Designing artificial neural networks for band structures computations in photonic crystals
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Data
2019-01-01
Autores
Da Ferreira, Adriano S.
Malheiros-Silveira, Gilliard N. [UNESP]
Hernández-Figueroa, Hugo E.
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Resumo
We modeled Multilayer Perceptron and Extreme Learning Machine Artificial Neural Networks (ANNs) for computing band structures (BSTs) and photonic band gaps (PBGs) of 2D and 3D photonic crystals (PhCs). We aim at providing fast ANN models which might boost the computations of BDs and PBGs regarding electromagnetic solvers. The case studies considered 2D and 3D PhCs with different lattices, geometries, and materials. Datasets for ANN training were built by varying the geometric shapes' dimensions and the dielectric constants of the case-study PhCs. We demonstrate simple and fast-training ANNs capable of providing accurate BSTs and PGBs through speedy computations.
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Palavras-chave
Artificial neural network, Photonic band gap, Photonic band structure, Photonic crystal
Como citar
Proceedings of SPIE - The International Society for Optical Engineering, v. 10912.