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Dynamic metasurface control using Deep Reinforcement Learning

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  • Zhao, Ying
  • Li, Liang
  • Lanteri, Stéphane
  • Viquerat, Jonathan

Abstract

Dynamic metasurface is an emerging concept for achieving a flexible control of electromagnetic waves. Generalized sheet transition conditions (GSTCs) can be used to model the relationship between the electromagnetic response and surface susceptibility parameters characterizing a metasurface. However, when it comes to the inverse problem of designing and controlling a metasurface in a space–time varying context based on GSTCs, the dynamic synthesis of the susceptibility parameters is a difficult and non-intuitive task. In this paper, we transform the inverse problem of solving dynamic susceptibility parameters into a sequence of control problems. Based on FDTD numerical simulations, a Deep Reinforcement Learning (DRL) framework using a proximal policy optimization (PPO) algorithm and a fully connected neural network is designed to control the susceptibility parameters intelligently and efficiently, promoting the further expansion of the application range of metasurface and thus helping realize more flexible and effective control of electromagnetic waves. We provide numerical results in a one-dimensional setting to show the applicability, correctness and effectiveness of the proposed approach.

Suggested Citation

  • Zhao, Ying & Li, Liang & Lanteri, Stéphane & Viquerat, Jonathan, 2022. "Dynamic metasurface control using Deep Reinforcement Learning," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 197(C), pages 377-395.
    • Handle: RePEc:eee:matcom:v:197:y:2022:i:c:p:377-395
    DOI: 10.1016/j.matcom.2022.02.016
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    1. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
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