[1] J. B. Pendry et al. Extremely low frequency plasmons in metallic mesostructures. Phys. Rev. Lett., 76, 4773(1996).

[2] J. B. Pendry et al. Magnetism from conductors and enhanced nonlinear phenomena. IEEE Trans. Microw. Theory Tech., 47, 2075(1999).

[3] V. G. Veselago. Electrodynamics of substances with simultaneously negative and values of ɛ and μ. Soviet Physics Uspekhi, 92, 517(1967).

[4] R. A. Shelby, D. R. Smith, S. Schultz. Experimental verification of a negative index of refraction. Science, 292, 77(2001).

[5] D. R. Smith, N. Kroll. Negative refractive index in left-handed materials. Phys. Rev. Lett., 85, 2933(2000).

[6] C. G. Parazzoli et al. Experimental verification and simulation of negative index of refraction using Snell’s law. Phys. Rev. Lett., 90, 107401(2003).

[7] I. V. Shadrivov, A. A. Sukhorukov, Y. S. Kivshar. Guided modes in negative-refractive-index waveguides. Phys. Rev. E, 67, 057602(2003).

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

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

[10] R. Liu et al. Broadband ground-plane cloak. Science, 323, 366(2009).

[11] F. Yang et al. DC electric invisibility cloak. Phys. Rev. Lett., 109, 053902(2012).

[12] H. F. Ma, T. J. Cui. Three-dimensional broadband and broad-angle transformation-optics lens. Nat. Commun., 1, 124(2010).

[13] F. M. Hui, T. J. Cui. Three-dimensional broadband ground-plane cloak made of metamaterials. Nat. Commun., 1, 21(2010).

[14] Q. Ma et al. Experiments on active cloaking and illusion for Laplace equation. Phys. Rev. Lett., 111, 173901(2013).

[15] Q. Ma et al. Open active cloaking and illusion devices for the Laplace equation. J. Opt., 18, 044004(2016).

[16] N. Fang et al. Sub-diffraction-limited optical imaging with a silver superlens. Science, 308, 534(2005).

[17] J. B. Pendry. Negative refraction makes a perfect lens. Phys. Rev. Lett., 85, 3966(2000).

[18] A. Grbic, G. V. Eleftheriades. Overcoming the diffraction limit with a planar left-handed transmission-line lens. Phys. Rev. Lett., 92, 117403(2004).

[19] W. X. Jiang et al. Broadband all-dielectric magnifying lens for far-field high-resolution imaging. Adv. Mater., 25, 6963(2013).

[20] Q. Ma et al. Broadband metamaterial lens antennas with special properties by controlling both refractive-index distribution and feed directivity. J. Opt., 20, 045101(2018).

[21] F. Falcone et al. Babinet principle applied to the design of metasurfaces and metamaterials. Phys. Rev. Lett., 93, 197401(2004).

[22] N. F. Yu et al. Light propagation with phase discontinuities: generalized laws of reflection and refraction. Science, 334, 333(2011).

[23] J. B. Pendry, L. Martin-Moreno, F. J. Garcia-Vidal. Mimicking surface plasmons with structured surfaces. Science, 305, 847(2004).

[24] A. Alu. Mantle cloak: invisibility induced by a surface. Phys. Rev. B, 80, 245115(2009).

[25] S. Sun et al. Gradient-index meta-surfaces as a bridge linking propagating waves and surface waves. Nat. Mater., 11, 426(2012).

[26] X. Ni et al. An ultrathin invisibility skin cloak for visible light. Science, 349, 1310(2015).

[27] X. Fang, H. Ren, M. Gu. Orbital angular momentum holography for high-security encryption. Nat. Photonics, 14, 102(2019).

[28] X. Ni, A. V. Kildishev, V. M. Shalaev. Metasurface holograms for visible light. Nat. Commun., 4, 2807(2013).

[29] K. K. Katare et al. Realization of split beam antenna using transmission-type coding metasurface and planar lens. IEEE Trans. Antennas Propag., 67, 2074-2084(2019).

[30] N. K. Grady et al. Terahertz metamaterials for linear polarization conversion and anomalous refraction. Science, 340, 1304-1307(2013).

[31] J. Hao et al. Manipulating electromagnetic wave polarizations by anisotropic metamaterials. Phys. Rev. Lett., 99(2007).

[32] L. Chen et al. Optically transparent metasurface absorber based on reconfigurable and flexible indium tin oxide film. Micromachines, 11, 1032(2020).

