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    Journals >Advanced Photonics >Volume 7 >Issue 3 >Page 034005 > Article
    • Advanced Photonics
    • Vol. 7, Issue 3, 034005 (2025)
    Deep learning in metasurfaces: from automated design to adaptive metadevices
    Yasir Saifullah1,2,3,†, Nanxuan Wu1, Huaping Wang4, Bin Zheng1,2,3..., Chao Qian1,* and Hongsheng Chen1,2,3,*|Show fewer author(s)
    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, Zhejiang Key Laboratory of Intelligent Electromagnetic Control and Advanced Electronic Integration, Hangzhou, China
  • 3Zhejiang University, Jinhua Institute of Zhejiang University, Jinhua, China
  • 4Zhejiang University, Institute of Marine Electronics Engineering, Ocean College, Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province, Hangzhou, China
    • show less
    DOI: 10.1117/1.AP.7.3.034005 Cite this Article Set citation alerts
    Yasir Saifullah, Nanxuan Wu, Huaping Wang, Bin Zheng, Chao Qian, Hongsheng Chen, "Deep learning in metasurfaces: from automated design to adaptive metadevices," Adv. Photon. 7, 034005 (2025) Copy Citation Text show less
    References

    [1] D. Schurig et al. Metamaterial electromagnetic cloak at microwave frequencies. Science, 314, 977-980(2006). https://doi.org/10.1126/science.1133628

    [2] X. Ni et al. An ultrathin invisibility skin cloak for visible light. Science, 349, 1310-1314(2015). https://doi.org/10.1126/science.aac9411

    [3] C. Qian et al. Deep-learning-enabled self-adaptive microwave cloak without human intervention. Nat. Photonics, 14, 383-390(2020). https://doi.org/10.1038/s41566-020-0604-2

    [4] A. Silva et al. Performing mathematical operations with metamaterials. Science, 343, 160-163(2014). https://doi.org/10.1126/science.1242818

    [5] H. Yang et al. Off-resonance spin-locked metasurfaces empowered by quasi-bound states in the continuum for optical analog computing. Adv. Funct. Mater., 33, 2305110(2023). https://doi.org/10.1002/adfm.202305110

    [6] P. Fu et al. Reconfigurable metamaterial processing units that solve arbitrary linear calculus equations. Nat. Commun., 15, 6258(2024). https://doi.org/10.1038/s41467-024-50483-x

    [7] M. Deng et al. Broadband angular spectrum differentiation using dielectric metasurfaces. Nat. Commun., 15, 2237(2024). https://doi.org/10.1038/s41467-024-46537-9

    [8] D. C. Tzarouchis, B. Edwards, N. Engheta. Programmable wave-based analog computing machine: a metastructure that designs metastructures. Nat. Commun., 16, 908(2025). https://doi.org/10.1038/s41467-025-56019-1

    [9] K. Wang et al. Quantum metasurface for multiphoton interference and state reconstruction. Science, 361, 1104-1108(2018). https://doi.org/10.1126/science.aat8196

    [10] A. S. Solntsev, G. S. Agarwal, Y. S. Kivshar. Metasurfaces for quantum photonics. Nat. Photonics, 15, 327-336(2021). https://doi.org/10.1038/s41566-021-00793-z

    [11] T. Santiago-Cruz et al. Resonant metasurfaces for generating complex quantum states. Science, 377, 991-995(2022). https://doi.org/10.1126/science.abq8684

    [12] C. Huang et al. Reconfigurable intelligent surfaces for energy efficiency in wireless communication. IEEE Trans. Wireless Commun., 18, 4157-4170(2019). https://doi.org/10.1109/TWC.2019.2922609

    [13] E. Basar et al. Wireless communications through reconfigurable intelligent surfaces. IEEE Access, 7, 116753-116773(2019). https://doi.org/10.1109/ACCESS.2019.2935192

    [14] M. Di Renzo et al. Smart radio environments empowered by reconfigurable intelligent surfaces: how it works, state of research, and the road ahead. IEEE J. Sel. Areas Commun., 38, 2450-2525(2020). https://doi.org/10.1109/JSAC.2020.3007211

    [15] W. Li et al. Intelligent metasurface system for automatic tracking of moving targets and wireless communications based on computer vision. Nat. Commun., 14, 989(2023). https://doi.org/10.1038/s41467-023-36645-3

    [16] H. Takeshita et al. Frequency-hopping wave engineering with metasurfaces. Nat. Commun., 15, 196(2024). https://doi.org/10.1038/s41467-023-44627-8

    [17] J. C. Liang et al. A filtering reconfigurable intelligent surface for interference-free wireless communications. Nat. Commun., 15, 3838(2024). https://doi.org/10.1038/s41467-024-47865-6

    [18] H. W. Tian et al. Solar-powered light-modulated microwave programmable metasurface for sustainable wireless communications. Nat. Commun., 16, 2524(2025). https://doi.org/10.1038/s41467-025-57923-2

    [19] A. V. Kildishev, A. Boltasseva, V. M. Shalaev. Planar photonics with metasurfaces. Science, 339, 1232009(2013). https://doi.org/10.1126/science.1232009

    [20] N. Yu, F. Capasso. Flat optics with designer metasurfaces. Nat. Mater., 13, 139-150(2014). https://doi.org/10.1038/nmat3839

