L. Mandelstam, Group velocity in crystalline arrays. Zhurnal Eksp. Teor. Fiz. 15, 475–478 (1945)
Google Scholar
W.E. Kock, Metal-lens antennas. Proc. IRE 34, 828–836 (1946)
Article
Google Scholar
J. Brown, Artificial dielectrics having refractive indices less than unity. Proc. IEE IV 100, 51–62 (1953)
Google Scholar
R. Mendis, D.M. Mittleman, Artificial dielectrics: ordinary metallic waveguides mimic extraordinary dielectric media. IEEE Microw. Mag. 15(7), 34–42 (2014)
Article
Google Scholar
J.B. Pendry, A.J. Holden, W.J. Stewart, I. Youngs, Extremely low frequency plasmons in metallic microstructures. Phys. Rev. Lett. 76, 4773 (1996)
Article
ADS
Google Scholar
T.W. Ebbesen, H.J. Lezec, H.F. Ghaemi et al., Extraordinary optical transmission through sub-wavelength hole arrays. Nature 39, 12 (1998)
Google Scholar
V.G. Veselago, The electrodynamics of substances with simultaneously negative values of ε and μ. Sov. Phys. Usp. 10(4), 509–514 (1945)
Article
ADS
Google Scholar
J.B. Pendry, Negative refraction makes a perfect lens. Phys. Rev. Lett. 85, 3966–3969 (2000)
Article
ADS
Google Scholar
D.R. Smith, W.J. Padilla, D.C. Vier et al., Composite medium with simultaneously negative permeability and permittivity. Phys. Rev. Lett. 84, 4184–4187 (2000)
Article
ADS
Google Scholar
R.A. Shelby, D.R. Smith, S. Schultz, Experimental verification of a negative index of refraction. Science 292, 6 (2001)
Article
Google Scholar
N. Garcia, E.V. Ponizovskaya, J.Q. Xiao, Zero permittivity materials: band gaps at the visible. Appl. Phys. Lett. 80, 7 (2002)
Article
Google Scholar
J.B. Pendry, L. Martin-Moreno, F.J. Garcia-Vidal, Mimicking surface plasmons with structured surfacers. Science 305, 847–848 (2004)
Article
ADS
Google Scholar
D. Schurig, J.J. Mock, B.J. Justice et al., Metamaterial electromagnetic cloak at microwave frequencies. Science 314, 977–980 (2006)
Article
ADS
Google Scholar
N. Yu et al., Light propagation with phase discontinuities: generalized laws of reflection and refraction. Science 334, 333–337 (2011)
Article
ADS
Google Scholar
S. Sun, Q. He, S. Xiao et al., Gradient-index meta-surfaces as a bridge linking propagating waves and surface waves. Nat. Mater. 11, 426–431 (2012)
Article
ADS
Google Scholar
C. Pfeiffer, A. Grbic, Metamaterial Huygens’ surfaces: tailoring wave fronts with reflectionless sheets. Phys. Rev. Lett. 110, 197401 (2013)
Article
ADS
Google Scholar
N. Shitrit et al., Spin-optical metamaterial route to spin-controlled photonics. Science 340, 724–726 (2013)
Article
ADS
MathSciNet
MATH
Google Scholar
C.D. Giovampaola, N. Engheta, Digital metamaterials. Nat. Mater. 13, 1115–1121 (2014)
Article
ADS
Google Scholar
T.J. Cui et al., Coding metamaterials, digital metamaterials and programmable metamaterials. Light Sci. Appl. 3, e218 (2014)
Article
Google Scholar
A. Shaltout, A. Kildishev, V. Shalaev, Time-varying metasurfaces and Lorentz non-reciprocity. Opt. Mater Express 5, 2456–2467 (2015)
Article
ADS
Google Scholar
T.J. Cui, S. Liu, L. Zhang, Information metamaterials and metasurfaces. J. Mater. Chem. C 5, 3644–3668 (2017)
Article
Google Scholar
R.Y. Wu, C.B. Shi, S. Liu et al., Addition theorem for digital coding metamaterials. Adv. Opt. Mater. 6, 1701236 (2018)
Article
Google Scholar
H. Wu et al., Information theory of metasurfaces. Natl. Sci. Rev. 7, 561 (2020)
Article
ADS
Google Scholar
Q. Ma, T.J. Cui, Information metamaterials: bridging the physical world and digital world. PhotoniX 1, 1 (2020)
Article
Google Scholar
L. Zhang, X.Q. Chen, S. Liu et al., Space-time-coding digital metasurfaces. Nat. Commun. 9, 4334 (2018)
Article
ADS
Google Scholar
A. Silva, F. Monticone, G. Castaldi et al., Performing mathematical operations with metamaterials. Science 343, 160–163 (2014)
Article
ADS
MathSciNet
MATH
Google Scholar
N. Mohammadi Estakhri, B. Edwards, N. Engheta, Inverse-designed metastrutures that solve equations. Science 363, 1333–1338 (2019)
Article
ADS
MathSciNet
MATH
Google Scholar
Z. Ballard, C. Brown, A.M. Madni et al., Machin learning and computation-enabled intelligent sensor design. Nat. Mach. Intell. 3, 556–565 (2021)
Article
Google Scholar
N. Fang, H. Lee, C. Sun, X. Zhang, Sub-diffraction-limited optical imaging with a silver superlens. Science 308, 534 (2005)
Article
ADS
Google Scholar
X. Zhang, Z. Liu, Superlenses to overcome the diffraction limit. Nat. Mater. 7, 435 (2008)
Article
ADS
Google Scholar
F. Lemoult, M. Fink, G. Lerosey, A polychromatic approach to far-field superlensing at visible wavelengths. Nat. Commun. 3, 177–180 (2012)
Article
Google Scholar
D. Lu, Z. Liu, Hyperlenses and metalenses for far-field super-resolution imaging. Nat. Commun. 3, 1205 (2012)
Article
ADS
Google Scholar
