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 VO_{2} 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