    [21] A. Arbabi et al. Dielectric metasurfaces for complete control of phase and polarization with subwavelength spatial resolution and high transmission. Nat. Nanotechnol., 10, 937-943(2015). https://doi.org/10.1038/nnano.2015.186

    [22] W. T. Chen, A. Y. Zhu, F. Capasso. Flat optics with dispersion-engineered metasurfaces. Nat. Rev. Mater., 5, 604-620(2020). https://doi.org/10.1038/s41578-020-0203-3

    [23] G. He et al. Twisted metasurfaces for on-demand focusing localization. Adv. Opt. Mater., 13, 2401933(2025). https://doi.org/10.1002/adom.202401933

    [24] Y. Yao et al. Electrically tunable metasurface perfect absorbers for ultrathin mid-infrared optical modulators. Nano Lett., 14, 6526-6532(2014). https://doi.org/10.1021/nl503104n

    [25] J. Sautter et al. Active tuning of all-dielectric metasurfaces. ACS Nano, 9, 4308-4315(2015). https://doi.org/10.1021/acsnano.5b00723

    [26] Q. Xu et al. Mechanically reprogrammable Pancharatnam–Berry metasurface for microwaves. Adv. Photonics, 4, 016002(2022). https://doi.org/10.1117/1.AP.4.1.016002

    [27] X. Zhu et al. Negative conductivity induced reconfigurable gain metasurfaces and their nonlinearity. Phys. Rev. Lett., 133, 113801(2024). https://doi.org/10.1103/PhysRevLett.133.113801

    [28] H. Lu et al. Eye accommodation-inspired neuro-metasurface focusing. Nat. Commun., 14, 3301(2023). https://doi.org/10.1038/s41467-023-39070-8

    [29] E. Wen, X. Yang, D. F. Sievenpiper. Real-data-driven real-time reconfigurable microwave reflective surface. Nat. Commun., 14, 7736(2023). https://doi.org/10.1038/s41467-023-43473-y

    [30] X. Y. Wu et al. An ultrathin, fast-response, large-scale liquid-crystal-facilitated multi-functional reconfigurable metasurface for comprehensive wavefront modulation. Adv. Mater., 36, 2402170(2024). https://doi.org/10.1002/adma.202402170

    [31] J. Wu et al. Terahertz metamaterial sensor with ultra-high sensitivity and tunability based on photosensitive semiconductor GaAs. IEEE Sens. J., 22, 15961-15966(2022). https://doi.org/10.1109/JSEN.2022.3190414

    [32] X. Zhao et al. Optically modulated ultra-broadband all-silicon metamaterial terahertz absorbers. ACS Photonics, 6, 830-837(2019). https://doi.org/10.1021/acsphotonics.8b01644

    [33] W. He et al. Ultrafast all-optical terahertz modulation based on an inverse-designed metasurface. Photonics Res., 9, 1099-1108(2021). https://doi.org/10.1364/PRJ.423119

    [34] S. Baek et al. Non-Hermitian chiral degeneracy of gated graphene metasurfaces. Light Sci. Appl., 12, 87(2023). https://doi.org/10.1038/s41377-023-01121-6

    [35] Y. K. Srivastava et al. MoS2 for ultrafast all-optical switching and modulation of THz Fano metaphotonic devices. Adv. Opt. Mater., 5, 1700762(2017). https://doi.org/10.1002/adom.201700762

    [36] T. Guo et al. Durable and programmable ultrafast nanophotonic matrix of spectral pixels. Nat. Nanotechnol., 19, 1635-1643(2024). https://doi.org/10.1038/s41565-024-01756-5

    [37] I. Kim et al. Pixelated bifunctional metasurface-driven dynamic vectorial holographic color prints for photonic security platform. Nat. Commun., 12, 3614(2021). https://doi.org/10.1038/s41467-021-23814-5

    [38] T. Lewi et al. Ultrawide thermo-optic tuning of PbTe meta-atoms. Nano Lett., 17, 3940-3945(2017). https://doi.org/10.1021/acs.nanolett.7b01529

    [39] P. Hosseini, C. D. Wright, H. Bhaskaran. An optoelectronic framework enabled by low-dimensional phase-change films. Nature, 511, 206-211(2014). https://doi.org/10.1038/nature13487

    [40] R. Kaissner et al. Electrochemically controlled metasurfaces with high-contrast switching at visible frequencies. Sci. Adv., 7, eabd9450(2021). https://doi.org/10.1126/sciadv.abd9450

    [41] G. K. Shirmanesh et al. Electro-optically tunable multifunctional metasurfaces. ACS Nano, 14, 6912-6920(2020). https://doi.org/10.1021/acsnano.0c01269

    [42] K. Sun et al. Metasurface optical solar reflectors using AZO transparent conducting oxides for radiative cooling of spacecraft. ACS Photonics, 5, 495-501(2018). https://doi.org/10.1021/acsphotonics.7b00991

    [43] Z. Wang et al. Origami-based reconfigurable metamaterials for tunable chirality. Adv. Mater., 29, 1700412(2017). https://doi.org/10.1002/adma.201700412

    [44] E. Arbabi et al. MEMS-tunable dielectric metasurface lens. Nat. Commun., 9, 812(2018). https://doi.org/10.1038/s41467-018-03155-6

    [45] Q. Li et al. Metasurface optofluidics for dynamic control of light fields. Nat. Nanotechnol., 17, 1097-1103(2022). https://doi.org/10.1038/s41565-022-01197-y