F. Aieta et al., Aberration-free ultrathin flat lenses and Axicons at telecom wavelengths based on plasmonic metasurfaces. Nano Lett. 12, 4932–4936 (2012)
Article
ADS
Google Scholar
E.T. Rogers et al., A super-oscillatory lens optical microscope for subwavelength imaging. Nat. Mater. 11, 432–435 (2012)
Article
ADS
Google Scholar
E.T. Rogers, N.I. Zheludev, Optical super-oscillations: sub-wavelength light focusing and super-resolution imaging. J. Opt. 15, 094008 (2013)
Article
ADS
Google Scholar
F. Lemoult et al., Resonant metalenses for breaking the diffraction barrier. Phys. Rev. Lett. 104, 203901 (2010)
Article
ADS
Google Scholar
M. Khorasaninejad, Metalenses at visible wavelengths: diffraction-limited focusing and subwavelength resolution imaging. Science 352, 1190–1194 (2016)
Article
ADS
Google Scholar
Y. Hadad, D.L. Sounas, A. Alu, Space-time gradient metasurfaces. Phys. Rev. B 92, 100304R (2015)
Article
ADS
Google Scholar
Y. Hadad, J.C. Soric, A. Alu, Breaking temporal symmetries for emission and absorption. PNAS 113(13), 3471–4347 (2016)
Article
ADS
Google Scholar
A.E. Cardin, S.R. Silva, S.R. Vardeny et al., Surface-wave-assisted nonreciprocity in spatio-temporally modulated metasurfaces. Nat. Commun. 11, 1469 (2020)
Article
ADS
Google Scholar
J.B. Pendry, D. Schurig, D.R. Smith, Controlling electromagnetic fields. Science 312, 1780–1782 (2006)
Article
ADS
MathSciNet
MATH
Google Scholar
J. Li, J.B. Pendry, Hiding under the carpet: a new strategy for cloaking. Phys. Rev. Lett. 101, 203901 (2008)
Article
ADS
Google Scholar
R. Liu, C. Ji, J.J. Mock et al., Broadband ground-plane cloak. Science 323, 366–369 (2009)
Article
ADS
Google Scholar
H.F. Ma, T.J. Cui, Three-dimensional broadband ground-plane cloak made of metamaterials. Nat. Commun. 1, 21 (2010)
Article
ADS
Google Scholar
T. Ergin, N. Stenger, P. Brenner et al., Three-dimensional invisibility cloak at optical wavelengths. Science 328, 337–339 (2010)
Article
ADS
Google Scholar
Y. Lai, J. Ng, H.Y. Chen et al., Illusion optics: the optical transformation of an object into another object. Phys. Rev. Lett. 102, 253902 (2009)
Article
ADS
Google Scholar
I. Liberal, A. Mahmoud, Y. Li et al., Photonic doping of epsilon-near-zero media. Science 355(6329), 1158–1062 (2017)
Article
Google Scholar
Z. Zhou, Y. Li, H. Li et al., Substrate-integrated photonic doping for near-zero-index devices. Nat. Commun. 10, 4132 (2019)
Article
ADS
Google Scholar
S. Larouche, Y.J. Tsai, T. Tyler, Infrared metamaterial phase holograms. Nat. Mater. 11, 450–454 (2012)
Article
ADS
Google Scholar
G. Zheng, H. Muhlenbernd, M. Kenney et al., Metasurface holograms reaching 80% efficiency. Nat. Nanotechnol. 10, 308–312 (2015)
Article
ADS
Google Scholar
L. Li et al., Intelligent metasurface imager and recognizer. Light Sci. Appl. 8, 97 (2019)
Article
ADS
Google Scholar
Q. Ma, G.D. Bai, H.B. Jing et al., Smart metasurface with self-adaptively reprogrammable functions. Light Sci. Appl. 8, 98 (2019)
Article
ADS
Google Scholar
P. del Hougne, M.F. Imani, A.V. Diebold et al., Learned integrated sensing pipeline: reconfigurable metasurface transceivers as trainable physical layer in an artificial neural network. Adv. Sci. 7, 1901913 (2019)
Article
Google Scholar
H.Y. Li et al., Intelligent electromagnetic sensing with learnable data acquisition and processing. Patterns 1, 100006 (2020)
Article
Google Scholar
C. Qian, B. Zheng, Y. Shen et al., Deep-learning-enabled self-adaptive microwave cloak without human intervention. Nat. Photonics 14, 383–390 (2020)
Article
ADS
Google Scholar
C. Liu, W.M. Yu, Q. Ma et al., Intelligent coding metasurface holograms by physics-assisted unsupervised generative adversarial network. Photonics Res. 9(4), B159–B167 (2021)
Article
Google Scholar
A.L. Holsteen, A.F. Cihan, M.L. Brongersma, Temporal color mixing and dynamic beam shaping with silicon metasurfaces. Science 365(6450), 257–260 (2019)
Article
ADS
Google Scholar
P.C. Wu, R.A. Pala, G. KafaieShirmanesh et al., Dynamic beam steering with all-dielectric electro-optic III–V multiple-quantum-well metasurfaces. Nat. Commun. 10(1), 1–9 (2019)
Google Scholar
X.G. Zhang, Y.L. Sun, Q. Yu et al., Smart Doppler cloak operating in broad band and full polarizations. Adv. Mater. 33, 2007966 (2021)
Article
Google Scholar
J. Han, L. Li, X. Ma et al., Adaptively smart wireless power transfer using 2-bit programmable metasurface. IEEE Trans. Ind. Electron. 69, 8524–8534 (2022)
Article
Google Scholar
Z. Wang, H. Zhang, H. Zhao et al., Intelligent electromagnetic metasurface camera: system design and experimental results. Nanophotonics (2022). https://doi.org/10.1515/nanoph-2021-0665
Article
Google Scholar
M. Veli, D. Mengu, N.T. Yardimci et al., Terahertz pulse shaping using diffractive surfaces. Nat. Commun. 12, 37 (2021)