[33] J. Luo et al. 2-bit amplitude-modulated coding metasurfaces based on indium tin oxide films. J. Appl. Phys., 126, 113102(2019).

[34] L. Bao et al. Design of digital coding metasurfaces with independent controls of phase and amplitude responses. Appl. Phys. Lett., 113, 063502(2018).

[35] R. C. Devlin et al. Arbitrary spin-to-orbital angular momentum conversion of light. Science, 358, 896(2017).

[36] M. M. Salary, H. Mosallaei. Time-modulated conducting oxide metasurfaces for adaptive multiple access optical communication. IEEE Trans. Antennas Propag., 68, 1628(2020).

[37] M. Z. Chen et al. Accurate and broadband manipulations of harmonic amplitudes and phases to reach 256QAM millimeter-wave wireless communications by time-domain digital coding metasurface. Natl. Sci. Rev., 9, nwab134(2021).

[38] X. Chen et al. Design and implementation of MIMO transmission based on dual-polarized reconfigurable intelligent surface. IEEE Wireless Commun. Lett., 10, 2155(2021).

[39] J. Y. Dai et al. Realization of multi-modulation schemes for wireless communication by time-domain digital coding metasurface. IEEE Trans. Antennas Propag., 68, 1618(2020).

[40] W. Tang et al. MIMO transmission through reconfigurable intelligent surface: system design, analysis, and implementation. IEEE J. Sel. Areas Commun., 38, 2683(2020).

[41] K. H. Dou et al. Off-axis multi-wavelength dispersion controlling metalens for multi-color imaging. Opto-Electron. Adv., 3, 19000501(2020).

[42] P. C. Huo et al. Photonic spin-multiplexing metasurface for switchable spiral phase contrast imaging. Nano Lett., 20, 2791(2020).

[43] M. F. Imani et al. Review of metasurface antennas for computational microwave imaging. IEEE Trans. Antennas Propag., 68, 1860(2020).

[44] M. Zhou et al. Doubly curved reflectarray for dual-band multiple spot beam communication satellites. IEEE Trans. Antennas Propag., 68, 2087(2020).

[45] M.-T. Zhang et al. Design of novel reconfigurable reflectarrays with single-bit phase resolution for ku-band satellite antenna applications. IEEE Trans. Antennas Propag., 64, 1634(2016).

[46] A.-S. Kaddour et al. A foldable and reconfigurable monolithic reflectarray for space applications. IEEE Access, 8, 219355(2020).

[47] H. X. Li et al. Ultrathin acoustic parity-time symmetric metasurface cloak. Research, 2019, 8345683(2019).

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

[49] X. G. Zhang et al. Smart doppler cloak operating in broad band and full polarizations. Adv. Mater., 33, e2007966(2021).

[50] Z. Ullah et al. Dynamic absorption enhancement and equivalent resonant circuit modeling of tunable graphene-metal hybrid antenna. Sensors, 20, 3187(2020).

[51] P. del Hougne, M. Davy, U. Kuhl. Optimal multiplexing of spatially encoded information across custom-tailored configurations of a metasurface-tunable chaotic cavity. Phys. Rev. Appl., 13, 041004(2020).

[52] L. Lei et al. Tunable and scalable broadband metamaterial absorber involving VO₂-based phase transition. Photonics Res., 7, 734(2019).

[53] J. B. Reeves et al. Tunable infrared metasurface on a soft polymer scaffold. Nano Lett., 18, 2802(2018).

[54] P. Padmanabhan et al. A graphene-based magnetoplasmonic metasurface for actively tunable transmission and polarization rotation at terahertz frequencies. Appl. Phys. Lett., 116, 221107(2020).

[55] X. Chen et al. Electrically tunable perfect terahertz absorber based on a graphene salisbury screen hybrid metasurface. Adv. Opt. Mater., 8, 1900660(2020).

[56] J. Kappa et al. Electrically reconfigurable micromirror array for direct spatial light modulation ofterahertz waves over a bandwidth wider than 1 THz. Sci. Rep., 9, 2597(2019).

[57] X. J. He et al. Active modulation and switching of toroidal resonance in micromachined reconfigurable terahertz metamaterials. Results Phys., 17, 103133(2020).

[58] L. Chen, Y. Ruan, H. Y. Cui. Liquid metal metasurface for flexible beam-steering. Opt. Express, 27, 23282(2019).