    [46] S. Sun et al. Real-time tunable colors from microfluidic reconfigurable all-dielectric metasurfaces. ACS Nano, 12, 2151-2159(2018). https://doi.org/10.1021/acsnano.7b07121

    [47] H.-S. Ee, R. Agarwal. Tunable metasurface and flat optical zoom lens on a stretchable substrate. Nano Lett., 16, 2818-2823(2016). https://doi.org/10.1021/acs.nanolett.6b00618

    [48] X. Zhao et al. Electromechanically tunable metasurface transmission waveplate at terahertz frequencies. Optica, 5, 303-310(2018). https://doi.org/10.1364/OPTICA.5.000303

    [49] L. Li et al. Intelligent metasurface imager and recognizer. Light Sci. Appl., 8, 97(2019). https://doi.org/10.1038/s41377-019-0209-z

    [50] L. Li et al. Intelligent metasurfaces: control, communication and computing. eLight, 2, 7(2022). https://doi.org/10.1186/s43593-022-00013-3

    [51] 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). https://doi.org/10.1002/advs.202203747

    [52] Y. Jia et al. A knowledge-inherited learning for intelligent metasurface design and assembly. Light Sci. Appl., 12, 82(2023). https://doi.org/10.1038/s41377-023-01131-4

    [53] C. Qian, I. Kaminer, H. Chen. A guidance to intelligent metamaterials and metamaterials intelligence. Nat. Commun., 16, 1154(2025). https://doi.org/10.1038/s41467-025-56122-3

    [54] Z. Liu et al. Generative model for the inverse design of metasurfaces. Nano Lett., 18, 6570-6576(2018). https://doi.org/10.1021/acs.nanolett.8b03171

    [55] T. Qiu et al. Deep learning: a rapid and efficient route to automatic metasurface design. Adv. Sci., 6, 1900128(2019). https://doi.org/10.1002/advs.201900128

    [56] W. Ma et al. Deep learning for the design of photonic structures. Nat. Photonics, 15, 77-90(2021). https://doi.org/10.1038/s41566-020-0685-y

    [57] J. Zhang et al. Uncertainty qualification for metasurface design with amendatory Bayesian network. Laser Photonics Rev., 17, 2200807(2023). https://doi.org/10.1002/lpor.202200807

    [58] C. Qian, L. Tian, H. Chen. Progress on intelligent metasurfaces for signal relay, transmitter, and processor. Light Sci. Appl., 14, 93(2025). https://doi.org/10.1038/s41377-024-01729-2

    [59] J. Zhao et al. Deep-learning-assisted simultaneous target sensing and super-resolution imaging. ACS Appl. Mater. Interfaces, 15, 47669-47681(2023). https://doi.org/10.1021/acsami.3c07812

    [60] T. Mikolov et al. Extensions of recurrent neural network language model, 5528-5531(2011). https://doi.org/10.1109/ICASSP.2011.5947611

    [61] W. Ma et al. Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy. Adv. Mater., 31, 1901111(2019). https://doi.org/10.1002/adma.201901111

    [62] D. A. Boiko et al. Autonomous chemical research with large language models. Nature, 624, 570-578(2023). https://doi.org/10.1038/s41586-023-06792-0

    [63] W. S. McCulloch, W. Pitts. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys., 5, 115-133(1943). https://doi.org/10.1007/BF02478259

    [64] Y. LeCun, Y. Bengio, G. Hinton. Deep learning. Nature, 521, 436-444(2015). https://doi.org/10.1038/nature14539

    [65] C. Qian et al. Performing optical logic operations by a diffractive neural network. Light Sci. Appl., 9, 59(2020). https://doi.org/10.1038/s41377-020-0303-2

    [66] M. Erdmann et al. Deep Learning for Physics Research(2021).

    [67] R. S. Hegde. Deep learning: a new tool for photonic nanostructure design. Nanoscale Adv., 2, 1007-1023(2020). https://doi.org/10.1039/C9NA00656G

    [68] P. R. Wiecha et al. Deep learning in nano-photonics: inverse design and beyond. Photonics Res., 9, B182-B200(2021). https://doi.org/10.1364/PRJ.415960

    [69] S. So et al. Deep learning enabled inverse design in nanophotonics. Nanophotonics, 9, 1041-1057(2020). https://doi.org/10.1515/nanoph-2019-0474

    [70] O. Khatib et al. Deep learning the electromagnetic properties of metamaterials—a comprehensive review. Adv. Funct. Mater., 31, 2101748(2021). https://doi.org/10.1002/adfm.202101748

    [71] Z. Fan et al. Holographic multiplexing metasurface with twisted diffractive neural network. Nat. Commun., 15, 9416(2024). https://doi.org/10.1038/s41467-024-53749-6

    [72] C. Qian, H. Chen. The rise of intelligent adaptive metasurfaces. J. Opt., 27, 012501(2024). https://doi.org/10.1088/2040-8986/ad97c9

    [73] Q. Tan, C. Qian, H. Chen. Inverse-designed metamaterials for on-chip combinational optical logic circuit. Prog. Electromagn. Res., 176, 55-65(2023). https://doi.org/10.2528/PIER22091502

    [74] Y. Jia et al. Dynamic inverse design of broadband metasurfaces with synthetical neural networks. Laser Photonics Rev., 18, 2400063(2024). https://doi.org/10.1002/lpor.202400063

