Computing Optical Properties of Photonic Crystals by Using Multilayer Perceptron and Extreme Learning Machine
Autor(a) principal: | |
---|---|
Data de Publicação: | 2018 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/JLT.2018.2856364 http://hdl.handle.net/11449/171222 |
Resumo: | 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. |
id |
UNSP_1a9d8b7b730e2cd9cc2afc0ec5d6e0b2 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/171222 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
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/requestopendoar:29462024-08-05T16:06:07.937980Repositório 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 |
|
_version_ |
1808128608140525568 |