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dc.contributor.authorDa Silva Ferreira, Adriano
dc.contributor.authorNardel Malheiros Silveira, Gilliard [UNESP]
dc.contributor.authorHernandez Figueroa, Hugo Enrique
dc.date.accessioned2019-10-06T15:34:45Z
dc.date.available2019-10-06T15:34:45Z
dc.date.issued2019-01-11
dc.identifierhttp://dx.doi.org/10.1109/SBFoton-IOPC.2018.8610926
dc.identifier.citation2018 SBFoton International Optics and Photonics Conference, SBFoton IOPC 2018.
dc.identifier.urihttp://hdl.handle.net/11449/187393
dc.description.abstractWe 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.en
dc.language.isoeng
dc.relation.ispartof2018 SBFoton International Optics and Photonics Conference, SBFoton IOPC 2018
dc.sourceScopus
dc.subjectmultilayer perceptron
dc.subjectphotonic band gap
dc.subjectphotonic crystal
dc.subjectprediction
dc.titleMultilayer Perceptron Models for Band Diagram Prediction in bi-dimensional Photonic Crystalsen
dc.typeTrabalho apresentado em evento
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionScience and Technology (IFSP)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.description.affiliationSchool of Electrical and Computer Engineering (FEEC) University of Campinas (UNICAMP)
dc.description.affiliationSão Paulo Federal Institute of Education Science and Technology (IFSP)
dc.description.affiliationSão Paulo State University (UNESP)
dc.description.affiliationUnespSão Paulo State University (UNESP)
dc.identifier.doi10.1109/SBFoton-IOPC.2018.8610926
dc.rights.accessRightsAcesso restrito
dc.identifier.scopus2-s2.0-85061983075
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