[59] L. Chen et al. Light-controllable metasurface for microwave wavefront manipulation. Opt. Express, 28, 18742(2020).

[60] M. Manjappa et al. Reconfigurable MEMS Fano metasurfaces with multiple-input-output states for logic operations at terahertz frequencies. Nat. Commun., 9, 4056(2018).

[61] Y. Ruan et al. Optical transparent and reconfigurable metasurface with autonomous energy supply. J. Phys. D, 53, 065301(2019).

[62] H. Yang et al. A 1-bit 10 × 10 reconfigurable reflectarray antenna: design, optimization, and experiment. IEEE Trans. Antennas Propag., 64, 2246(2016).

[63] H. Yang et al. A 1600-element dual-frequency electronically reconfigurable reflectarray at X/Ku-band. IEEE Trans. Antennas Propag., 65, 3024(2017).

[64] Z. Tao et al. Reconfigurable conversions of reflection, transmission, and polarization states using active metasurface. Appl. Phys. Lett., 110, 121901(2017).

[65] C. Della Giovampaola, N. Engheta. Digital metamaterials. Nat. Mater., 13, 1115(2014).

[66] T. J. Cui et al. Coding metamaterials, digital metamaterials and programmable metamaterials. Light Sci. Appl., 3, e218(2014).

[67] Q. Ma et al. Beam-editing coding metasurfaces based on polarization bit and orbital-angular-momentum-mode bit. Adv. Opt. Mater., 5, 1700548(2017).

[68] T. J. Cui, S. Liu, L. Zhang. Information metamaterials and metasurfaces. J. Mater. Chem. C, 5, 3644(2017).

[69] Q. Ma, T. J. Cui. Information Metamaterials: bridging the physical world and digital world. PhotoniX, 1, 1(2020).

[70] T. J. Cui et al. Information metamaterial systems. iScience, 23, 101403(2020).

[71] L. Zhang et al. Dynamically realizing arbitrary multi-bit programmable phases using a 2-bit time-domain coding metasurface. IEEE Trans. Antennas Propag., 68, 2984(2020).

[72] X. Wan et al. Beam forming of leaky waves at fixed frequency using binary programmable metasurface. IEEE Trans. Antennas Propag., 66, 4942(2018).

[73] N. Zhang et al. Programmable coding metasurface for dual-band independent real-time beam control. IEEE J. Emerging Sel. Top. Circuits Syst., 10, 20(2020).

[74] Y. Shuang et al. Programmable high-order OAM-carrying beams for direct-modulation wireless communications. IEEE J. Emerging Sel. Top. Circuits Syst., 10, 29(2020).

[75] J. W. You et al. Reprogrammable plasmonic topological insulators with ultrafast control. Nat. Commun., 12, 5468(2021).

[76] G. D. Bai et al. Spin-symmetry breaking through metasurface geometric phases. Phys. Rev. Appl., 12, 044042(2019).

[77] Q. Xiao et al. Orbital-angular-momentum-encrypted holography based on coding information metasurface. Adv. Opt. Mater., 9, 2002155(2021).

[78] L. Chen et al. Space-energy digital-coding metasurface based on an active amplifier. Phys. Rev. Appl., 11, 040551(2019).

[79] L. Chen et al. Dual-polarization programmable metasurface modulator for near-field information encoding and transmission. Photonics Res., 9, 116(2021).

[80] Q. Ma et al. Editing arbitrarily linear polarizations using programmable metasurface. Phys. Rev. Appl., 13, 021003(2020).

[81] L. Zhang et al. A wireless communication scheme based on space- and frequency-division multiplexing using digital metasurfaces. Nat. Electron., 4, 218(2021).

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

[83] Q. Ma et al. Controllable and programmable nonreciprocity based on detachable digital coding metasurface. Adv. Opt. Mater., 7, 1901285(2019).

[84] L. Zhang et al. Breaking reciprocity with space-time-coding digital metasurfaces. Adv. Mater., 31, 1904069(2019).

[85] Q. Ma et al. Smart metasurface with self-adaptively reprogrammable functions. Light Sci. Appl., 8, 98(2019).

[86] Q. Ma et al. Smart sensing metasurface with self-defined functions in dual polarizations. Nanophotonics, 9, 3271-3278(2020).

[87] L. Li et al. Intelligent metasurface imager and recognizer. Light Sci. Appl., 8, 97(2019).

