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

Autor(es)

Ferreira, Adriano da Silva ; Malheiros Silveira, Gilliard Nardel ; Hernandez Figueroa, Hugo Enrique ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2018 SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE (SBFOTON IOPC); v. N/A, p. 5-pg., 2018-01-01.

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. (AU)

• Processo FAPESP: 15/24517-8 - Fotônica para internet de nova geração
• Beneficiário: Hugo Enrique Hernández Figueroa
• Modalidade de apoio: Auxílio à Pesquisa - Temático