Multilayer Perceptron Models for Band Diagram Prediction in bi-dimensional Photonic Crystals

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorDa Silva Ferreira, Adriano-
Autor(es): dc.creatorNardel Malheiros Silveira, Gilliard-
Autor(es): dc.creatorHernandez Figueroa, Hugo Enrique-
Data de aceite: dc.date.accessioned2021-03-11T01:32:26Z-
Data de disponibilização: dc.date.available2021-03-11T01:32:26Z-
Data de envio: dc.date.issued2019-10-06-
Data de envio: dc.date.issued2019-10-06-
Data de envio: dc.date.issued2019-01-11-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/SBFoton-IOPC.2018.8610926-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/187393-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/187393-
Descrição: dc.descriptionWe modeled Multilayer Perceptron (MLP) Artificial Neural Network for predicting band diagrams (BD) of bi-dimensional photonic crystals. Datasets for MLP training were created by relating geometric and material properties to BDs of triangular-and square-lattice photonic crystals. We demonstrate that fast-Training MLP models are able to estimate accurate BDs and existing photonic band gaps through rapid computations.-
Idioma: dc.languageen-
Relação: dc.relation2018 SBFoton International Optics and Photonics Conference, SBFoton IOPC 2018-
Direitos: dc.rightsclosedAccess-
Palavras-chave: dc.subjectmultilayer perceptron-
Palavras-chave: dc.subjectphotonic band gap-
Palavras-chave: dc.subjectphotonic crystal-
Palavras-chave: dc.subjectprediction-
Título: dc.titleMultilayer Perceptron Models for Band Diagram Prediction in bi-dimensional Photonic Crystals-
Aparece nas coleções:Repositório Institucional - Unesp

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