    [75] J. Zhang et al. Harnessing the missing spectral correlation for metasurface inverse design. Adv. Sci., 11, 2308807(2024). https://doi.org/10.1002/advs.202308807

    [76] D. Blanco, R. Sauleau. Broadband and broad-angle multilayer polarizer based on hybrid optimization algorithm for low-cost Ka-band applications. IEEE Trans. Antennas Propag., 66, 1874-1881(2018). https://doi.org/10.1109/TAP.2018.2804618

    [77] J. Peurifoy et al. Nanophotonic particle simulation and inverse design using artificial neural networks. Sci. Adv., 4, eaar4206(2018). https://doi.org/10.1126/sciadv.aar4206

    [78] I. Malkiel et al. Plasmonic nanostructure design and characterization via deep learning. Light Sci. Appl., 7, 60(2018). https://doi.org/10.1038/s41377-018-0060-7

    [79] Z. Gu et al. High-resolution programmable metasurface imager based on multilayer perceptron network. Adv. Opt. Photonics, 10, 2200619(2022). https://doi.org/10.1002/adom.202200619

    [80] G.-X. Liu et al. Inverse design in quantum nanophotonics: combining local-density-of-states and deep learning. Nanophotonics, 12, 1943-1955(2023). https://doi.org/10.1515/nanoph-2022-0746

    [81] K. Donda et al. Ultrathin acoustic absorbing metasurface based on deep learning approach. Smart Mater. Struct., 30, 085003(2021). https://doi.org/10.1088/1361-665X/ac0675

    [82] X. Shi et al. Metasurface inverse design using machine learning approaches. J. Phys. D: Appl. Phys., 53, 275105(2020). https://doi.org/10.1088/1361-6463/ab8036

    [83] S. An et al. Deep convolutional neural networks to predict mutual coupling effects in metasurfaces. Adv. Opt. Photonics, 10, 2102113(2022). https://doi.org/10.1002/adom.202102113

    [84] R. Lin et al. Inverse design of plasmonic metasurfaces by convolutional neural network. Opt. Lett., 45, 1362-1365(2020). https://doi.org/10.1364/OL.387404

    [85] Y. Jia et al. In situ customized illusion enabled by global metasurface reconstruction. Adv. Funct. Mater., 32, 2109331(2022). https://doi.org/10.1002/adfm.202109331

    [86] G. Van Houdt, C. Mosquera, G. Nápoles. A review on the long short-term memory model. Artif. Intell. Rev., 53, 5929-5955(2020). https://doi.org/10.1007/s10462-020-09838-1

    [87] W. Deng et al. Long short-term memory neural network for directly inverse design of nanofin metasurface. Opt. Lett., 47, 3239-3242(2022). https://doi.org/10.1364/OL.458453

    [88] T. W. Hughes et al. Wave physics as an analog recurrent neural network. Sci. Adv., 5, eaay6946(2019). https://doi.org/10.1126/sciadv.aay6946

    [89] I. Sajedian, J. Kim, J. Rho. Finding the optical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks. Microsyst. Nanoeng., 5, 27(2019). https://doi.org/10.1038/s41378-019-0069-y

    [90] P. Pillai et al. Leveraging long short-term memory (LSTM)-based neural networks for modeling structure–property relationships of metamaterials from electromagnetic responses. Sci. Rep., 11, 18629(2021). https://doi.org/10.1038/s41598-021-97999-6

    [91] R. Yan et al. Efficient inverse design and spectrum prediction for nanophotonic devices based on deep recurrent neural networks. Nanotechnology, 32, 335201(2021). https://doi.org/10.1088/1361-6528/abff8d

    [92] J. Zhang, C. Zong. Deep neural networks in machine translation: an overview. IEEE Intell. Syst., 30, 16-25(2015). https://doi.org/10.1109/MIS.2015.69

    [93] Z. Yang et al. Metasurfaces designed by a bidirectional deep neural network and iterative algorithm for generating quantitative field distributions. Light: Adv. Manuf., 4, 104-114(2023). https://doi.org/10.37188/lam.2023.009

    [94] Z. A. Kudyshev et al. Machine-learning-assisted metasurface design for high-efficiency thermal emitter optimization. Appl. Phys. Rev., 7, 021407(2019). https://doi.org/10.1063/1.5134792

    [95] Z. Liu et al. A hybrid strategy for the discovery and design of photonic structures. IEEE J. Emerg. Sel. Top. Circuits Syst., 10, 126-135(2020). https://doi.org/10.1109/JETCAS.2020.2970080

    [96] Z. Li et al. Empowering metasurfaces with inverse design: principles and applications. ACS Photonics, 9, 2178-2192(2022). https://doi.org/10.1021/acsphotonics.1c01850

    [97] S. Chen, W. Guo. Auto-encoders in deep learning—a review with new perspectives. Mathematics, 11, 1777(2023). https://doi.org/10.3390/math11081777

    [98] A. Aggarwal, M. Mittal, G. Battineni. Generative adversarial network: an overview of theory and applications. Int. J. Inform. Manage. Data Insights, 1, 100004(2021). https://doi.org/10.1016/j.jjimei.2020.100004

    [99] Z. Liu et al. Compounding meta-atoms into metamolecules with hybrid artificial intelligence techniques. Adv. Mater., 32, 1904790(2020). https://doi.org/10.1002/adma.201904790