[88] C. Liu et al. Intelligent coding metasurface holograms by physics-assisted unsupervised generative adversarial network. Photonics Res., 9, B159(2021).

[89] L. Li et al. Machine-learning reprogrammable metasurface imager. Nat. Commun., 10, 1082(2019).

[90] H. Zhao et al. Metasurface-assisted massive backscatter wireless communication with commodity Wi-Fi signals. Nat. Commun., 11, 3926(2020).

[91] Q. R. Hong et al. Programmable amplitude-coding metasurface with multifrequency modulations. Adv. Intell. Syst. Comput., 3, 2000260(2021).

[92] H. Wu et al. Controlling energy radiations of electromagnetic waves via frequency coding metamaterials. Adv. Sci., 4, 1700098(2017).

[93] J. Liu et al. Terahertz information metamaterials and metasurfaces. J. Radars, 7, 46-55(2018).

[94] S. Liu et al. Anisotropic coding metamaterials and their powerful manipulation of differently polarized terahertz waves. Light Sci. Appl., 5, e16076(2016).

[95] S. Liu et al. Full-state controls of terahertz waves using tensor coding metasurfaces. ACS Appl. Mater. Interfaces, 9, 21503(2017).

[96] T.-J. Cui, S. Liu, L.-L. Li. Information entropy of coding metasurface. Light Sci. Appl., 5, e16172(2016).

[97] S. Liu et al. Convolution operations on coding metasurface to reach flexible and continuous controls of terahertz. Adv. Sci., 3, 1600156(2016).

[98] R. Y. Wu et al. Addition theorem for digital coding metamaterials. Adv. Opt. Mater., 6, 1701236(2018).

[99] T. J. Cui et al. Direct transmission of digital message via programmable coding metasurface. Research, 2019, 2584509(2019).

[100] J. Y. Dai et al. Wireless communications through a simplified architecture based on time-domain digital coding metasurface. Adv. Mater. Technol., 4, 1900044(2019).

[101] L. Li et al. Electromagnetic reprogrammable coding-metasurface holograms. Nat. Commun., 8, 197(2017).

[102] H. Wu et al. Information theory of metasurfaces. Natl. Sci. Rev., 7, 561(2020).

[103] H. Wu et al. Harmonic information transitions of spatiotemporal metasurfaces. Light Sci. Appl., 9, 198(2020).

[104] J. Y. Dai et al. Independent control of harmonic amplitudes and phases via a time-domain digital coding metasurface. Light Sci. Appl., 7, 90(2018).

[105] J. Zhao et al. Programmable time-domain digital-coding metasurface for non-linear harmonic manipulation and new wireless communication systems. Natl. Sci. Rev., 6, 231(2019).

[106] J. Y. Dai et al. High-efficiency synthesizer for spatial waves based on space-time-coding digital metasurface. Laser Photonics Rev., 14, 1900133(2020).

[107] W. Tang et al. Wireless communications with programmable metasurface: transceiver design and experimental results. China Commun., 16, 46(2019).

[108] J. Y. Dai et al. Wireless communication based on information metasurfaces. IEEE Trans. Microw. Theory Tech., 69, 1493(2021).

[109] J. Y. Dai et al. Arbitrary manipulations of dual harmonics and their wave behaviors based on space-time-coding digital metasurface. Appl. Phys. Rev., 7, 041408(2020).

[110] A. Zappone et al. Overhead-aware design of reconfigurable intelligent surfaces in smart radio environments. IEEE Trans. Wireless Commun., 20, 126(2021).

[111] Z. Yang et al. Energy-efficient wireless communications with distributed reconfigurable intelligent surfaces. IEEE Trans. Wireless Commun., 21, 665(2021).

[112] W. Tang et al. Wireless communications with reconfigurable intelligent surface: path loss modeling and experimental measurement. IEEE Trans. Wireless Commun., 20, 421(2021).

[113] C. Huang et al. Reconfigurable intelligent surfaces for energy efficiency in wireless communication. IEEE Trans. Wireless Commun., 18, 4157(2019).

[114] 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(2020).

[115] Y. Liu et al. Reconfigurable intelligent surfaces: principles and opportunities. IEEE Commun. Surv. Tutor., 23, 1546(2021).

[116] D. Silver et al. Mastering the game of Go without human knowledge. Nature, 550, 354(2017).