Article
ADS
Google Scholar
M.J. Dicken et al., Frequency tunable near-infrared metamaterials based on VO2 phase transition. Opt. Express 17, 18330 (2009)
Article
ADS
Google Scholar
H. Tao et al., Reconfigurable terahertz metamaterials. Phys. Rev. Lett. 103, 147401 (2009)
Article
ADS
Google Scholar
L. Ju et al., Graphene plasmonic for tunable terahertz metamaterials. Nat. Nanotechnol. 6, 630–634 (2011)
Article
ADS
Google Scholar
J.Y. Ou, E. Plum, L. Jiang et al., Reconfigurable photonic metamaterials. Nano Lett. 11, 2142–2144 (2011)
Article
ADS
Google Scholar
J.Y. Ou et al., An electromechanically reconfigurable plasmonic metamaterial operating in the near-infrared. Nat. Nanotechnol. 8, 252–255 (2013)
Article
ADS
Google Scholar
Q. Wang et al., Optically reconfigurable metasurfaces and photonic devices based on phase change materials. Nat. Photonics 10, 60–65 (2015)
Article
ADS
Google Scholar
G. Kaplan, K. Aydin, J. Scheuer, Dynamically controlled plasmonic nano-antenna phased array utilizing vanadium dioxide. Opt. Mater. Express 5, 2513 (2015)
Article
ADS
Google Scholar
A. Ghanekar et al., High-rectification near-field thermal diode using phase change periodic nanostructure. Appl. Phys. Lett. 109, 123106 (2016)
Article
ADS
Google Scholar
Q. Wang et al., Optically reconfigurable metasurfaces and photonic devices based on phase change materials. Nat. Photonics 10, 60 (2016)
Article
ADS
Google Scholar
L. Cong, P. Pitchappa, C. Lee et al., Active phase transition via loss engineering in a terahertz MEMS metamaterial. Adv. Mater. 29, 1700733 (2017)
Article
Google Scholar
Y.W. Huang et al., Gate-tunable conducting oxide metasurfaces. Nano Lett. 16, 5319–5325 (2016)
Article
ADS
Google Scholar
L. Wang et al., A review of THz modulators with dynamic tunable metasurfaces. Nanomaterials 9, 965 (2019)
Article
Google Scholar
L. Kang, S. Lan, Y. Cui et al., An active metamaterial platform for chiral responsive optoelectronics. Adv. Mater. 27(29), 4377–4383 (2015)
Article
Google Scholar
Z. Wang, L. Jing, K. Yao et al., Origami-based reconfigurable metamaterials for tunable chirality. Adv. Mater. 29, 1700412 (2017)
Article
Google Scholar
Z. Liu, H. Du, J. Li et al., Nano-kirigami with giant optical chirality. Sci. Adv. 4, eaat4436 (2018)
Article
ADS
Google Scholar
F. Shu, F. Yu, R. Peng et al., Dynamic plasmonic color generation based on phase transition of vanadium dioxide. Adv. Opt. Mater. 6, 1700939 (2018)
Article
Google Scholar
Y. Zhang, C. Fowler, J. Liang et al., Electrically reconfigurable non-volatile metasurface using low-loss optical phase-change material. Nat. Nanotechnol. 16(6), 661–666 (2021)
Article
ADS
Google Scholar
F. Shu, J. Wang, R. Peng et al., Electrically driven tunable broadband polarization states via active metasurfaces based on Joule-Heat-induced phase transition of Vanadium dioxide. Laser Photonics Rev. 15, 2100155 (2021)
Article
ADS
Google Scholar
M.Y. Shalaginov, S. An, Y. Zhang et al., Reconfigurable all-dielectric metalens with diffraction-limited performance. Nat. Commun. 12(1), 1–8 (2021)
Article
ADS
Google Scholar
X.G. Zhang et al., An optically driven digital metasurface for programming electromagnetic functions. Nat. Electron. 3, 165–171 (2020)
Article
ADS
Google Scholar
B. Gholipour et al., An all-optical, non-volatile, bidirectional, phase-change meta-switch. Adv. Mater. 25, 3050–3054 (2013)
Article
Google Scholar
L. Li et al., Electromagnetic reprogrammable coding-metasurface holograms. Nat. Commun. 8, 197 (2017)
Article
ADS
Google Scholar
R.M. Neal, Bayesian Learning for Neural Networks Lecture notes in statistics 29–53. (Springer, New York, 1996)
Book
MATH
Google Scholar
Y. LeCun, L. Bottou, Y. Bengio et al., Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
Article
Google Scholar
K. Friston, The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010)
Article
Google Scholar
T.H. Davenport, D. Patil, Data scientist: the sexiest job of the 21st century. Harv. Bus. Rev. 90(5), 70–76 (2012)
Google Scholar
D.P. Kingma, M. Welling, Auto-encoding variational Bayes (2014), Preprint at arXiv:1312.6114
D.P. Kingma, J. Ba, Adam: a method for stochastic optimization (2014), Preprint at arXiv:1412.6980
Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436–443 (2015)
Article
ADS
Google Scholar
O. Ronneberger, P. Fischer, T. Brox, U-net: convolutional networks for biomedical image segmentation, in International conference on medical image computing and computer-assisted intervention (Springer, 2015), p. 234–241
C. Doersch, Tutorial on variational autoencoders (2016), Preprint at arXiv:1606.05908arXiv:1606.05908
