Designing artificial neural networks for band structures computations in photonic crystals

机译：设计用于光子晶体能带结构计算的人工神经网络

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We modeled Multilayer Perceptron and Extreme Learning Machine Artificial Neural Networks (ANNs) for computingband structures (BSTs) and photonic band gaps (PBGs) of 2D and 3D photonic crystals (PhCs). We aimat providing fast ANN models which might boost the computations of BSTs and PBGs regarding electromagneticsolvers. The case studies considered 2D and 3D PhCs with different lattices, geometries, and materials. Datasetsfor ANN training were built by varying the geometric dimensions and the dielectric constants of the case-studyPhCs. We demonstrate simple and fast-training ANNs capable of providing accurate BSTs and PGBs throughspeedy computations.

机译：我们对多层感知器和极限学习机人工神经网络（ANN）进行建模，以计算2D和3D光子晶体（PhC）的带结构（BST）和光子带隙（PBG）。我们的目标是提供快速的ANN模型，这可能会提高有关电磁分解器的BST和PBG的计算。案例研究考虑了具有不同晶格，几何形状和材料的2D和3D PhC。通过改变案例研究\ r \ nPhCs的几何尺寸和介电常数来建立用于ANN训练的数据集。我们演示了简单且训练有素的人工神经网络，能够通过快速计算提供准确的BST和PGB。

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《Physics and Simulation of Optoelectronic Devices XXVII》|2019年|109121N.1-109121N.8|共8页
会议地点 0277-786X;1996-756X
作者
Adriano da S. Ferreira; Gilliard N. Malheiros-Silveira; Hugo E. Hernandez-Figueroa;
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School of Electrical and Computer Engineering, University of Campinas (UNICAMP), campus of Campinas, Sao Paulo, 13083-852, Brazil,Sao Paulo Federal Institute of Education, Science and Technology (IFSP), campus of Hortolandia, Sao Paulo, 13183-250, Brazil adrianof@decom.fee.unicamp.br;

School of Electrical and Computer Engineering, University of Campinas (UNICAMP), campus of Campinas, Sao Paulo, 13083-852, Brazil,Sao Paulo State University (UNESP), campus of Sao Joao da Boa Vista, Sao Paulo, 13876-750, Brazil;

School of Electrical and Computer Engineering, University of Campinas (UNICAMP), campus of Campinas, Sao Paulo, 13083-852, Brazil;

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机译：利用人工神经网络提高光子晶体光纤光子结构的精度
3. Advances in artificial neural networks, machine learning and computational intelligence Selected papers from the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018) [J] . Oneto Luca, Bunte Kerstin, Schleif Frank-Michael Neurocomputing . 2019,第MAYa21期

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4. Designing artificial neural networks for band structures computations in photonic crystals [C] . Adriano da S. Ferreira, Gilliard N. Malheiros-Silveira, Hugo E. Hernandez-Figueroa Conference on Physics and Simulation of Optoelectronic Devices XXVII . 2019

机译：用于光子晶体频带结构计算的人工神经网络
5. Computation of photonic band structure and transmission spectra of two-dimensional photonic bandgap crystals. [D] . Qiu, Yang. 1998

机译：二维光子带隙晶体的光子能带结构和透射光谱的计算。
6. Optical Properties of Porous Alumina Assisted Niobia Nanostructured Films–Designing 2-D Photonic Crystals Based on Hexagonally Arranged Nanocolumns [O] . Andrei Pligovka, Alexander Poznyak, Małgorzata Norek 2021

机译：基于六角形纳米柱的多孔氧化铝辅助铌纳米结构薄膜设计2-D光子晶体的光学性质
7. Computation of the band structure of two-dimensional photonic crystals with hp finite elements [O] . K. Schmidt, P. Kauf 1249

机译：用hp有限元计算二维光子晶体的能带结构

Designing artificial neural networks for band structures computations in photonic crystals

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