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

Q4 Environmental Science

Scopus: Journal of East African Ornithology Pub Date : 2018-10-01 DOI:10.1109/SBFOTON-IOPC.2018.8610926

A. S. Ferreira, Gilliard Nardel Malheiros Silveira, H. Figueroa

引用次数: 0

Abstract

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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二维光子晶体带图预测的多层感知器模型

我们建立了多层感知器（MLP）人工神经网络模型，用于预测二维光子晶体的能带图（BD）。MLP训练的数据集是通过将几何和材料特性与三角形和方形晶格光子晶体的BD相关联来创建的。我们证明，快速训练的MLP模型能够通过快速计算来估计准确的BD和现有的光子带隙。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scopus: Journal of East African Ornithology Environmental Science-Ecology

CiteScore

0.60

自引率

0.00%

发文量

期刊介绍： Journal of East African Ornithology has been published since 1977 by the Bird Committee of the East Africa Natural History Society. Originally titled Scopus, the addition of Journal of East African Ornithology began with our January 2018 issue. The journal is published Open Access twice a year, typically in January and July. Authors retain copyright and their work is licensed under the Creative Commons Attribution 4.0 International License. Our copyright and licensing agreement only applies from January 2018 onwards, and does not apply to previously published issues. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of these articles.