S.L. Brunton, J.L. Proctor, J. NathanKutz, Discovering governing equations from data by sparse identification of nonlinear dynamical systems. PNAS 113(15), 3932–3937 (2016)
Article
ADS
MathSciNet
MATH
Google Scholar
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE international conference on computer vision and pattern recognition (CVPR) (2016)
I. Goodfellow, Y. Bengio, A. Courville, Deep learning (MIT Press, Cambridge, 2016)
MATH
Google Scholar
A. Vaswani, N. Shazeer, N. Parmar, et al., Attention is all you need, in 31st conference on neural information processing systems (NIPS 2017), Long Beach, CA (2017)
K.H. Jin, M.T. McCann, E. Froustey et al., Deep convolutional neural network for inverse problems in imaging. IEEE Trans. Image Process. 26, 4509–4522 (2017)
Article
ADS
MathSciNet
MATH
Google Scholar
M.M. Bronstein, J. Bruna, Y. LeCun et al., Geometric deep learning. IEEE Signal Process. Mag. 34(4), 18–42 (2017)
Article
ADS
Google Scholar
F.M. Bayat, M. Prezioso, B. Chakrabarti et al., Implementation of multiplayer perceptron network with highly uniform passive memristive crossbar circuits. Nat. Commun. 9, 2331 (2018)
Article
ADS
Google Scholar
V. Sitzmann et al., End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging. ACM Trans. Graph. 37, 1–13 (2018)
Article
Google Scholar
B. Lusch, J.N. Kutz, S.L. Brunton, Deep learning for universal linear embeddings of nonlinear dynamics. Nat. Commun. 9, 4950 (2019)
Article
ADS
Google Scholar
G. Carleo, I. Cirac, K. Cranmer et al., Machin learning and the physical sciences. Rev. Mod. Phys. 91, 045002 (2019)
Article
ADS
Google Scholar
P. Mehta et al., A high-bias, low-variance introduction to machine learning for physicists. Phys. Rep. 810, 1–124 (2019)
Article
ADS
MathSciNet
Google Scholar
A. Kurenkov, A brief history of neural nets and deep learning (2020), https://www.skynettoday.com/overviews/neural-net-history
R. van de Schoot, S. Depaoli, R. King et al., Bayesian statistics and modelling. Nat. Rev. Methods Prim. 1, 1 (2021)
Article
Google Scholar
W. Ma, Z. Liu, Z.A. Kudyshev et al., Deep learning for the design of photonic structures. Nat. Photonics 15, 77–90 (2021)
Article
ADS
Google Scholar
I. Malkiel, M. Mrejen, A. Nagler et al., Plasmonic nanostructure design and characterization via deep learning. Light Sci. Appl. 7, 60 (2018)
Article
ADS
Google Scholar
W. Ma, F. Cheng, Y. Liu, Deep-learning-enabled on-demand design of chiral metamaterials. ACS Nano 12(6), 6326–6334 (2018)
Article
Google Scholar
L. Li et al., DeepNIS: deep neural network for nonlinear electromagnetic inverse scattering. IEEE Trans. Antennas Propag. 67, 1819–1825 (2019)
Article
ADS
Google Scholar
T. Qiu, X. Shi, J. Wang et al., Deep learning: a rapid and efficient route to automatic metasurface design. Adv. Sci. 6, 1900128 (2019)
Article
Google Scholar
S. So, T. Badloe, J. Noh et al., Deep learning enabled inverse design in nanophotonics. Nanophotonics 9(5), 1041–1057 (2020)
Article
Google Scholar
J. Jiang, M. Chen, J.A. Fan, Deep neural networks for the evaluation and design of photonic devices. Nat. Rev. Mater. 6, 679–700 (2021)
Article
ADS
Google Scholar
W. Ma, Z. Liu, Z.A. Kudyshev et al., Deep learning for the design of photonic structures. Nat. Photonics 15(2), 77–90 (2020)
Article
ADS
Google Scholar
D. Zhu, Z. Liu, L. Raju et al., Building multifunctional metasystems via algorithmic construction. ACS Nano 15(2), 2318–2326 (2021)
Article
Google Scholar
A. Jacot, F. Gabriel, C. Hongler, Neural tangent kernel: convergence and generalization in neural networks (2020), Preprint at arXiv:1806.07572v4
M. Tancik, P.P. Srinivasan, B. Mildenhall, et al., Fourier features let networks learn high frequency functions in low dimensional domains (2020), Preprint at arXiv:2006.10739v1
C. Fang, H. He, Q. Long et al., Exploring deep neural networks via layer-peeled model: minority collapse in imbalanced training. PNAS 118, e2103091118 (2021)
Article
MathSciNet
Google Scholar
J.B. Simon, M. Dickens, M.R. DeWeese, Neural tangent kernel eigenvalues accurately predict generalization (2021), Preprint at arXiv:2110.03922v2
N. Elhage, N. Nanda, C. Olsson, et al., A mathematical framework for transformer circuits, https://transformer-circuits.pub/2021/framwork/index.html
D.A. Roberts, S. Yaida, The principle of deep learning theory (2021), Preprint at arXiv:2106.10165v1
L. Li et al., Machine-learning reprogrammable metasurface imager. Nat. Commun. 10, 1082 (2019)
Article
ADS
Google Scholar
T.J. Cui, S. Liu, L. Li, Information entropy of coding metasurface. Light Sci. Appl. 5, e16172 (2016)
Article
Google Scholar
S. Liu, T.J. Cui, L. Zhang et al., Convolution operations on coding metasurface to reach flexible and continuous controls of terahertz beams. Adv. Sci. 3, 1600156 (2016)