[117] Y. Lecun, Y. Bengio, G. Hinton. Deep learning. Nature, 521, 436(2015).

[118] G. Hinton et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag., 29, 82(2012).

[119] G. Pironkov, S. U. N. Wood, S. Dupont. Hybrid-task learning for robust automatic speech recognition. Comput. Speech Lang., 64, 101103(2020).

[120] A. Graves, A. R. Mohamed, G. Hinton. International Conference on Acoustics Speech and Signal Processing ICASSP. IEEE International Conference on Acoustics, Speech and Signal Processing, 6645(2013).

[121] K. Lu. Intelligent recognition system for high precision image significant features in large data background. Advances in Intelligent Systems and Computing, 1056(2020).

[122] T. Tong et al. MBVCNN: joint convolutional neural networks method for image recognition. AIP Conf. Proc., 1839, 020091(2017).

[123] H. Sun et al. MBVCNN: joint convolutional neural networks method for image recognition. 7th International Conference on System of Systems Engineering, 24(2012).

[124] K. Cho et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation(2014).

[125] C. Escolano, M. R. Costa-Jussa, J. A. R. Fonollosa. From bilingual to multilingual neural-based machine translation by incremental training. 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, 236(2019).

[126] S. Kwon, B. H. Go, J. H. Lee. A text-based visual context modulation neural model for multimodal machine translation. Pattern Recognit. Lett., 136, 212(2020).

[127] I. J. Goodfellow et al. Generative adversarial nets. Advances in Neural Information Processing Systems(2014).

[128] C. Ledig et al. Photo-realistic single Image super-resolution using a generative adversarial network. 30th IEEE Conference on Computer Vision and Pattern Recognition, 105(2017).

[129] Y. Choi et al. StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8789(2018).

[130] J. Xiao, X. Bi. Multi-scale attention generative adversarial networks for video frame interpolation. IEEE Access, 8, 94842(2020).

[131] C. He et al. Integral reinforcement learning-based multi-robot minimum time-energy path planning subject to collision avoidance and unknown environmental disturbances. IEEE Control Syst. Lett., 5, 983(2021).

[132] B. Sangiovanni et al. Self-configuring robot path planning with obstacle avoidance via deep reinforcement learning. IEEE Control Syst. Lett., 5, 397(2021).

[133] J. Yoo et al. Hybrid reinforcement learning control for a micro quadrotor flight. IEEE Control Syst. Lett., 5, 505(2021).

[134] Q. Zhang et al. Shaping electromagnetic waves using software-automatically-designed metasurfaces. Sci. Rep., 7, 3588(2017).

[135] T. Qiu et al. Deep learning: a rapid and efficient route to automatic metasurface design. Adv. Sci., 6, 1900128(2019).

[136] F. Ghorbani et al. A deep learning approach for inverse design of the metasurface for dual-polarized waves. Appl. Phys. A, 127, 869(2021).

[137] S. Inampudi, H. Mosallaei. Neural network based design of metagratings. Appl. Phys. Lett., 112, 241102(2018).

[138] Y. T. Luo et al. Probability-density-based deep learning paradigm for the fuzzy design of functional metastructures. Research, 2020, 8757403(2020).

[139] 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(2019).

[140] Z. H. Wu et al. A comprehensive survey on graph neural networks. IEEE Trans. Neural Networks Learn. Syst., 32, 4(2021).

[141] A. Sanchez-Gonzalez et al. Learning to simulate complex physics with graph networks. 37th International Conference on Machine Learning, 8428(2020).

[142] H. Momose, T. Kaneko, T. Asai. Systems and circuits for AI chips and their trends. Jpn. J. Appl. Phys., 59, 050502(2020).

[143] A. Isozaki et al. AI on a chip. Lab Chip, 20, 3074(2020).

[144] W. Q. Zhang et al. Neuro-inspired computing chips. Nat. Electron., 3, 371(2020).

[145] Q. Zhang et al. Artificial neural networks enabled by nanophotonics. Light Sci. Appl., 8, 42(2019).

[146] E. Goi et al. Perspective on photonic memristive neuromorphic computing. PhotoniX, 1, 3(2020).

[147] X. Lin et al. All-optical machine learning using diffractive deep neural networks. Science, 361, 1004(2018).

[148] Y. Luo et al. Design of task-specific optical systems using broadband diffractive neural networks. Light Sci. Appl., 8, 112(2019).

