.0-85049998933.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Lightwave Technology1,166info:eu-repo/semantics/openAccess2023-10-27T06:07:05Zoai:repositorio.unesp.br:11449/171222Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestrepositoriounesp@unesp.bropendoar:29462023-10-27T06:07:05Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Computing Optical Properties of Photonic Crystals by Using Multilayer Perceptron and Extreme Learning Machine
title Computing Optical Properties of Photonic Crystals by Using Multilayer Perceptron and Extreme Learning Machine
spellingShingle Computing Optical Properties of Photonic Crystals by Using Multilayer Perceptron and Extreme Learning Machine
Da Silva Ferreira, Adriano
Dispersion relation
extreme learning machine
multilayer perceptron
photonic band gap
photonic crystal
title_short Computing Optical Properties of Photonic Crystals by Using Multilayer Perceptron and Extreme Learning Machine
title_full Computing Optical Properties of Photonic Crystals by Using Multilayer Perceptron and Extreme Learning Machine
title_fullStr Computing Optical Properties of Photonic Crystals by Using Multilayer Perceptron and Extreme Learning Machine
title_full_unstemmed Computing Optical Properties of Photonic Crystals by Using Multilayer Perceptron and Extreme Learning Machine
title_sort Computing Optical Properties of Photonic Crystals by Using Multilayer Perceptron and Extreme Learning Machine
author Da Silva Ferreira, Adriano
author_facet Da Silva Ferreira, Adriano
Malheiros-Silveira, Gilliard Nardel [UNESP]
Hernandez-Figueroa, Hugo Enrique
author_role author
author2 Malheiros-Silveira, Gilliard Nardel [UNESP]
Hernandez-Figueroa, Hugo Enrique
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual de Campinas (UNICAMP)
Universidade Estadual Paulista (Unesp)
Centro de Tecnologia da Informacao Renato Archer
dc.contributor.author.fl_str_mv Da Silva Ferreira, Adriano
Malheiros-Silveira, Gilliard Nardel [UNESP]
Hernandez-Figueroa, Hugo Enrique
dc.subject.por.fl_str_mv Dispersion relation
extreme learning machine
multilayer perceptron
photonic band gap
photonic crystal
topic Dispersion relation
extreme learning machine
multilayer perceptron
photonic band gap
photonic crystal
description In this paper, dispersion relations (DRs) of photonic crystals (PhCs) are computed by multilayer perceptron (MLP) and extreme learning machine (ELM) artificial neural networks (ANNs). Bi- and tri-dimensional optimized structures presenting distinct DRs and photonic band gaps (PBGs) were selected for case studies. Optical properties of a set of PhCs with similar geometries and different dimensions were calculated by an electromagnetic solver in order to provide input data for ANN training and testing. We demonstrate that simple- and fast-training ANN models are capable of providing accurate DRs' curves in a very short time.
publishDate 2018
dc.date.none.fl_str_mv 2018-12-11T16:54:28Z
2018-12-11T16:54:28Z
2018-09-15
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/JLT.2018.2856364
Journal of Lightwave Technology, v. 36, n. 18, p. 4066-4073, 2018.
0733-8724
http://hdl.handle.net/11449/171222
10.1109/JLT.2018.2856364
2-s2.0-85049998933
2-s2.0-85049998933.pdf
url http://dx.doi.org/10.1109/JLT.2018.2856364
http://hdl.handle.net/11449/171222
identifier_str_mv Journal of Lightwave Technology, v. 36, n. 18, p. 4066-4073, 2018.
0733-8724
10.1109/JLT.2018.2856364
2-s2.0-85049998933
2-s2.0-85049998933.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Journal of Lightwave Technology
1,166
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 4066-4073
application/pdf
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv repositoriounesp@unesp.br
_version_ 1834483196981411840

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