Article
Google Scholar
H.T. Wu, S. Liu, X. Wan et al., Controlling energy radiations of electromagnetic waves via frequency coding metamaterials. Adv. Sci. 4, 1700098 (2017)
Article
Google Scholar
F. Zangeneh-Nejad, D.L. Sounas, A. Alu, Analogue computing with metamaterials. Nat. Rev. Mater. 6, 207–225 (2021)
Article
ADS
Google Scholar
T. Zhu, Y. Zhou, Y. Lou et al., Plasmonic computing of spatial differentiation. Nat. Commun. 8, 15391 (2017)
Article
ADS
Google Scholar
P. del Hougne, G. Lerosey, Leveraging chaos for wave-based analog computation: demonstration with indoor wireless communication signals. Phys. Rev. X 8, 041037 (2018)
Google Scholar
J. Chang, C. Sitzmann, X. Dun et al., Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification. Sci. Rep. 8, 12324 (2018)
Article
ADS
Google Scholar
X. Lin, Y. Rivenson, N.T. Yardimci et al., All-optical machine learning using diffractive deep neural networks. Science 361, 1004–1008 (2018)
Article
ADS
MathSciNet
MATH
Google Scholar
M.W. Mattnes, P. del Hougne, J. de Rosny et al., Optical complex media as universal reconfigurable linear operators. Optica 6(4), 465–472 (2019)
Article
ADS
Google Scholar
A. McClung, M. Mansouree, A. Arbabi, At-will chromatic dispersion by prescribing light trajectories with cascaded metasurfaces. Light Sci. Appl. 9, 93 (2020)
Article
ADS
Google Scholar
H. Rajabalipanah, A. Abdolali, S. Iqbal et al., Analog signal processing through space-time digital metasurfaces. Nanophotonics 10(6), 1753–1764 (2021)
Article
Google Scholar
C. Wu, H. Yu, S. Lee et al., Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network. Nat. Commun. 12, 96 (2021)
Article
ADS
Google Scholar
M.A. Badiu, J.P. Coon, Communication through a large reflecting surface with phase errors. IEEE Wirel. Commun. Lett. 9, 184 (2020)
Article
Google Scholar
P. Xu, G. Chen, Z. Yang et al., Reconfigurable intelligent surfaces assisted communications with discrete phase shifts: how many quantization levels are required to achieve full diversity? IEEE Wirel. Commun. Lett. 10(2), 358–362 (2020)
Article
Google Scholar
D. Li, Ergodic capacity of intelligent reflecting surface-assisted communication systems with phase errors. IEEE Commun. Lett. 24, 1646 (2020)
Article
ADS
Google Scholar
Y. Shuang, H. Zhao, M. Wei, et al., One-bit quantization is good for programmable metasurfaces, to be published. (2022)
L. Zhang, S. Liu, L. Li et al., Spin-controlled multiple pencil beams and vortex beams with different polarizations generated by Pancharatnam–Berry coding metasurfaces. ACS Appl. Mater. Interfaces 9, 36447 (2017)
Article
Google Scholar
J. Wang, Y. Li, Z.H. Jiang et al., Metantenna: when metasurface meets antenna again. IEEE Trans. Antenna Propag. 68(3), 1332–1347 (2020)
Article
ADS
Google Scholar
H.J. Visser, Array and phased array antenna basics (Wiley, Chichester, 2005)
Book
Google Scholar
D.R. Smith et al., An analysis of beamed wireless power transfer in the Fresnel zone using a dynamic metasurface aperture. J. Appl. Phys. 121, 014901 (2017)
Article
ADS
Google Scholar
P. del Hougne, M. Fink, G. Lerosey, Shaping microwave fields using nonlinear unsolicited feedback: application to enhance energy harvesting. Phys. Rev. Appl. 8, 061001 (2017)
Article
ADS
Google Scholar
M. Song, P. Jayathurathnage, E. Zanganeh et al., Wireless power transfer based on novel physical concepts. Nat. Electron. 4, 707–716 (2021)
Article
Google Scholar
T. Sasatani, A.P. Sample, Y. Kawahara, Room-scale magnetoquasistatic wireless power transfer using a cavity-based multimode resonator. Nat. Electron. 4, 689–697 (2021)
Article
Google Scholar
A. Kurs, A. Karalis, R. Moffatt et al., Wireless power transfer via strongly coupled magnetic resonances. Science 317(5834), 83–86 (2007)
Article
ADS
MathSciNet
Google Scholar
S. Assawaworrarit, X. Yu, S. Fan, Robust wireless power transfer using a nonlinear parity–time-symmetric circuit. Nature 546(7658), 387–390 (2017)
Article
ADS
Google Scholar
T. Ozaki, N. Ohta, T. Jimbo et al., A wireless radiofrequency-powered insect-scale flapping-wing aerial vehicle. Nat. Electron. 4, 845–852 (2021)
Article
Google Scholar
J. Li, S. Kamin, G. Zheng et al., Addressable metasurfaces for dynamic holography and optical information encryption. Sci. Adv. 4, eaar6768 (2018)
Article
ADS
Google Scholar
B. Xiong, Y. Xu, J. Wang et al., Realizing colorful holographic mimicry by metasurfaces. Adv. Mater. 33, 2005864 (2021)
Article
Google Scholar
I. Kim, J. Jang, G. Kim et al., Pixelated bifunctional metasurface-driven dynamic vectorial holographic color prints for photonic security platform. Nat. Commun. 12(1), 1–9 (2021)
Google Scholar