    [100] C. Tan et al. Artificial neural networks and machine learning–ICANN. Lect. Notes Comput. Sci., 1140, 270-279(2018). https://doi.org/10.1007/978-3-030-01421-6

    [101] R. Zhu et al. Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning. Nat. Commun., 12, 2974(2021). https://doi.org/10.1038/s41467-021-23087-y

    [102] Z. Fan et al. Transfer-learning-assisted inverse metasurface design for 30% data savings. Phys. Rev. Appl., 18, 024022(2022). https://doi.org/10.1103/PhysRevApplied.18.024022

    [103] J. Zhang et al. Heterogeneous transfer-learning-enabled diverse metasurface design. Adv. Opt. Photonics, 10, 2200748(2022). https://doi.org/10.1002/adom.202200748

    [104] K. Arulkumaran et al. Deep reinforcement learning: a brief survey. IEEE Signal Process. Mag., 34, 26-38(2017). https://doi.org/10.1109/MSP.2017.2743240

    [105] T. Badloe, I. Kim, J. Rho. Biomimetic ultra-broadband perfect absorbers optimised with reinforcement learning. Phys. Chem. Chem. Phys., 22, 2337-2342(2020). https://doi.org/10.1039/C9CP05621A

    [106] H. Lu et al. Soft actor–critic-driven adaptive focusing under obstacles. Materials, 16, 1366(2023). https://doi.org/10.3390/ma16041366

    [107] D. Liu et al. Training deep neural networks for the inverse design of nanophotonic structures. ACS Photonics, 5, 1365-1369(2018). https://doi.org/10.1021/acsphotonics.7b01377

    [108] Z. Zhen et al. Realizing transmitted metasurface cloak by a tandem neural network. Photonics Res., 9, B229-B235(2021). https://doi.org/10.1364/PRJ.418445

    [109] N. Wu et al. Pushing the limits of metasurface cloak using global inverse design. Adv. Opt. Photonics, 11, 2202130(2023). https://doi.org/10.1002/adom.202202130

    [110] J. Chen et al. Correlating metasurface spectra with a generation-elimination framework. Nat. Commun., 14, 4872(2023). https://doi.org/10.1038/s41467-023-40619-w

    [111] T. Cai et al. Experimental realization of a superdispersion-enabled ultrabroadband terahertz cloak. Adv. Mater., 34, 2205053(2022). https://doi.org/10.1002/adma.202205053

    [112] P. Lin et al. Enabling intelligent metasurfaces for semi-known input. Prog. Electromagn. Res., 178, 83-91(2023). https://doi.org/10.2528/PIER23090201

    [113] P. Lin et al. Assembling reconfigurable intelligent metasurfaces with synthetic neural network. IEEE Trans. Antennas Propag., 72, 5252-5260(2024). https://doi.org/10.1109/TAP.2024.3395909

    [114] Z. K. Lawal et al. Physics-informed neural network (PINN) evolution and beyond: a systematic literature review and bibliometric analysis. Big Data Cognit. Comput., 6, 140(2022). https://doi.org/10.3390/bdcc6040140

    [115] J. Jiang, J. A. Fan. Global optimization of dielectric metasurfaces using a physics-driven neural network. Nano Lett., 19, 5366-5372(2019). https://doi.org/10.1021/acs.nanolett.9b01857

    [116] M. Raissi, P. Perdikaris, G. E. Karniadakis. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys., 378, 686-707(2019). https://doi.org/10.1016/j.jcp.2018.10.045

    [117] Y. Chen et al. Physics-informed neural networks for inverse problems in nano-optics and metamaterials. Opt. Express, 28, 11618-11633(2020). https://doi.org/10.1364/OE.384875

    [118] Z. Fang, J. Zhan. Deep physical informed neural networks for metamaterial design. IEEE Access, 8, 24506-24513(2020). https://doi.org/10.1109/ACCESS.2019.2963375

    [119] G. You et al. Driving deep-learning-based metasurface design with Kramers-Kronig relations. Phys. Rev. Appl., 22, L041002(2024). https://doi.org/10.1103/PhysRevApplied.22.L041002

    [120] A. Alù, N. Engheta. Achieving transparency with plasmonic and metamaterial coatings. Phys. Rev. E, 72, 016623(2005). https://doi.org/10.1103/PhysRevE.72.016623

    [121] U. Leonhardt. Optical conformal mapping. Science, 312, 1777-1780(2006). https://doi.org/10.1126/science.1126493

    [122] W. Cai et al. Optical cloaking with metamaterials. Nat. Photonics, 1, 224-227(2007). https://doi.org/10.1038/nphoton.2007.28

    [123] R. Liu et al. Broadband ground-plane cloak. Science, 323, 366-369(2009). https://doi.org/10.1126/science.1166949

    [124] T. Ergin et al. Three-dimensional invisibility cloak at optical wavelengths. Science, 328, 337-339(2010). https://doi.org/10.1126/science.1186351

    [125] H. F. Ma, T. J. Cui. Three-dimensional broadband ground-plane cloak made of metamaterials. Nat. Commun., 1, 21(2010). https://doi.org/10.1038/ncomms1023

    [126] X. Huang et al. Dirac cones induced by accidental degeneracy in photonic crystals and zero-refractive-index materials. Nat. Mater., 10, 582-586(2011). https://doi.org/10.1038/nmat3030

    [127] X. Chen et al. Macroscopic invisibility cloaking of visible light. Nat. Commun., 2, 176(2011). https://doi.org/10.1038/ncomms1176

