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由可重构超表面和自博弈强化学习实现的开放无形空间。

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

作者信息

Yin Xinman, Zhao Yanyu

机构信息

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.

出版信息

Light Sci Appl. 2025 Sep 15;14(1):323. doi: 10.1038/s41377-025-01944-5.

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

摘要

利用可重构超表面和自博弈强化学习构建了一个开放、动态且电磁隐形的空间。提出了一种名为MetaSeeker的模型,以优化随机分布超表面的隐身性能。隐藏物体可在构建的隐形空间内自由移动,环境相似度达99.5%。这一进展为复杂环境中的隐身技术提供了创新解决方案。由可重构超表面和自博弈强化学习实现的开放隐形空间。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0cd/12436619/eb0286ee3a82/41377_2025_1944_Figa_HTML.jpg

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