Autonomous aeroamphibious invisibility cloak with stochastic-evolution learning

Chao Qian; Yuetian Jia; Zhedong Wang; Jieting Chen; Pujing Lin; Xiaoyue Zhu; Erping Li; Hongsheng Chen

doi:10.1117/1.AP.6.1.016001

Advanced Photonics, Volume. 6, Issue 1, 016001(2024)

Autonomous aeroamphibious invisibility cloak with stochastic-evolution learning Article Video

Chao Qian^1、†,*, Yuetian Jia¹, Zhedong Wang¹, Jieting Chen¹, Pujing Lin¹, Xiaoyue Zhu¹, Erping Li¹, and Hongsheng Chen^1,2,3、*

Author Affiliations

¹Zhejiang University, ZJU-UIUC Institute, Interdisciplinary Center for Quantum Information, State Key Laboratory of Extreme Photonics and Instrumentation, Hangzhou, China

²Zhejiang University, ZJU-Hangzhou Global Science and Technology Innovation Center, Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang, Hangzhou, China

³Zhejiang University, Jinhua Institute of Zhejiang University, Jinhua, China

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References(46)

[1] C. Qian, H. Chen. A perspective on the next generation of invisibility cloaks—intelligent cloaks. Appl. Phys. Lett., 118, 180501(2021).

[2] C. M. Watts, X. Liu, W. J. Padilla. Metamaterial electromagnetic wave absorbers. Adv. Mater., 24, OP98-OP120(2012).

[3] M. Stevens, S. Merilaita. Animal camouflage: current issues and new perspectives. Philos. Trans. R. Soc. Lond. B Biol. Sci., 364, 423-427(2009).

[4] J. B. Pendry, D. Schurig, D. R. Smith. Controlling electromagnetic fields. Science, 312, 1780-1782(2006).

[5] B. Zheng et al. Revealing the transformation invariance of full-parameter omnidirectional invisibility cloaks. Electromagn. Sci., 6, 0020092(2023).

[6] D. Schurig et al. Metamaterial electromagnetic cloak at microwave frequencies. Science, 314, 977-980(2006).

[7] M. Gharghi et al. A carpet cloak for visible light. Nano Lett., 11, 2825-2828(2011).

[8] X. Huang et al. Dirac cones induced by accidental degeneracy in photonic crystals and zero-refractive-index materials. Nat. Mater., 10, 582-586(2011).

[9] K. L. Tsakmakidis et al. Ultrabroadband 3D invisibility with fast-light cloaks. Nat. Commun., 10, 4859(2019).

[10] S. Xu et al. Cross-wavelength invisibility integrated with various invisibility tactics. Sci. Adv., 6, eabb3755(2020).

[11] Z. Qin et al. Superscattering of water waves. Natl. Sci. Rev., 10, nwac255(2022).

[12] T. Cai et al. Ultrawideband chromatic aberration-free meta-mirrors. Adv. Photonics, 3, 016001(2021).

[13] A. H. Dorrah, F. Capasso. Tunable structured light with flat optics. Science, 376, eabi6860(2022).

[14] F. Ding. A review of multifunctional optical gap-surface plasmon metasurfaces. Prog. Electromagn. Res., 174, 55-73(2022).

[15] C. Qian et al. Deep-learning-enabled self-adaptive microwave cloak without human intervention. Nat. Photonics, 14, 383-390(2020).

[16] Y. Saifullah et al. Recent progress in reconfigurable and intelligent metasurfaces: a comprehensive review of tuning mechanisms, hardware designs, and applications. Adv. Sci., 9, 2203747(2022).

[17] Y. Xu et al. Software-defined nanophotonic devices and systems empowered by machine learning. Prog. Quantum Electron., 89, 100469(2023).

[18] K. Edwards et al. Machine-learning-enabled recovery of prior information from experimental breast microwave imaging data. Prog. Electromagn. Res., 175, 1-11(2022).

[19] W. Ma et al. Pushing the limits of functionality—multiplexing capability in metasurface design based on statistical machine learning. Adv. Mater., 34, 2110022(2022).

[20] N. Wu et al. Pushing the limits of metasurface cloak using global inverse design. Adv. Opt. Mater., 11, 2202130(2023).

[21] Q. Tan et al. Solving multivariable equations with tandem metamaterial kernels. Prog. Electromagn. Res., 175, 139-147(2022).