    [128] N. Landy, D. R. Smith. A full-parameter unidirectional metamaterial cloak for microwaves. Nat. Mater., 12, 25-28(2013). https://doi.org/10.1038/nmat3476

    [129] B. Zheng et al. 3D Visible-light invisibility cloak. Adv. Sci., 5, 1800056(2018). https://doi.org/10.1002/advs.201800056

    [130] B. Zheng et al. Revealing the transformation invariance of full-parameter omnidirectional invisibility cloaks. Electromagn. Sci., 1, 0020092(2023). https://doi.org/10.23919/emsci.2023.0009

    [131] M. Huang et al. Evolutionary games-assisted synchronization metasurface for simultaneous multisource invisibility cloaking. Adv. Funct. Mater., 34, 2401909(2024). https://doi.org/10.1002/adfm.202401909

    [132] H.-X. Xu et al. Polarization-insensitive 3D conformal-skin metasurface cloak. Light Sci. Appl., 10, 75(2021).

    [133] S. Tretyakov et al. Broadband electromagnetic cloaking of long cylindrical objects. Phys. Rev. Lett., 103, 103905(2009). https://doi.org/10.1103/PhysRevLett.103.103905

    [134] B. Edwards et al. Experimental verification of plasmonic cloaking at microwave frequencies with metamaterials. Phys. Rev. Lett., 103, 153901(2009). https://doi.org/10.1103/PhysRevLett.103.153901

    [135] B. Orazbayev et al. Terahertz carpet cloak based on a ring resonator metasurface. Phys. Rev. B, 91, 195444(2015). https://doi.org/10.1103/PhysRevB.91.195444

    [136] M. Wei et al. Ultrathin metasurface-based carpet cloak for terahertz wave. Opt. Express, 25, 15635-15642(2017). https://doi.org/10.1364/OE.25.015635

    [137] A. Alù, N. Engheta. Multifrequency optical invisibility cloak with layered plasmonic shells. Phys. Rev. Lett., 100, 113901(2008). https://doi.org/10.1103/PhysRevLett.100.113901

    [138] J. Valentine et al. An optical cloak made of dielectrics. Nat. Mater., 8, 568-571(2009). https://doi.org/10.1038/nmat2461

    [139] L. H. Gabrielli et al. Silicon nanostructure cloak operating at optical frequencies. Nat. Photonics, 3, 461-463(2009). https://doi.org/10.1038/nphoton.2009.117

    [140] I. I. Smolyaninov et al. Anisotropic metamaterials emulated by tapered waveguides: application to optical cloaking. Phys. Rev. Lett., 102, 213901(2009). https://doi.org/10.1103/PhysRevLett.102.213901

    [141] B. Zhang et al. Macroscopic invisibility cloak for visible light. Phys. Rev. Lett., 106, 033901(2011). https://doi.org/10.1103/PhysRevLett.106.033901

    [142] M. Gharghi et al. A carpet cloak for visible light. Nano Lett., 11, 2825-2828(2011). https://doi.org/10.1021/nl201189z

    [143] H. Chen, B. Zheng. Broadband polygonal invisibility cloak for visible light. Sci. Rep., 2, 255(2012). https://doi.org/10.1038/srep00255

    [144] J. B. Pendry, D. Schurig, D. R. Smith. Controlling electromagnetic fields. Science, 312, 1780-1782(2006). https://doi.org/10.1126/science.1125907

    [145] B. Zheng et al. Concealing arbitrary objects remotely with multi-folded transformation optics. Light Sci. Appl., 5, e16177(2016). https://doi.org/10.1038/lsa.2016.177

    [146] J. Li, J. B. Pendry. Hiding under the carpet: a new strategy for cloaking. Phys. Rev. Lett., 101, 203901(2008). https://doi.org/10.1103/PhysRevLett.101.203901

    [147] P.-Y. Chen, A. Alu. Mantle cloaking using thin patterned metasurfaces. Phys. Rev. B, 84, 205110(2011). https://doi.org/10.1103/PhysRevB.84.205110

    [148] M. Selvanayagam, G. V. Eleftheriades. Experimental demonstration of active electromagnetic cloaking. Phys. Rev. X, 3, 041011(2013). https://doi.org/10.1103/PhysRevX.3.041011

    [149] H. Chen et al. Ray-optics cloaking devices for large objects in incoherent natural light. Nat. Commun., 4, 2652(2013). https://doi.org/10.1038/ncomms3652

    [150] S. Xu et al. Broadband surface-wave transformation cloak. Proc. Natl. Acad. Sci., 112, 7635-7638(2015). https://doi.org/10.1073/pnas.1508777112

    [151] Y. Yang et al. Full-polarization 3D metasurface cloak with preserved amplitude and phase. Adv. Mater., 28, 6866-6871(2016). https://doi.org/10.1002/adma.201600625

    [152] T. Chen et al. Direct current remote cloak for arbitrary objects. Light Sci. Appl., 8, 30(2019). https://doi.org/10.1038/s41377-019-0141-2

    [153] H. Chu et al. A hybrid invisibility cloak based on integration of transparent metasurfaces and zero-index materials. Light Sci. Appl., 7, 50(2018). https://doi.org/10.1038/s41377-018-0052-7

    [154] C. Qian et al. Autonomous aeroamphibious invisibility cloak with stochastic-evolution learning. Adv. Photonics, 6, 016001(2024). https://doi.org/10.1117/1.AP.6.1.016001

