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Total Transmission from Deep Learning Designs

Bei Wu Zhan-Lei Hao Jin-Hui Chen Qiao-Liang Bao Yi-Neng Liu Huan-Yang Chen

电子科技学刊2022，Vol.20Issue(1)：9-19,11.

电子科技学刊2022，Vol.20Issue(1)：9-19,11.DOI:10.1016/j.jnlest.2021.100146

Total Transmission from Deep Learning Designs

Bei Wu ¹Zhan-Lei Hao ²Jin-Hui Chen ³Qiao-Liang Bao ⁴Yi-Neng Liu ⁵Huan-Yang Chen¹

作者信息

1. Department of Physics, Xiamen University, Xiamen 361005
2. Institute of Electromagnetics and Acoustics, Xiamen University, Xiamen 361005
3. Xiamen Key Laboratory of Multiphysics Electronic Information, Xiamen 361005
4. Fujian Provincial Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen 361005
5. Fujian Engineering Research Center for EDA, Xiamen 361005
折叠

摘要

关键词

Artificial neural networks (ANNs)/deep learning/forward spectral prediction/inverse material design/total transmission

Key words

Artificial neural networks (ANNs)/deep learning/forward spectral prediction/inverse material design/total transmission

引用本文复制引用

Bei Wu,Zhan-Lei Hao,Jin-Hui Chen,Qiao-Liang Bao,Yi-Neng Liu,Huan-Yang Chen..Total Transmission from Deep Learning Designs[J].电子科技学刊,2022,20(1):9-19,11.

基金项目

This work was supported by the National Key Research and Development Program of China under Grant No. 2020YFA0710100 （）

the National Natural Science Foundation of China under Grants No. 92050102, No. 11874311, and No. 11504306 （）

the Fundamental Research Funds for the Central Universities under Grant No. 20720200074. （）

电子科技学刊

OACSCD

ISSN：1674-862X

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