(Peer-Reviewed) Intelligent metaphotonics empowered by machine learning
Sergey Krasikov ¹ ², Aaron Tranter ³, Andrey Bogdanov ², Yuri Kivshar ¹
¹ Nonlinear Physics Center, Research School of Physics, The Australian National University, Canberra ACT 2601, Australia
² School of Physics and Engineering, ITMO University, St. Petersburg 197101, Russia
³ Centre for Quantum Computation and Communication Technology, Department of Quantum Science, Research School of Physics, The Australian National University, Canberra, ACT 2601, Australia
Opto-Electronic Advances, 2022-03-25
Abstract
In the recent years, a dramatic boost of the research is observed at the junction of photonics, machine learning and artificial intelligence. A new methodology can be applied to the description of a variety of photonic systems including optical waveguides, nanoantennas, and metasurfaces. These novel approaches underpin the fundamental principles of light-matter interaction developed for a smart design of intelligent photonic devices.
Artificial intelligence and machine learning penetrate rapidly into the fundamental physics of light, and they provide effective tools for the study of the field of metaphotonics driven by optically induced electric and magnetic resonances. Here we overview the evaluation of metaphotonics induced by artificial intelligence and present a summary of the concepts of machine learning with some specific examples developed and demonstrated for metasystems and metasurfaces.
Separation and identification of mixed signal for distributed acoustic sensor using deep learning
Huaxin Gu, Jingming Zhang, Xingwei Chen, Feihong Yu, Deyu Xu, Shuaiqi Liu, Weihao Lin, Xiaobing Shi, Zixing Huang, Xiongji Yang, Qingchang Hu, Liyang Shao
Opto-Electronic Advances
2025-11-25
