石油化工高等学校学报2023，Vol.36Issue(6)：57-63,7.DOI:10.12422/j.issn.1006-396X.2023.06.006
用于红外宽带吸收器的深度学习网络模型框架
Deep Learning Neural Network Modeling Framework for Infrared Broadband Absorbers
摘要
Abstract
To reveal complex light-matter interactions,it is necessary to simplify the on-demand design of metamaterials for both forward and inverse applications.Deep learning,a popular data-driven approach,has recently alleviated to a large extent the time-consuming and empirical nature of widely used numerical simulations.A fully-connected deep neural network-based framework for inverse design and spectral prediction of broadband absorbers was proposed.The results demonstrate and validate the high accuracy of the proposed DNN model at 87.47%.The model not only outperform traditional numerical algorithms while ensuring accuracy,but also provides an important reference for on-demand design performance of metamaterials.关键词
逆设计问题/石墨烯/黑磷/深度学习/宽带吸收Key words
Inverse design problem/Graphene/Black phosphorus/Deep learning/Broadband absorption分类
信息技术与安全科学引用本文复制引用
王璇,冯乃星,张玉贤..用于红外宽带吸收器的深度学习网络模型框架[J].石油化工高等学校学报,2023,36(6):57-63,7.基金项目
国家自然科学青年基金项目(62101333). （62101333）
