An open invisible space enabled by reconfigurable metasurfaces and self-play reinforcement learning

Xinman Yin; Yanyu Zhao

doi:10.1038/s41377-025-01944-5

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An open invisible space enabled by reconfigurable metasurfaces and self-play reinforcement learning

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- An open invisible space enabled by reconfigurable metasurfaces and self-play reinforcement learning
- Light: Science & Applications Vol. 14, Issue 11, Pages: 3332-3334(2025)
- Affiliations：
  
  Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Engineering Medicine, Beihang University, Beijing 100191, China
- Author bio：
  
  Yanyu Zhao (yanyuzhao@buaa.edu.cn)
- Funds：
- DOI：10.1038/s41377-025-01944-5
  CLC：
- Published Online：15 September 2025，
  
  Published：30 November 2025
- Accepted：
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Yin, X. M. & Zhao, Y. Y. An open invisible space enabled by reconfigurable metasurfaces and self-play reinforcement learning. Light: Science & Applications, 14, 3332-3334 (2025).
DOI：

Yin, X. M. & Zhao, Y. Y. An open invisible space enabled by reconfigurable metasurfaces and self-play reinforcement learning. Light: Science & Applications, 14, 3332-3334 (2025). DOI： 10.1038/s41377-025-01944-5.

摘要

Abstract

An open

dynamic

and electromagnetically invisible space has been constructed using reconfigurable metasurfaces and self-play reinforcement learning. A model named MetaSeeker is proposed to optimize the cloaking performance of randomly distributed metasurfaces. The hidden objects can move freely within the constructed invisible space

with environmental similarity of 99.5%. This advancement provides an innovative solution for cloaking technologies in complex environments.

关键词

Keywords

references

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