Photonics Research, Volume. 11, Issue 5, 695(2023)

Self-design of arbitrary polarization-control waveplates via deep neural networks Spotlight on Optics

Zhengchang Liu1、†, Zhibo Dang1、†, Zhixin Liu, Yu Li, Xiao He, Yuchen Dai, Yuxiang Chen, Pu Peng, and Zheyu Fang*
Author Affiliations
  • School of Physics, State Key Lab for Mesoscopic Physics, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Center of Quantum Matter, Yangtze Delta Institute of Optoelectronics, and Nano-optoelectronics Frontier Center of Ministry of Education, Peking University, Beijing 100871, China
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    References(49)

    [34] S. Kim, P. Y. Lu, C. Loh, J. Smith, J. Snoek, M. Soljačić. Deep learning for Bayesian optimization of scientific problems with high-dimensional structure. arXiv(2022).

    [47] E. Brochu, V. M. Cora, N. de Freitas. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv(2010).

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    Zhengchang Liu, Zhibo Dang, Zhixin Liu, Yu Li, Xiao He, Yuchen Dai, Yuxiang Chen, Pu Peng, Zheyu Fang. Self-design of arbitrary polarization-control waveplates via deep neural networks[J]. Photonics Research, 2023, 11(5): 695

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    Paper Information

    Category: Nanophotonics and Photonic Crystals

    Received: Nov. 22, 2022

    Accepted: Jan. 8, 2023

    Published Online: Apr. 13, 2023

    The Author Email: Zheyu Fang (zhyfang@pku.edu.cn)

    DOI:10.1364/PRJ.480845

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