    [155] R. Peng et al. Temperature-controlled chameleonlike cloak. Phys. Rev. X, 7, 011033(2017). https://doi.org/10.1103/PhysRevX.7.011033

    [156] C. Huang et al. Reconfigurable metasurface cloak for dynamical electromagnetic illusions. ACS Photonics, 5, 1718-1725(2017). https://doi.org/10.1021/acsphotonics.7b01114

    [157] J. Liao et al. Polarization-insensitive metasurface cloak for dynamic illusions with an electromagnetic transparent window. ACS Appl. Mater. Interfaces, 15, 16953-16962(2023). https://doi.org/10.1021/acsami.2c21565

    [158] C. Qian, H. Chen. A perspective on the next generation of invisibility cloaks—intelligent cloaks. Appl. Phys. Lett., 118, 180501(2021). https://doi.org/10.1063/5.0049748

    [159] Z. Wang et al. 3D intelligent cloaked vehicle equipped with thousand-level reconfigurable full-polarization metasurfaces. Adv. Mater., 36, 2400797(2024). https://doi.org/10.1002/adma.202400797

    [160] W. Tang et al. Wireless communications with reconfigurable intelligent surface: path loss modeling and experimental measurement. IEEE Trans. Wireless Commun., 20, 421-439(2020). https://doi.org/10.1109/TWC.2020.3024887

    [161] L. Dai et al. Reconfigurable intelligent surface-based wireless communications: antenna design, prototyping, and experimental results. IEEE Access, 8, 45913-45923(2020). https://doi.org/10.1109/ACCESS.2020.2977772

    [162] M. Di Renzo et al. Reconfigurable intelligent surfaces vs. relaying: differences, similarities, and performance comparison. IEEE Open J. Commun. Soc., 1, 798-807(2020). https://doi.org/10.1109/OJCOMS.2020.3002955

    [163] Q. Wu, R. Zhang. Towards smart and reconfigurable environment: intelligent reflecting surface aided wireless network. IEEE Commun. Mag., 58, 106-112(2019). https://doi.org/10.1109/MCOM.001.1900107

    [164] Q. Wu, R. Zhang. Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming. IEEE Trans. Wireless Commun., 18, 5394-5409(2019). https://doi.org/10.1109/TWC.2019.2936025

    [165] Ö. Özdogan, E. Björnson, E. G. Larsson. Intelligent reflecting surfaces: physics, propagation, and pathloss modeling. IEEE Wireless Commun. Lett., 9, 581-585(2019). https://doi.org/10.1109/LWC.2019.2960779

    [166] M. Jian et al. Reconfigurable intelligent surfaces for wireless communications: overview of hardware designs, channel models, and estimation techniques. Intell. Converg. Netw., 3, 1-32(2022). https://doi.org/10.23919/ICN.2022.0005

    [167] S. Zeng et al. Reconfigurable intelligent surface (RIS) assisted wireless coverage extension: RIS orientation and location optimization. IEEE Commun. Lett., 25, 269-273(2020). https://doi.org/10.1109/LCOMM.2020.3025345

    [168] H. Tuan et al. RIS-aided multiple-input multiple-output broadcast channel capacity. IEEE Trans. Commun., 72, 117-132(2023). https://doi.org/10.1109/TCOMM.2023.3321909

    [169] W. Shi et al. On secrecy performance of RIS-assisted MISO systems over rician channels with spatially random eavesdroppers. IEEE Trans. Wireless Commun., 23, 8357-8371(2024). https://doi.org/10.1109/TWC.2023.3348591

    [170] L. Zhang et al. A wireless communication scheme based on space- and frequency-division multiplexing using digital metasurfaces. Nat. Electron., 4, 218-227(2021). https://doi.org/10.1038/s41928-021-00554-4

    [171] Q. Hu et al. Broadband wireless communication with space-time-varying polarization-converting metasurface. Nanophotonics, 12, 1327-1336(2023). https://doi.org/10.1515/nanoph-2023-0027

    [172] H. Zhao et al. Metasurface-assisted massive backscatter wireless communication with commodity Wi-Fi signals. Nat. Commun., 11, 3926(2020). https://doi.org/10.1038/s41467-020-17808-y

    [173] Y. Zheng et al. Metasurface-assisted wireless communication with physical level information encryption. Adv. Sci., 9, 2204558(2022). https://doi.org/10.1002/advs.202204558

    [174] Z. Fan et al. Homeostatic neuro-metasurfaces for dynamic wireless channel management. Sci. Adv., 8, eabn7905(2022). https://doi.org/10.1126/sciadv.abn7905

    [175] J. Xu et al. In-situ manipulation of wireless link with reinforcement-learning-driven programmable metasurface in indoor environment. J. Inform. Intell., 1, 217-227(2023). https://doi.org/10.1016/j.jiixd.2023.06.007

    [176] Z. Fan et al. Spatial multiplexing encryption with cascaded metasurfaces. J. Opt., 25, 125105(2023). https://doi.org/10.1088/2040-8986/ad0659

    [177] N. Ullah et al. Gate-controlled terahertz focusing based on graphene-loaded metasurface. Opt. Express, 28, 2789-2798(2020). https://doi.org/10.1364/OE.381765