[149] D. Mengu et al. Analysis of diffractive optical neural networks and their integration with electronic neural networks. IEEE J. Sel. Top Quantum. Electron., 26, 3700114(2020).

[150] D. Mengu et al. Misalignment resilient diffractive optical networks. Nanophotonics, 9, 4207(2020).

[151] M. S. S. Rahman et al. Ensemble learning of diffractive optical networks. Light Sci. Appl., 10, 14(2021).

[152] T. Yan et al. Fourier-space diffractive deep neural network. Phys. Rev. Lett., 123, 023901(2019).

[153] T. Zhou et al. In situ optical backpropagation training of diffractive optical neural networks. Photonics Res., 8, 940(2020).

[154] T. Zhou et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photonics, 15, 367(2021).

[155] C. Liu et al. A programmable diffractive deep neural network based on a digital-coding metasurface array. Nat. Electron., 5, 113(2022).

[156] L. G. Wright et al. Deep physical neural networks trained with backpropagation. Nature, 601, 549(2022).

[157] S. Sun et al. Electromagnetic metasurfaces: physics and applications. Adv. Opt. Photonics, 11, 380(2019).

[158] A. Nemati et al. Tunable and reconfigurable metasurfaces and metadevices. Opto-Electron. Adv., 1, 18000901(2018).

[159] T. J. Cui. Microwave metamaterials-from passive to digital and programmable controls of electromagnetic waves. J. Opt., 19, 084004(2017).

[160] S. Liu, T. J. Cui. Concepts, working principles, and applications of coding and programmable metamaterials. Adv. Opt. Mater., 5, 1700624(2017).

[161] Y. Yang et al. Realization of a three-dimensional photonic topological insulator. Nature, 565, 622(2019).

[162] S. Kruk et al. Nonlinear light generation in topological nanostructures. Nat. Nanotechnol., 14, 126(2019).

[163] Y. Zeng et al. Electrically pumped topological laser with valley edge modes. Nature, 578, 246(2020).

[164] X. Wu et al. Direct observation of valley-polarized topological edge states in designer surface plasmon crystals. Nat. Commun., 8, 1304(2017).

[165] X. P. Shen, T. J. Cui. Ultrathin plasmonic metamaterial for spoof localized surface plasmons. Laser Photonics Rev., 8, 137(2014).

[166] X. Cheng et al. Robust reconfigurable electromagnetic pathways within a photonic topological insulator. Nat. Mater., 15, 542(2016).

[167] J. P. Xia et al. Programmable coding acoustic topological insulator. Adv. Mater., 30, e1805002(2018).

[168] Q. Zhang et al. Programmable elastic valley Hall insulator with tunable interface propagation routes. Extreme Mech. Lett., 28, 76-80(2019).

[169] H. Zhao et al. Non-Hermitian topological light steering. Science, 365, 1163(2019).

[170] T. Cao et al. Dynamically reconfigurable topological edge state in phase change photonic crystals. Sci. Bull., 64, 814(2019).

[171] A. Darabi, M. Collet, M. J. Leamy. Experimental realization of a reconfigurable electroacoustic topological insulator. Proc. Natl Acad. Sci. USA, 117, 16138(2020).

[172] C. G. Parazzoli et al. Experimental verification and simulation of negative index of refraction using Snell’s law. Phys. Rev. Lett., 90, 107401(2003).

[173] Q. Cheng, W. X. Jiang, T. J. Cui. Spatial power combination for omnidirectional radiation via anisotropic metamaterials. Phys. Rev. Lett., 108, 213903(2012).

[174] X. Chen et al. Macroscopic invisibility cloaking of visible light. Nat. Commun., 2, 176(2011).

[175] Q. He, S. L. Sun, L. Zhou. Tunable/reconfigurable metasurfaces: physics and applications. Research, 2019, 1849272(2019).

[176] Y. Zhou et al. Reconfigurable metasurface for multiple functions: magnitude, polarization and phase modulation. Opt. Express, 26, 29451(2018).

[177] S. Bang et al. Recent advances in tunable and reconfigurable metamaterials. Micromachines, 9, 560(2018).

[178] S. Jafar-Zanjani, S. Inampudi, H. Mosallaei. Adaptive genetic algorithm for optical metasurfaces design. Sci. Rep., 8, 11040(2018).

[179] J. Zhang et al. Genetic algorithms to automate the design of metasurfaces for absorption bandwidth broadening. ACS Appl. Mater. Interfaces, 13, 7792-7800(2021).