W. Ma, Y. Xu, B. Xiong et al., Pushing the limits of functionality-multiplexing capability in metasurface design based on statistical machine learning. Adv. Mater. (2022). https://doi.org/10.1002/adma.202110022
Article
Google Scholar
R.W. Gerchberg, W.O. Saxton, A practical algorithm for the determination of the phase from image and diffraction plane pictures. Optik 35, 237–246 (1972)
Google Scholar
J.C. Duchi, Introductory lectures on stochastic optimization, https://web.stanford.edu/~jduchi/PCMIConvex/
R. Liu, Q. Cheng, T. Hand et al., Experimental demonstration of electromagnetic tunneling through an epsilon-near-zero metamaterial at microwave frequencies. Phys. Rev. Lett. 100, 023903 (2008)
Article
ADS
Google Scholar
J. Valentine, J. Li, T. Zentgraf et al., An optical cloak made of dielectrics. Nat. Mater. 8(7), 568–571 (2009)
Article
ADS
Google Scholar
M. Gharghi, C. Gladden, T. Zentgraf et al., A carpet cloak for visible light. Nano Lett. 11(7), 2825–2828 (2011)
Article
ADS
Google Scholar
X. Chen, Y. Luo, J. Zhang et al., Macroscopic invisibility cloaking of visible light. Nat. Commun. 2(1), 1–6 (2011)
Article
Google Scholar
B. Zhang, Y. Luo, X. Liu et al., Macroscopic invisibility cloak for visible light. Phys. Rev. Lett. 106, 033901 (2011)
Article
ADS
Google Scholar
J.B. Pendry, A. Aubry, D.R. Smith et al., Transformation optics and subwavelength control of light. Science 337(6094), 549–552 (2012)
Article
ADS
MathSciNet
MATH
Google Scholar
C.E. Shannon, A mathematical theory of communication. Bell Syst. Tech. J. 27, 379 (1948)
Article
MathSciNet
MATH
Google Scholar
G.J. Foschini, M.J. Gans, On limits of wireless communications in a fading environment when using multiple antennas. Wirel. Pers. Commun. 6, 311 (1998)
Article
Google Scholar
A. Goldsmith, Wireless Communications (Cambridge University Press, Cambridge, 2005)
Book
Google Scholar
Y.S. Cho, J. Kim, W.Y. Yang et al., MIMO-OFDM wireless communications with MATLAB (Wiley-IEEE Press, Singapore, 2011)
Google Scholar
T.L. Marzetta, Massive MIMO: an introduction. Bell Labs Tech. J. 20, 11 (2015)
Article
Google Scholar
T. Francis, Entropy and information optics: connecting information and time (CRC Press, Boca Raton, 2017)
Google Scholar
P. del Hougne, M. Fink, G. Lerosey, Optimally diverse communication channels in disordered environments with tuned randomness. Nat. Electron. 2, 36 (2019)
Article
Google Scholar
S. Rout, S. Sonkusale, Wireless multi-level terahertz amplitude modulator using active metamaterial-based spatial light modulation. Opt. Express 24, 14618 (2016)
Article
ADS
Google Scholar
S. Hu, F. Rusek, O. Edfors, Beyond Massive MIMO: The potential of data transmission with large intelligent surfaces. IEEE Trans. Signal Process. 66, 2746 (2018)
Article
ADS
MathSciNet
MATH
Google Scholar
M. Di Renzo, M. Debbah, D.T. Phan-Huy et al., Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come. EURASIP J. Wirel. Commun. Netw. (2019). https://doi.org/10.1186/s13638-019-1438-9
Article
Google Scholar
M. Di Renzo, A. Zappone, M. Dehhah et al., Smart radio environments empowered by reconfigurable intelligent surfaces: how it works, state of research, and road ahead. IEEE J. Sel. Areas Commun. 38(11), 2450–2525 (2020)
Article
Google Scholar
E. Basar, Marco Di Renzo, J. de Rosny, et al., Wireless communications through reconfigurable intelligent surfaces (2019), Preprint at arXiv:1906.09490v2
L. Yang, J. Yang, W. Xie et al., Secrecy performance analysis of RIS-aided wireless communication systems. IEEE Trans. Veh. Technol. 69(10), 12296–12300 (2020)
Article
Google Scholar
J. Qiao, M.S. Alouini, Secure transmission for intelligent reflecting surface-aided mmWave and Terahertz systems. IEEE Wirel. Commun. Lett. 9(10), 1743–1747 (2020)
Article
Google Scholar
Y. Liu, J. Zhao, Z. Xiong, et al., Intelligent reflecting surface meets mobile edge computing: enhancing wireless communications for computation offloading (2020), Preprint at arXiv:2001.07449v2
T. Bai, C. Pan, Y. Deng et al., Latency minimization for intelligent reflecting surface aided mobile edge computing. IEEE J. Sel. Areas Commun. 38(11), 2666–2682 (2020)
Article
Google Scholar
V. Arun, H. Balakrishnan, RFocus: beamforming using thousands of passive antennas, in 17th USENIX symposium on networked systems design and implementation, Santa Clara, CA, USA (2020), p. 1047–1061
W. Tang, Y. Han, M.D. Renzo et al., Wireless communications with reconfigurable intelligent surface: path loss modeling and experimental measurement. IEEE Trans. Wirel. Commun. 20, 19 (2021)
Article
Google Scholar
J. Kimionis, A. Geordiadis, S.N. Daskalakis et al., A printed millimetre-wave modulator and antenna array for backscatter communications at gigabit data rates. Nat. Electron. 4, 439–446 (2021)
Article
Google Scholar
