Multilayer Perceptron Models for Band Diagram Prediction in bi-dimensional Photonic Crystals
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Data
2019-01-11
Autores
Da Silva Ferreira, Adriano
Nardel Malheiros Silveira, Gilliard [UNESP]
Hernandez Figueroa, Hugo Enrique
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Resumo
We 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.
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multilayer perceptron, photonic band gap, photonic crystal, prediction
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2018 SBFoton International Optics and Photonics Conference, SBFoton IOPC 2018.