    [178] F. Deng et al. Bessel beam generated by the zero-index metalens. Prog. Electromagn. Res., 174, 89-106(2022). https://doi.org/10.2528/PIER22050401

    [179] X. An et al. Broadband achromatic metalens design based on deep neural networks. Opt. Lett., 46, 3881-3884(2021). https://doi.org/10.1364/OL.427221

    [180] W.-Q. Chen et al. Nearly dispersionless multicolor metasurface beam deflector for near eye display designed by a physics-driven deep neural network. Appl. Opt., 60, 3947-3953(2021). https://doi.org/10.1364/AO.421901

    [181] Z. Li et al. Inverse design enables large-scale high-performance meta-optics reshaping virtual reality. Nat. Commun., 13, 2409(2022). https://doi.org/10.1038/s41467-022-29973-3

    [182] S. Latif et al. Spin-selective angular dispersion control in dielectric metasurfaces for multichannel meta-holographic displays. Nano Lett., 24, 708-714(2024). https://doi.org/10.1021/acs.nanolett.3c04064

    [183] B. Yang et al. Deep-learning-based colorimetric polarization-angle detection with metasurfaces. Optica, 9, 217-220(2022). https://doi.org/10.1364/OPTICA.449893

    [184] J. Xiong et al. Dynamic brain spectrum acquired by a real-time ultraspectral imaging chip with reconfigurable metasurfaces. Optica, 9, 461-468(2022). https://doi.org/10.1364/OPTICA.440013

    [185] P. Zheng et al. Metasurface-based key for computational imaging encryption. Sci. Adv., 7, eabg0363(2021). https://doi.org/10.1126/sciadv.abg0363

    [186] C. Qian et al. Dynamic recognition and mirage using neuro-metamaterials. Nat. Commun., 13, 2694(2022). https://doi.org/10.1038/s41467-022-30377-6

    [187] P. Del Hougne et al. Learned integrated sensing pipeline: reconfigurable metasurface transceivers as trainable physical layer in an artificial neural network. Adv. Sci., 7, 1901913(2020). https://doi.org/10.1002/advs.201901913

    [188] M. Huang et al. Diffraction neural network for multi-source information of arrival sensing. Laser Photonics Rev., 17, 2300202(2023). https://doi.org/10.1002/lpor.202300202

    [189] H.-Y. Li et al. Intelligent electromagnetic sensing with learnable data acquisition and processing. Patterns, 1, 100006(2020). https://doi.org/10.1016/j.patter.2020.100006

    [190] L. Sun et al. A pixelated frequency-agile metasurface for broadband terahertz molecular fingerprint sensing. Nanoscale, 14, 9681-9685(2022). https://doi.org/10.1039/D2NR01561G

    [191] Z. Zhang et al. Advanced terahertz refractive sensing and fingerprint recognition through metasurface-excited surface waves. Adv. Mater., 36, 2308453(2024). https://doi.org/10.1002/adma.202308453

    [192] I. Kim et al. Nanophotonics for light detection and ranging technology. Nat. Nanotechnol., 16, 508-524(2021). https://doi.org/10.1038/s41565-021-00895-3

    [193] R. Juliano Martins et al. Metasurface-enhanced light detection and ranging technology. Nat. Commun., 13, 5724(2022). https://doi.org/10.1038/s41467-022-33450-2

    [194] E. Marinov et al. Overcoming the limitations of 3D sensors with wide field of view metasurface-enhanced scanning lidar. Adv. Photonics, 5, 046005(2023). https://doi.org/10.1117/1.AP.5.4.046005

    [195] J. Zhou et al. Optical edge detection based on high-efficiency dielectric metasurface. Proc. Natl. Acad. Sci., 116, 11137-11140(2019). https://doi.org/10.1073/pnas.1820636116

    [196] P. Huo et al. Photonic spin-multiplexing metasurface for switchable spiral phase contrast imaging. Nano Lett., 20, 2791-2798(2020). https://doi.org/10.1021/acs.nanolett.0c00471

    [197] Y. Zhou et al. Flat optics for image differentiation. Nat. Photonics, 14, 316-323(2020). https://doi.org/10.1038/s41566-020-0591-3

    [198] Y. Shou et al. Deep learning approach based optical edge detection using ENZ layers. Prog. Electromagn. Res., 175, 81-89(2022). https://doi.org/10.2528/PIER22061403

    [199] I. Kim et al. Holographic metasurface gas sensors for instantaneous visual alarms. Sci. Adv., 7, eabe9943(2021). https://doi.org/10.1126/sciadv.abe9943

    [200] D. Rodrigo et al. Resolving molecule-specific information in dynamic lipid membrane processes with multi-resonant infrared metasurfaces. Nat. Commun., 9, 2160(2018). https://doi.org/10.1038/s41467-018-04594-x

    [201] A. John-Herpin et al. Infrared metasurface augmented by deep learning for monitoring dynamics between all major classes of biomolecules. Adv. Mater., 33, 2006054(2021). https://doi.org/10.1002/adma.202006054

    [202] O. Wu et al. General characterization of intelligent metasurfaces with graph coupling network. Laser Photonics Rev., 19, 2400979(2025). https://doi.org/10.1002/lpor.202400979

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    Yasir Saifullah, Nanxuan Wu, Huaping Wang, Bin Zheng, Chao Qian, Hongsheng Chen, "Deep learning in metasurfaces: from automated design to adaptive metadevices," Adv. Photon. 7, 034005 (2025)
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