W. Tang, J.Y. Dai, M.Z. Chen et al., MIMO transmission through reconfigurable intelligent surface: system design, analysis and implementation. IEEE J. Sel. Areas Commun. 38(11), 2683–2699 (2020)
Article
Google Scholar
X. Wan, Q. Zhang, T.Y. Chen et al., Multichannel direct transmissions of near-field information. Light Sci. Appl. 8, 60 (2019)
Article
ADS
Google Scholar
Y. Shuang, H. Zhao, W. Ji et al., Programmable high-order OAM-carrying beams for direct-modulation wireless communications. IEEE J. Emerg. Sel. Top. Circuits Syst. 10, 29 (2020)
Article
ADS
Google Scholar
T.J. Cui, S. Liu, G.D. Bai et al., Direct transmission of digital message via programmable coding metasurface. Research 2019(1–12), 2584509 (2019)
Google Scholar
L. Zhang, M.Z. Chen, W. Tang et al., A wireless communication scheme based on space- and frequency-division multiplexing using digital metasurfaces. Nat. Electron. 4, 218–227 (2021)
Article
Google Scholar
J. Zhao et al., Programmable time-domain digital-coding metasurface for non-linear harmonic manipulation and new wireless communication systems. Natl. Sci. Rev. 6, 231–238 (2019)
Article
Google Scholar
H. Zhao et al., Metasurface-assisted massive backscatter wireless communication with commodity Wi-Fi signals. Nat. Commun. 11, 3926 (2020)
Article
ADS
Google Scholar
H. Ur Rehman, F. Bellili, A. Mezghani, et al., Modulating intelligent surfaces for multi-user MIMO systems: beamforming and modulation design (2021), Preprint at arXiv:2108.10505v2
S. Venkatesh, X. Lu, B. Tang et al., Secure space-time-modulated millimeter-wave wireless links that are resilient to distributed eavesdropper attacks. Nat. Electron. 4, 827–836 (2021)
Article
Google Scholar
G. Wang, F. Gao, R. Fan et al., Ambient backscatter communication systems: detection and performance analysis. IEEE Trans. Commun. 64(11), 4836–4856 (2016)
Article
Google Scholar
D.T. Hoang, D. Niyato, P. Wang et al., Ambient backscatter: a new approach to improve network performance for RF-powered cognitive radio networks. IEEE Trans. Commun. 65(9), 3659–3674 (2017)
Article
Google Scholar
N.V. Huynh, D.T. Hoang, X. Lu et al., Ambient backscatter communications: a contemporary survey. IEEE Commun. Surv. Tutor. 20(4), 2889–2992 (2018)
Article
Google Scholar
P. Ambs, Optical computing: a 60-year adventure. Adv. Opt. Technol. 2010(1–15), 372652 (2010)
Google Scholar
W. Tobin, Evolution of the Foucault-Secretan reflecting telescope. J. Astron. Hist. Herit. 19, 106–184 (2016)
ADS
Google Scholar
G. Wetzstein, A. Ozcan, S. Gigan et al., Inference in artificial intelligence with deep optics and photonics. Nature 588, 39–47 (2020)
Article
ADS
Google Scholar
D.A.B. Miller, Waves, modes, communications, and optics: a tutorial. Adv. Opt. Photonics 11(3), 679–823 (2019)
Article
ADS
Google Scholar
X. Xu, M. Tan, B. Corxoran et al., 11 TOPS photonic convolutional accelerator for optical neural networks. Nature 589, 44–50 (2021)
Article
ADS
Google Scholar
W.M. Brown, Synthetic aperture radar. IEEE Trans. Aerosp. Electron. Syst. 3, 217–229 (1967)
Article
ADS
Google Scholar
A.J. Devaney, Mathematical Foundations of Imaging, Tomography and Wavefield Inversion (Cambridge University Press, Cambridge, 2012)
Book
MATH
Google Scholar
G. Picardi, Radar soundings of the subsurface of mars. Science 310, 1925–1928 (2005)
Article
ADS
Google Scholar
S. Ravur, K. Lenc, M. Willson et al., Skillful precipitation nowcasting using deep generative models of radar. Nature 597, 672–677 (2021)
Article
ADS
Google Scholar
M. Zhao, Y. Tian, H. Zhao, et al., RF-based 3D skeletons, in Proceedings of the 2018 conference of the ACM special group on data communication, (2018), p. 267–281
M. Mercuri, I.R. Lorato, Y.H. Liu et al., Vital-sign monitoring and spatial tracking of multiple people using a contactless radar-based sensor. Nat. Electron. 2, 252–262 (2019)
Article
Google Scholar
M.F. Duarte, M.A. Davenport, D. Takhar et al., Single-pixel imaging via compressive sampling. IEEE Signal Process. Mag. 25, 83–91 (2008)
Article
ADS
Google Scholar
M.P. Edgar, G.M. Gibson, M.J. Padgett, Principles and prospects for single-pixel imaging. Nat. Photonics 13, 13–20 (2019)
Article
ADS
Google Scholar
W.K. Chan, K. Charan, D. Takhar et al., A single-pixel terahertz imaging system based on compressed sensing. Appl. Phys. Lett. 93, 121105 (2008)
Article
ADS
Google Scholar
A. Liutkus, D. Martina, S. Popoff et al., Imaging with nature: compressive imaging using a multiply scattering medium. Sci. Rep. 4, 5552–5552 (2014)
Article
Google Scholar
L. Wang, L. Li, Y. Li et al., Single-shot and single-sensor high/super-resolution microwave imaging based on metasurface. Sci. Rep. 6, 26959 (2016)
Article
ADS
Google Scholar
