A Scattering-Mode-Decoupling-Enabled Intelligent Metasurface for Wide-Angle and Multiband Cloaking

Guo Bai; Jianming Liao; Yi Chen; Cheng Huang; Xiaoliang Ma; Xiangang Luo

doi:10.1016/j.eng.2026.07.038

Engineering ›› :202607038 DOI: 10.1016/j.eng.2026.07.038

research-article

A Scattering-Mode-Decoupling-Enabled Intelligent Metasurface for Wide-Angle and Multiband Cloaking

Guo Bai ^a^,^#
, Jianming Liao ^a^,^#
, Yi Chen ^a^,^b
, Cheng Huang ^a^,^b^,^*
, Xiaoliang Ma ^a^,^b^,^*
, Xiangang Luo ^a^,^b^,^*

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Abstract

Reconfigurable metasurfaces provide a promising approach for adaptive electromagnetic cloaking but still present critical challenges in predicting scattering responses with high accuracy and in optimizing performance across broad bandwidths and wide angular ranges, especially in the presence of scatterer-induced occlusions. In this work, we propose a physics-informed neural network (PINN)-enabled reconfigurable metasurface cloak that integrates a novel scattering-mode-decoupling theory (SMDT) to achieve accurate wideband and wide-angle prediction and optimization with only a limited amount of training data. The proposed framework resolves complex mutual coupling and occlusion effects, enabling a multi-frequency and multi-angle electromagnetic illusion using a single optimized spatially distributed bias configuration. As a result, the approach reduces dependence on high-precision angle and frequency sensors while alleviating the latency and performance degradation caused by frequent configuration switching. The experimental results demonstrate that the fabricated cloak effectively controls complex scattering responses across a broadband frequency range from 6.5 to 9.0 GHz and over wide angular spans up to ±40°, even under occlusion conditions. This study establishes a new benchmark for adaptive cloaking by enhancing scalability, improving robustness under dynamic environments, and expanding applicability to practical scenarios such as concealment and electromagnetic illusion.

Keywords

Reconfigurable metasurface / Electromagnetic cloaking / Physics-informed neural networks / Electromagnetic illusion / Wideband and wide-angle scattering

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Guo Bai, Jianming Liao, Yi Chen, Cheng Huang, Xiaoliang Ma, Xiangang Luo. A Scattering-Mode-Decoupling-Enabled Intelligent Metasurface for Wide-Angle and Multiband Cloaking. Engineering 202607038 DOI:10.1016/j.eng.2026.07.038

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References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Schurig D, Mock JJ, Justice BJ, Cummer SA, Pendry JB, Starr AF, et al. Metamaterial electromagnetic cloak at microwave frequencies. Science 2006; 314(5801):977—80.

[2]	Pendry JB, Schurig D, Smith DR . Controlling electromagnetic fields. Science 2006; 312(5781):1780-2.

[3]	Cui TJ, Qi MQ, Wan X, Zhao J, Cheng Q . Coding metamaterials, digital metamaterials and programmable metamaterials. Light Sci Appl 2014; 3(10):e218.

[4]	Liu R, Ji C, Mock JJ, Chin JY, Cui TJ, Smith DR . Broadband ground—plane cloak. Science 2009; 323(5912):366-9.

[5]	Xu HX, Hu G, Wang Y, Wang C, Wang M, Wang S, et al. Polarization—insensitive 3D conformal—skin metasurface cloak. Light Sci Appl 2021; 10(1):75.

[6]	Wang HL, Zhang YK, Cheng YT, Zhang TY, Zheng S, Cui TJ, et al. A self—adaptive switchable transparency/invisibility cloak based on programmable metasurface. Laser Photonics Rev 2025; 19(13);2500057.

[7]	Watts CM, Liu X, Padilla WJ . Metamaterial electromagnetic wave absorbers. Adv Mater 2012; 24(23):98-120.

[8]	Alù A, Engheta N . Achieving transparency with plasmonic and metamaterial coatings. Phys Rev E 2005; 72:016623.

[9]	Qian C, Jia Y, Wang Z, Chen J, Lin P, Zhu X, et al. Autonomous aeroamphibious invisibility cloak with stochastic—evolution learning. Adv Photonics 2024; 6(1):016001.

[10]	Huang C, Yang J, Wu X, Song J, Pu M, Wang C, et al. Reconfigurable metasurface cloak for dynamical electromagnetic illusions. ACS Photonics 2018;5(5):1718—25.

[11]	Qian C, Chen H . A perspective on the next generation of invisibility cloaks—intelligent cloaks. Appl Phys Lett 2021; 118(18):180501.

[12]	Qian C, Tian L, Chen H . Progress on intelligent metasurfaces for signal relay, transmitter, and processor. Light Sci Appl 2025; 14(1):93.