[180] Q. Zhang et al. Machine-learning designs of anisotropic digital coding metasurfaces. Adv. Theor. Simul., 2, 1800132(2019).

[181] C. C. Nadell et al. Deep learning for accelerated all-dielectric metasurface design. Opt. Express, 27, 27523(2019).

[182] T. S. Qiu et al. Deep learning: a rapid and efficient route to automatic metasurface design. Adv. Sci., 6, 1900128(2019).

[183] Y. Luo et al. Probability-density-based deep learning paradigm for the fuzzy design of functional metastructures. Research, 2020, 8757403(2020).

[184] S. S. An et al. A deep learning approach for objective-driven all-dielectric metasurface design. ACS Photonics, 6, 3196(2019).

[185] W. Ma, F. Cheng, Y. M. Liu. Deep-learning-enabled on-demand design of chiral metamaterials. ACS Nano, 12, 6326(2018).

[186] A. Khan et al. A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev., 53, 5455(2020).

[187] B. S. Prakash et al. A survey on recurrent neural network architectures for sequential learning. Advances in Intelligent Systems and Computing, 57(2019).

[188] Q. Zhang et al. Shaping electromagnetic waves using software-automatically-designed metasurfaces. Sci. Rep., 7, 3588(2017).

[189] J. Kennedy et al. A discrete binary version of the particle swarm algorithm. Smc ‘97 Conference Proceedings, 4104(1997).

[190] T. He et al. Perfect anomalous reflectors at optical frequencies. Sci. Adv., 8, eabk3381(2022).

[191] N. Mohammadi Estakhri, A. Alù. Wave-front transformation with gradient metasurfaces. Phys. Rev. X, 6, 041008(2016).

[192] L. P. Kaelbling, M. L. Littman, A. W. Moore. Reinforcement learning: a survey. J. Artif. Intell. Res., 4, 237(1996).

[193] V. Mnih et al. Human-level control through deep reinforcement learning. Nature, 518, 529(2015).

[194] I. Sajedian, H. Lee, J. Rho. Double-deep Q-learning to increase the efficiency of metasurface holograms. Sci. Rep., 9, 10899(2019).

[195] I. Malkiel et al. Plasmonic nanostructure design and characterization via deep learning. Light Sci. Appl., 7, 60(2018).

[196] 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).

[197] Z. C. Liu et al. Generative model for the inverse design of metasurfaces. Nano Lett., 18, 6570(2018).

[198] S. S. An et al. Multifunctional metasurface design with a generative adversarial network. Adv. Opt. Mater., 9, 2001433(2021).

[199] 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).

[200] T. Young et al. Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag., 13, 55(2018).

[201] J. M. Johnson, Y. Rahmat-Samii. Genetic algorithm optimization for aerospace electromagnetic design and analysis. IEEE Aerospace Applications Conference, 87(1996).

[202] J. Robinson, Y. Rahmat-Samii. Particle swarm optimization in electromagnetics. IEEE Trans. Antennas Propag., 52, 397(2004).

[203] J. Wu et al. Anisotropic metasurface holography in 3D space with high resolution and efficiency. IEEE Trans. Antennas Propag., 69, 302(2020).

[204] L. Shi et al. Towards real-time photorealistic 3D holography with deep neural networks. Nature, 591, 234(2021).

[205] T. Shan et al. Coding programmable metasurfaces based on deep learning techniques. IEEE J. Emerg. Sel. Topics Power Electron, 10, 114(2020).

[206] H. C. Zhang et al. Broadband amplification of spoof surface plasmon polaritons at microwave frequencies. Laser Photonics Rev., 9, 83(2015).

[207] D. Wang et al. Efficient generation of complex vectorial optical fields with metasurfaces. Light Sci. Appl., 10, 67(2021).

[208] Z. Wang et al. Excite spoof surface plasmons with tailored wavefronts using high-efficiency terahertz metasurfaces. Adv. Sci., 7, 2000982(2020).

[209] S. Li et al. Helicity-delinked manipulations on surface waves and propagating waves by metasurfaces. Nanophotonics, 9, 3473(2020).

[210] Y. Meng et al. Optical meta-waveguides for integrated photonics and beyond. Light Sci. Appl., 10, 235(2021).

[211] E. Khoram et al. Nanophotonic media for artificial neural inference. Photonics Res., 7, 823(2019).