Y.B. Li et al., Transmission-type 2-bit programmable metasurface for single-sensor and single-frequency microwave imaging. Sci. Rep. 6, 23731 (2016)
Article
ADS
Google Scholar
T. Sleasman, M.F. Imani, J.N. Gollub et al., Microwave imaging using a disordered cavity with a dynamically tunable impedance surface. Phys. Rev. Appl. 6, 054019 (2016)
Article
ADS
Google Scholar
C.M. Watts et al., Terahertz compressive imaging with metamaterial spatial light modulators. Nat. Photonics 8, 605 (2014)
Article
ADS
Google Scholar
M.F. Iamni, J.N. Gollub, O. Yurduseven et al., Review of metasurface antennas for computational microwave imaging. IEEE Trans. Antenna Propag. 68(3), 1860–1875 (2020)
Article
ADS
Google Scholar
W.J. Padilla, R.D. Averitt, Imaging with metamaterials. Nat. Rev. Phys. (2021). https://doi.org/10.1038/s42254-021-00394-3
Article
Google Scholar
W.B. Johnson, J. Lindenstrauss, Extensions of Lipschitz mapping into a Hilbert space. Contemp. Math. 26, 189–206 (1982)
Article
MathSciNet
MATH
Google Scholar
E.J. Candes, J. Romberg, T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52, 489–509 (2004)
Article
MathSciNet
MATH
Google Scholar
D.L. Donoho, For most large underdetermined systems of equations, the minimal l1-norm near-solution approximates the sparsest near-solution. Commun. Pure Appl. Math. 59, 907–934 (2004)
Article
Google Scholar
D.L. Donoho, Compressed sensing. IEEE Trans. Inform. Theory 52, 1289–1306 (2006)
Article
MathSciNet
MATH
Google Scholar
T. Jolliffe, Principal Component Analysis (Springer, New York, 2002)
MATH
Google Scholar
M.S.S. Rahman, A. Ozcan, Computer-free, all optical reconstruction of holograms using diffractive networks. ACS Photonics 8, 3375–3384 (2021)
Article
Google Scholar
C. Liu, Q. Ma, Z. Luo, et al., Programmable artificial intelligence machine for wave sensing and communciations, https://doi.org/10.21203/rs.3.rs-90701/v1
L.G. Wright, T. Onodera, M.M. Stein et al., Deep physical neural networks trained with backpropagation. Nature 601, 549–555 (2022)
Article
ADS
Google Scholar
J. Hunt et al., Metamaterial apertures for computational imaging. Science 339, 310–313 (2013)
Article
ADS
Google Scholar
Q. Pu, S. Gupta, S. Gollakota, S. Patel, Whole-home gesture recognition using wireless signals, in Proceedings of the 19th annual international conference on mobile computing & networking, (2013), p. 27–38
D. Huang, R. Nandakumar, S. Gollakota, Feasibility and limits of Wi-Fi imaging, in Proceedings of the 12th ACM conference on embedded network sensor systems, (2014), p. 266–279
G. Wang, Y. Zou, Z. Zhou, K. Wu, L.M. Ni, We can hear you with Wi-Fi! IEEE Trans. Mobile Comput. 15(11), 2907–2920 (2016)
Article
Google Scholar
P.M. Holl, F. Reinhard, Holography of Wi-Fi radiation. Phys. Rev. Lett. 118, 18390 (2017)
Article
Google Scholar
N. Golestani, M. Maghaddam, Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks. Nat. Commun. 11, 1551 (2020)
Article
ADS
Google Scholar
U.S. Kamilov, I.N. Papadopoulos, M.H. Shoreh et al., Learning approach to optical tomography. Optica 2(6), 517–522 (2015)
Article
ADS
Google Scholar
L. Waller, L. Tian, Computational imaging: machine learning for 3D microscopy. Nature 523, 416–417 (2015)
Article
ADS
Google Scholar
A. Sinha, J. Lee, S. Li, G. Barbastathis, Lensless computational imaging through deep learning. Optica 4(9), 1117–1125 (2017)
Article
ADS
Google Scholar
F. Willomitzer, P.V. Rangarajan, F. Li et al., Fast non-line-of-sight imaging with high-resolution and wide field of view using synthetic wavelength holography. Nat. Commun. 12, 6647 (2021)
Article
ADS
Google Scholar
A. Turpin, V. Kapitany, J. Radford et al., 3D imaging from multipath temporal echoes. Phys. Rev. Lett. 126, 174301 (2021)
Article
ADS
Google Scholar
E. Tseng et al., Neural nano-optics for high-quality thin lens imaging. Nat. Commun. 12, 6493 (2021)
Article
ADS
Google Scholar
S. Vedula, O. Senouf, G. Zurakhov et al., Learning beamforming in ultrasound imaging. Proc. Mach. Learn. Res. 102, 493–511 (2019)
Google Scholar
M. Xu, P.V.S. Lee, D.J. Collins, Microfluidic acoustic sawtooth metasurfaces for patterning and separation using travelling surface acoustic waves. Lab Chip 22, 90–99 (2022)
Article
Google Scholar
Z. Chen, Y. Liu, H. Sun, Physics-informed learning of governing equations from scarce data. Nat. Commun. 12, 6136 (2021)
Article
ADS
Google Scholar
J. Lin, D. Psaltis, MaxwellNet: physics-driven deep neural network training based on Maxwell’s equations. APL Photon 7, 011301 (2022)
Article
ADS
Google Scholar
D.J. Gauthier, E. Bollt, A. Griffith et al., Next generation reservoir computing. Nat. Commun. 12, 5564 (2021)
Article
ADS
Google Scholar
J.A. Kong, Electromagnetic Wave Theory (Wiley, New York, 1986)
Google Scholar