[13]	Qian C, Kaminer I, Chen H . A guidance to intelligent metamaterials and metamaterials intelligence. Nat Commun 2025; 16(1):1154.

[14]	Rao J, Zhang Y, Tang S, Li Z, Shen S, Chiu CY, et al. A novel reconfigurable intelligent surface for wide—angle passive beamforming. IEEE Trans Microw Theory Tech 2022; 70(12):5427—39.

[15]	Lyu Y, Gan TH, Chen ZN . TE—TM balanced wide—angle metacells for low scan—loss metalens antenna using prior knowledge—guided generative deep learning—enabled method. IEEE Trans Antennas Propag 2025; 73(5):2940—9.

[16]	Wang Z, Qian C, Lin P, Zheng B, Kim G, Noh J, et al. 3D intelligent cloaked vehicle equipped with thousand—level reconfigurable full—polarization metasurfaces. Adv Mater 2024; 36(31):2400797.

[17]	Jia Y, Qian C, Fan Z, Ding Y, Wang Z, Wang D, et al. In situ customized illusion enabled by global metasurface reconstruction. Adv Funct Mater 2022; 32(19):2109331.

[18]	Li W, Ma Q, Liu C, Zhang Y, Wu X, Wang J, et al. Intelligent metasurface system for automatic tracking of moving targets and wireless communications based on computer vision. Nat Commun 2023; 14(1):989.

[19]	Jia Y, Qian C, Fan Z, Cai T, Li EP, Chen H . A knowledge—inherited learning for intelligent metasurface design and assembly. Light Sci Appl 2023; 12(1):82.

[20]	Huang M, Zheng B, Li R, Shen L, Li X, Lu H, et al. Evolutionary games—assisted synchronization metasurface for simultaneous multisource invisibility cloaking. Adv Funct Mater 2024; 34(36):2401909.

[21]	Wen E, Yang X, Sievenpiper DF . Real—data—driven real—time reconfigurable microwave reflective surface. Nat Commun 2023; 14(1):7736.

[22]	Zhang JW, Dai JY, Wu GB, Lu YJ, Cao WW, Liang JC, et al. Low—cost surrogate modeling for expedited data acquisition of reconfigurable metasurfaces. Adv Mater Technol 2025; 10(4):2400850.

[23]	Qian C, Zheng B, Shen Y, Jing L, Li E, Shen L, et al. Deep—learning—enabled self—adaptive microwave cloak without human intervention. Nat Photonics 2020; 14(6):383-90.

[24]	Chen Z, Liu Y, Sun H . Physics—informed learning of governing equations from scarce data. Nat Commun 2021; 12(1):6136.

[25]	Carleo G, Cirac I, Cranmer K, Daudet L, Schuld M, Tishby N, et al. Machine learning and the physical sciences. Rev Mod Phys 2019; 91(4):045002.

[26]	Zhang Z, Zhang JW, Wu JW, Liang JC, Wang ZX, Cheng Q, et al. Macromodeling of reconfigurable intelligent surface based on microwave network theory. IEEE Trans Antennas Propag 2022; 70(10):8707—17.

[27]	Wei Z, Zhou Z, Wang P, Ren J, Yin Y, Pedersen GF, et al. Equivalent circuit theory—assisted deep learning for accelerated generative design of metasurfaces. IEEE Trans Antenn Propag 2022; 70(7):5120—9.

[28]	Xu Y, Yang JQ, Fan K, Wang S, Wu J, Zhang C, et al. Physics—informed inverse design of programmable metasurfaces. Adv Sci 2024; 11(41):2406878.

[29]	Liu Y, Sarris CD . A convolutional neural network based method for accurate computation of scattered fields from reconfigurable intelligent surfaces. IEEE Trans Antennas Propag 2025; 73(4):2615-27.

[30]	Zhen Z, Qian C, Jia Y, Fan Z, Hao R, Cai T, et al. Realizing transmitted metasurface cloak by a tandem neural network. Photon Res 2021; 9(5):B229—35.

[31]	Gao L, Li X, Liu D, Wang L, Yu Z . A bidirectional deep neural network for accurate silicon color design. Adv Mater 2019; 31(51):1905467.

[32]	Liu D, Tan Y, Khoram E, Yu Z . Training deep neural networks for the inverse design of nanophotonic structures. ACS Photonics 2018; 5(4):1365—9.

[33]	Liao J, Ji C, Yuan L, Huang C, Wang Y, Peng J, et al. Polarization—insensitive metasurface cloak for dynamic illusions with an electromagnetic transparent window. ACS Appl Mater Interfaces 2023; 15(13):16953—62.

[34]	Li H, Li YB, Gong CY, Dong SY, Wang SY, Wang HP, et al. Design of programmable transmitarray antenna with independent controls of transmission amplitude and phase. IEEE Trans Antennas Propag 2022; 70(9):8086-99.