[22] S. So et al. Deep learning enabled inverse design in nanophotonics. Nanophotonics, 9, 1041-1057(2020).

[23] X. Guo et al. Nonreciprocal metasurface with space-time phase modulation. Light Sci. Appl., 8, 123(2019).

[24] L. Zhang et al. Space-time-coding digital metasurfaces. Nat. Commun., 9, 4334(2018).

[25] Z. Fan et al. Homeostatic neuro-metasurfaces for dynamic wireless channel management. Sci. Adv., 8, eabn7905(2022).

[26] O. Tsilipakos et al. Toward intelligent metasurfaces: the progress from globally tunable metasurfaces to software-defined metasurfaces with an embedded network of controllers. Adv. Opt. Mater., 8, 2000783(2020).

[27] Q. Ma et al. Information metasurfaces and intelligent metasurfaces. Photonics Insights, 6, R01(2022).

[28] P. Lin et al. Enabling intelligent metasurfaces for semi-known input. Prog. Electromagn. Res., 178, 83-91(2023).

[29] Y. Jia et al. A knowledge-inherited learning for intelligent metasurface design and assembly. Light Sci. Appl., 12, 82(2023).

[30] Z. Yu, S. Fan. Complete optical isolation created by indirect interband photonic transitions. Nat. Photonics, 3, 91-94(2009).

[31] D. Ramaccia et al. Phase-induced frequency conversion and Doppler effect with time-modulated metasurfaces. IEEE Trans. Antennas Propag., 68, 1607-1617(2020).

[32] X. Zhu et al. Realization of index modulation with intelligent spatiotemporal metasurfaces. Adv. Intell. Syst., 5, 2300065(2023).

[33] Z. Wang et al. Demonstration of spider eyes-like intelligent antennas for dynamically perceiving incoming waves. Adv. Intell. Syst., 3, 2100066(2021).

[34] Y. Jia et al. In-situ customized illusion enabled by global metasurface reconstruction. Adv. Funct. Mater., 32, 2109331(2022).

[35] C. Huang et al. Reconfigurable metasurface cloak for dynamical electromagnetic illusions. ACS Photonics, 5, 1718-1725(2017).

[36] Z. Zhen et al. Realizing transmitted metasurface cloak by a tandem neural network. Photonics Res., 9, B229-B235(2021).

[37] J. Chen et al. Correlating metasurface spectra with a generation-elimination framework. Nat. Commun., 14, 4872(2023).

[38] K. Sohn, H. Lee, X. Yan. Learning structured output representation using deep conditional generative models, 3483-3491(2015).

[39] L. van der Maaten, G. Hinton. Visualizing data using t-SNE. J. Mach. Learn. Res., 9, 2579-2605(2008).

[40] T. Cai et al. Experimental realization of a superdispersion-enabled ultrabroadband terahertz cloak. Adv. Mater., 34, 2205053(2022).

[41] C. Qian et al. Breaking the fundamental scattering limit with gain metasurfaces. Nat. Commun., 13, 4383(2022).

[42] J. Zhang et al. Heterogeneous transfer-learning-enabled diverse metasurface design. Adv. Opt. Mater., 10, 2200748(2022).

[43] Z. Chen, Y. Liu, H. Sun. Physics-informed learning of governing equations from scarce data. Nat. Commun., 12, 6136(2021).

[44] C. Qian et al. Dynamic recognition and mirage using neuro-metamaterials. Nat. Commun., 13, 2694(2022).

[45] F. Succetti et al. A fast deep learning technique for Wi-Fi-based human activity recognition. Prog. Electromagn. Res., 174, 127-141(2022).

[46] R. Napolitano, W. Reinhart, J. P. Gevaudan. Smart cities built with smart materials. Science, 371, 1200-1201(2021).

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Chao Qian, Yuetian Jia, Zhedong Wang, Jieting Chen, Pujing Lin, Xiaoyue Zhu, Erping Li, Hongsheng Chen, "Autonomous aeroamphibious invisibility cloak with stochastic-evolution learning," Adv. Photon. 6, 016001 (2024)

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Paper Information

Category: Research Articles

Received: Sep. 20, 2023

Accepted: Dec. 14, 2023

Published Online: Jan. 15, 2024

The Author Email: Qian Chao (chaoq@intl.zju.edu.cn), Chen Hongsheng (hansomchen@zju.edu.cn)

DOI:10.1117/1.AP.6.1.016001

CSTR:32187.14.1.AP.6.1.016001

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology