[1] P. Mühlschlegel, H.-J. Eisler, O. J. F. Martin, B. Hecht, D. W. Pohl. Resonant optical antennas. Science, 308, 1607-1609(2005).
[2] A. I. Kuznetsov, A. E. Miroshnichenko, M. L. Brongersma, Y. S. Kivshar, B. Luk’yanchuk. Optically resonant dielectric nanostructures. Science, 354, aag2472(2016).
[3] C. Girard. Near fields in nanostructures. Rep. Prog. Phys., 68, 1883-1933(2005).
[4] M. Kauranen, A. V. Zayats. Nonlinear plasmonics. Nat. Photonics, 6, 737-748(2012).
[5] G. C. des Francs, J. Barthes, A. Bouhelier, J. C. Weeber, A. Dereux, A. Cuche, C. Girard. Plasmonic Purcell factor and coupling efficiency to surface plasmons. Implications for addressing and controlling optical nanosources. J. Opt., 18, 094005(2016).
[6] J. Wang, F. Sciarrino, A. Laing, M. G. Thompson. Integrated photonic quantum technologies. Nat. Photonics, 14, 273-284(2020).
[7] J. B. Pendry. Negative refraction makes a perfect lens. Phys. Rev. Lett., 85, 3966-3969(2000).
[8] P. Genevet, F. Capasso, F. Aieta, M. Khorasaninejad, R. Devlin. Recent advances in planar optics: from plasmonic to dielectric metasurfaces. Optica, 4, 139-152(2017).
[9] N. M. Estakhri, B. Edwards, N. Engheta. Inverse-designed metastructures that solve equations. Science, 363, 1333-1338(2019).
[10] W. R. Clements, P. C. Humphreys, B. J. Metcalf, W. S. Kolthammer, I. A. Walmsley. Optimal design for universal multiport interferometers. Optica, 3, 1460-1465(2016).
[11] F. Zangeneh-Nejad, D. L. Sounas, A. Alù, R. Fleury. Analogue computing with metamaterials. Nat. Rev. Mater., 6, 207-225(2021).
[12] E. A. Ash, G. Nicholls. Super-resolution aperture scanning microscope. Nature, 237, 510-512(1972).
[13] D. W. Pohl, W. Denk, M. Lanz. Optical stethoscopy: image recording with resolution
[14] E. Betzig, A. Harootunian, A. Lewis, M. Isaacson. Near-field diffraction by a slit: implications for superresolution microscopy. Appl. Opt., 25, 1890-1900(1986).
[15] B. Gallinet, J. Butet, O. J. F. Martin. Numerical methods for nanophotonics: standard problems and future challenges. Laser Photonics Rev., 9, 577-603(2015).
[16] Y. LeCun, Y. Bengio, G. Hinton. Deep learning. Nature, 521, 436-444(2015).
[17] I. Goodfellow, Y. Bengio, A. Courville. Deep Learning(2016).
[18] S. Chan, E. L. Siegel. Will machine learning end the viability of radiology as a thriving medical specialty?. Br. J. Radiol., 92, 20180416(2018).
[19] A. S. Lundervold, A. Lundervold. An overview of deep learning in medical imaging focusing on MRI. Z. Med. Phys., 29, 102-127(2019).
[20] D. A. Cirovic. Feed-forward artificial neural networks: applications to spectroscopy. TRAC Trends Anal. Chem., 16, 148-155(1997).
[21] T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, D. Amodei. Language models are few-shot learners. Proceedings of Advances in Neural Information Processing System, 1877-1901(2020).
[22] C. Szegedy, S. Ioffe, V. Vanhoucke, A. Alemi. Inception-v4, inception-ResNet and the impact of residual connections on learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 4278-4284(2016).
[23] X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, A. Ozcan. All-optical machine learning using diffractive deep neural networks. Science, 361, 1004-1008(2018).
[24] T. W. Hughes, I. A. D. Williamson, M. Minkov, S. Fan. Wave physics as an analog recurrent neural network. Sci. Adv., 5, eaay6946(2019).
[25] D. Mengu, Y. Rivenson, A. Ozcan. Scale-, shift- and rotation-invariant diffractive optical networks. ACS Photon., 8, 324-334(2021).
[26] R. S. Hegde. Deep learning: a new tool for photonic nanostructure design. Nanoscale Adv., 2, 1007-1023(2020).
[27] W. Ma, Z. Liu, Z. A. Kudyshev, A. Boltasseva, W. Cai, Y. Liu. Deep learning for the design of photonic structures. Nat. Photonics, 15, 77-90(2020).
[28] S. So, T. Badloe, J. Noh, J. Bravo-Abad, J. Rho. Deep learning enabled inverse design in nanophotonics. Nanophotonics, 9, 1041-1057(2020).
[29] J. Jiang, M. Chen, J. A. Fan. Deep neural networks for the evaluation and design of photonic devices(2020).
[30] L. Huang, L. Xu, A. E. Miroshnichenko. Deep learning enabled nanophotonics. Advances in Deep Learning(2020).
[31] M. M. R. Elsawy, S. Lanteri, R. Duvigneau, J. A. Fan, P. Genevet. Numerical optimization methods for metasurfaces. Laser Photonics Rev., 14, 1900445(2020).
[32] S. Molesky, Z. Lin, A. Y. Piggott, W. Jin, J. Vucković, A. W. Rodriguez. Inverse design in nanophotonics. Nat. Photonics, 12, 659-670(2018).
[33] G. M. Sacha, P. Varona. Artificial intelligence in nanotechnology. Nanotechnology, 24, 452002(2013).
[34] K. Yao, R. Unni, Y. Zheng. Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale. Nanophotonics, 8, 339-366(2019).
[35] J. Zhou, B. Huang, Z. Yan, J.-C. G. Bünzli. Emerging role of machine learning in light-matter interaction. Light Sci. Appl., 8, 1(2019).
[36] D. Piccinotti, K. F. MacDonald, S. Gregory, I. Youngs, N. I. Zheludev. Artificial intelligence for photonics and photonic materials. Rep. Prog. Phys., 84, 012401(2020).
[37] J. Moughames, X. Porte, M. Thiel, G. Ulliac, L. Larger, M. Jacquot, M. Kadic, D. Brunner. Three-dimensional waveguide interconnects for scalable integration of photonic neural networks. Optica, 7, 640-646(2020).
[38] X. Porte, A. Skalli, N. Haghighi, S. Reitzenstein, J. A. Lott, D. Brunner. A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser(2020).
[39] L.-J. Black, Y. Wang, C. H. de Groot, A. Arbouet, O. L. Muskens. Optimal polarization conversion in coupled dimer plasmonic nanoantennas for metasurfaces. ACS Nano, 8, 6390-6399(2014).
[40] M. Celebrano, X. Wu, M. Baselli, S. Großmann, P. Biagioni, A. Locatelli, C. De Angelis, G. Cerullo, R. Osellame, B. Hecht, L. Duò, F. Ciccacci, M. Finazzi. Mode matching in multiresonant plasmonic nanoantennas for enhanced second harmonic generation. Nat. Nanotechnol., 10, 412-417(2015).
[41] J. S. Jensen, O. Sigmund. Topology optimization for nano-photonics. Laser Photonics Rev., 5, 308-321(2011).
[42] S. D. Campbell, D. Sell, R. P. Jenkins, E. B. Whiting, J. A. Fan, D. H. Werner. Review of numerical optimization techniques for meta-device design [Invited]. Opt. Mater. Express, 9, 1842-1863(2019).
[43] F. Meng, X. Huang, B. Jia. Bi-directional evolutionary optimization for photonic band gap structures. J. Comput. Phys., 302, 393-404(2015).
[44] S. Osher, J. A. Sethian. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys., 79, 12-49(1988).
[45] T. Feichtner, O. Selig, M. Kiunke, B. Hecht. Evolutionary optimization of optical antennas. Phys. Rev. Lett., 109, 127701(2012).
[46] P. R. Wiecha, P. R. Wiecha, C. Majorel, C. Girard, A. Cuche, V. Paillard, O. L. Muskens, A. Arbouet. Design of plasmonic directional antennas via evolutionary optimization. Opt. Express, 27, 29069-29081(2019).
[47] P. R. Wiecha, A. Arbouet, C. Girard, A. Lecestre, G. Larrieu, V. Paillard. Evolutionary multi-objective optimization of colour pixels based on dielectric nanoantennas. Nat. Nanotechnol., 12, 163-169(2017).
[48] D. Z. Zhu, E. B. Whiting, S. D. Campbell, D. B. Burckel, D. H. Werner. Optimal high efficiency 3D plasmonic metasurface elements revealed by lazy ants. ACS Photonics, 6, 2741-2748(2019).
[49] Y. Augenstein, C. Rockstuhl. Inverse design of nanophotonic devices with structural integrity. ACS Photonics, 7, 2190-2196(2020).
[50] R. Selle, G. Vogt, T. Brixner, G. Gerber, R. Metzler, W. Kinzel. Modeling of light-matter interactions with neural networks. Phys. Rev. A, 76, 023810(2007).
[51] R. Selle, T. Brixner, T. Bayer, M. Wollenhaupt, T. Baumert. Modelling of ultrafast coherent strong-field dynamics in potassium with neural networks. J. Phys. B, 41, 074019(2008).
[52] I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, H. Suchowski. Plasmonic nanostructure design and characterization via deep learning. Light Sci. Appl., 7, 60(2018).
[53] S. An, C. Fowler, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, C. Rivero-Baleine, K. A. Richardson, T. Gu, J. Hu, H. Zhang. A deep learning approach for objective-driven all-dielectric metasurface design. ACS Photonics, 6, 3196-3207(2019).
[54] M. V. Zhelyeznyakov, S. L. Brunton, A. Majumdar. Deep learning to accelerate Maxwell’s equations for inverse design of dielectric metasurfaces(2020).
[55] Y. Li, Y. Wang, S. Qi, Q. Ren, L. Kang, S. D. Campbell, P. L. Werner, D. H. Werner. Predicting scattering from complex nano-structures via deep learning. IEEE Access, 8, 139983(2020).
[56] S. So, J. Mun, J. Rho. Simultaneous inverse design of materials and structures via deep learning: demonstration of dipole resonance engineering using core–shell nanoparticles. ACS Appl. Mater. Interfaces, 11, 24264-24268(2019).
[57] A.-P. Blanchard-Dionne, O. J. F. Martin. Teaching optics to a machine learning network. Opt. Lett., 45, 2922-2925(2020).
[58] P. R. Wiecha, O. L. Muskens. Deep learning meets nanophotonics: a generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures. Nano Lett., 20, 329-338(2020).
[59] Y. Zhu, N. Zabaras, P.-S. Koutsourelakis, P. Perdikaris. Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data. J. Comput. Phys., 394, 56-81(2019).
[60] T. Bartz-Beielstein, T. Chugh, C. Sun, B. Filipič, P. Korošec, H. Wang, Y. Jin, E.-G. Talbi. Surrogate-assisted evolutionary optimization of large problems. High-Performance Simulation-Based Optimization, 165-187(2020).
[61] S. D. Campbell, D. Z. Zhu, E. B. Whiting, J. Nagar, D. H. Werner, P. L. Werner. Advanced multi-objective and surrogate-assisted optimization of topologically diverse metasurface architectures. Proc. SPIE, 10719, 107190U(2018).
[62] V. Kalt, A. K. González-Alcalde, S. Es-Saidi, R. Salas-Montiel, S. Blaize, D. Macías. Metamodeling of high-contrast-index gratings for color reproduction. J. Opt. Soc. Am. A, 36, 79-88(2019).
[63] A. K. González-Alcalde, R. Salas-Montiel, V. Kalt, S. Blaize, D. Macías. Engineering colors in all-dielectric metasurfaces: metamodeling approach. Opt. Lett., 45, 89-92(2020).
[64] R. Pestourie, Y. Mroueh, T. V. Nguyen, P. Das, S. G. Johnson. Active learning of deep surrogates for PDEs: application to metasurface design(2020).
[65] R. S. Hegde. Photonics inverse design: pairing deep neural networks with evolutionary algorithms. IEEE J. Sel. Top. Quantum Electron., 26, 7700908(2020).
[66] Z. A. Kudyshev, A. V. Kildishev, V. M. Shalaev, A. Boltasseva. Machine learning assisted global optimization of photonic devices. Nanophotonics, 10, 371-383(2020).
[67] J. Su, D. V. Vargas, S. Kouichi. One pixel attack for fooling deep neural networks. IEEE Trans. Evol. Comput., 23, 828-841(2019).
[68] J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, J. A. Fan. Free-form diffractive metagrating design based on generative adversarial networks. ACS Nano, 13, 8872-8878(2019).
[69] R. Trivedi, L. Su, J. Lu, M. F. Schubert, J. Vuckovic. Data-driven acceleration of photonic simulations. Sci. Rep., 9, 19728(2019).
[70] D. Liu, Y. Tan, E. Khoram, Z. Yu. Training deep neural networks for the inverse design of nanophotonic structures. ACS Photonics, 5, 1365-1369(2018).
[71] W. Ma, F. Cheng, Y. Liu. Deep-learning-enabled on-demand design of chiral metamaterials. ACS Nano, 12, 6326-6334(2018).
[72] L. Gao, X. Li, D. Liu, L. Wang, Z. Yu. A bidirectional deep neural network for accurate silicon color design. Adv. Mater., 31, 1905467(2019).
[73] S. An, B. Zheng, H. Tang, M. Y. Shalaginov, L. Zhou, H. Li, T. Gu, J. Hu, C. Fowler, H. Zhang. Multifunctional metasurface design with a generative adversarial network(2020).
[74] Z. Liu, D. Zhu, S. P. Rodrigues, K.-T. Lee, W. Cai. Generative model for the inverse design of metasurfaces. Nano Lett., 18, 6570-6576(2018).
[75] S. So, J. Rho. Designing nanophotonic structures using conditional deep convolutional generative adversarial networks. Nanophotonics, 8, 1255-1261(2019).
[76] A. Mall, A. Patil, D. Tamboli, A. Sethi, A. Kumar. Fast design of plasmonic metasurfaces enabled by deep learning. J. Phys. D, 53, 49LT01(2020).
[77] Z. Liu, L. Raju, D. Zhu, W. Cai. A hybrid strategy for the discovery and design of photonic structures. IEEE J. Emerging Sel. Top. Circuits Syst., 10, 126-135(2020).
[78] W. Ma, F. Cheng, Y. Xu, Q. Wen, Y. Liu. Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy. Adv. Mater., 31, 1901111(2019).
[79] X. Shi, T. Qiu, J. Wang, X. Zhao, S. Qu. Metasurface inverse design using machine learning approaches. J. Phys. D, 53, 275105(2020).
[80] W. Ma, Y. Liu. A data-efficient self-supervised deep learning model for design and characterization of nanophotonic structures. Sci. China Phys. Mech. Astron., 63, 284212(2020).
[81] D. P. Kingma, M. Welling. An introduction to variational autoencoders. Found. Trends Mach. Learn., 12, 307-392(2019).
[82] T. Badloe, I. Kim, J. Rho. Biomimetic ultra-broadband perfect absorbers optimised with reinforcement learning. Phys. Chem. Chem. Phys., 22, 2337-2342(2020).
[83] H. Wang, Z. Zheng, C. Ji, L. J. Guo. Automated multi-layer optical design via deep reinforcement learning. Mach. Learn. Sci. Technol.(2020).
[84] H. Wang, Z. Zheng, C. Ji, L. J. Guo. Automated multi-layer optical design via deep reinforcement learning. Mach. Learn. Sci. Technol., 2, 025013(2021).
[85] E. Ashalley, K. Acheampong, L. V. Besteiro, L. V. Besteiro, P. Yu, A. Neogi, A. O. Govorov, A. O. Govorov, Z. M. Wang. Multitask deep-learning-based design of chiral plasmonic metamaterials. Photon. Res., 8, 1213-1225(2020).
[86] J. Trisno, H. Wang, H. T. Wang, R. J. H. Ng, S. D. Rezaei, J. K. W. Yang. Applying machine learning to the optics of dielectric nano-blobs. Adv. Photonics Res., 1, 2000068(2020).
[87] J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, M. Soljačić. Nanophotonic particle simulation and inverse design using artificial neural networks. Sci. Adv., 4, eaar4206(2018).
[88] A. Sheverdin, F. Monticone, C. Valagiannopoulos. Photonic inverse design with neural networks: the case of invisibility in the visible. Phys. Rev. Appl., 14, 024054(2020).
[89] A.-P. Blanchard-Dionne, O. J. F. Martin. Successive training of a generative adversarial network for the design of an optical cloak. OSA Contin., 4, 87-95(2021).
[90] Y. Chen, L. Lu, G. E. Karniadakis, L. D. Negro. Physics-informed neural networks for inverse problems in nano-optics and metamaterials. Opt Express, 28, 11618-11633(2020).
[91] I. Sajedian, H. Lee, J. Rho. Double-deep Q-learning to increase the efficiency of metasurface holograms. Sci. Rep., 9, 10899(2019).
[92] A. D. Phan, C. V. Nguyen, P. T. Linh, T. V. Huynh, V. D. Lam, A.-T. Le, K. Wakabayashi. Deep learning for the inverse design of mid-infrared graphene plasmons. Crystals, 10, 125(2020).
[93] C. Yeung, J.-M. Tsai, B. King, B. Pham, J. Liang, D. Ho, M. W. Knight, A. P. Raman. Designing multiplexed supercell metasurfaces with tandem neural networks. Nanophotonics, 10, 1133-1143(2021).
[94] T. Asano, S. Noda. Iterative optimization of photonic crystal nanocavity designs by using deep neural networks. Nanophotonics, 8, 2243-2256(2019).
[95] F. Wen, J. Jiang, J. A. Fan. Robust freeform metasurface design based on progressively growing generative networks. ACS Photonics, 7, 2098-2104(2020).
[96] S. Wang, K. Fan, N. Luo, Y. Cao, F. Wu, C. Zhang, K. A. Heller, L. You. Massive computational acceleration by using neural networks to emulate mechanism-based biological models. Nat. Commun., 10, 4354(2019).
[97] J. Jiang, J. A. Fan. Multiobjective and categorical global optimization of photonic structures based on ResNet generative neural networks. Nanophotonics, 10, 361-369(2020).
[98] J. Jiang, J. A. Fan. Global optimization of dielectric metasurfaces using a physics-driven neural network. Nano Lett., 19, 5366-5372(2019).
[99] K. Deb. Multi-Objective Optimization Using Evolutionary Algorithms, 16(2001).
[100] R. Unni, K. Yao, Y. Zheng. Deep convolutional mixture density network for inverse design of layered photonic structures. ACS Photonics, 7, 2703-2712(2020).
[101] B. Hu, B. Wu, D. Tan, J. Xu, J. Xu, Y. Chen, Y. Chen. Robust inverse-design of scattering spectrum in core-shell structure using modified denoising autoencoder neural network. Opt. Express, 27, 36276-36285(2019).
[102] Z. Fang, J. Zhan. Deep physical informed neural networks for metamaterial design. IEEE Access, 8, 24506-24513(2020).
[103] O. Ronneberger, P. Fischer, T. Brox. U-Net: convolutional networks for biomedical image segmentation(2015).
[104] N. Borhani, E. Kakkava, C. Moser, D. Psaltis. Learning to see through multimode fibers. Optica, 5, 960-966(2018).
[105] H. Kabir, Y. Wang, M. Yu, Q.-J. Zhang. Neural network inverse modeling and applications to microwave filter design. IEEE Trans. Microwave Theory Tech., 56, 867-879(2008).
[106] C. Zhang, J. Jin, W. Na, Q.-J. Zhang, M. Yu. Multivalued neural network inverse modeling and applications to microwave filters. IEEE Trans. Microwave Theory Tech., 66, 3781-3797(2018).
[107] Y.-T. Luo, P.-Q. Li, D.-T. Li, Y.-G. Peng, Z.-G. Geng, S.-H. Xie, Y. Li, A. Alù, J. Zhu, X.-F. Zhu. Probability-density-based deep learning paradigm for the fuzzy design of functional metastructures. Research, 2020, 8757403(2020).
[108] J. Xie, F. Pereira, L. Xu, C. J. C. Burges, E. Chen, L. Bottou, K. Q. Weinberger. Image denoising and inpainting with deep neural networks. Advances in Neural Information Processing Systems, 25, 341-349(2012).
[109] H. Gao, L. Sun, J.-X. Wang. PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain. J. Comput. Phys., 428, 110079(2020).
[110] Z. Liu, Z. Liu, Z. Zhu, W. Cai. Topological encoding method for data-driven photonics inverse design. Opt. Express, 28, 4825-4835(2020).
[111] C. C. Nadell, B. Huang, J. M. Malof, W. J. Padilla. Deep learning for accelerated all-dielectric metasurface design. Opt. Express, 27, 27523-27535(2019).
[112] Y. Qu, L. Jing, Y. Shen, M. Qiu, M. Soljačić. Migrating knowledge between physical scenarios based on artificial neural networks. ACS Photonics, 6, 1168-1174(2019).
[113] M. Närhi, L. Salmela, J. Toivonen, C. Billet, J. M. Dudley, G. Genty. Machine learning analysis of extreme events in optical fibre modulation instability. Nat. Commun., 9, 4923(2018).
[114] M. Raissi, P. Perdikaris, G. E. Karniadakis. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys., 378, 686-707(2019).
[115] M. Raissi, A. Yazdani, G. E. Karniadakis. Hidden fluid mechanics: learning velocity and pressure fields from flow visualizations. Science, 367, 1026-1030(2020).
[116] B. Moseley, A. Markham, T. Nissen-Meyer. Solving the wave equation with physics-informed deep learning(2020).
[117] Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, A. Adibi. Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices. Adv. Theor. Simul., 2, 1900088(2019).
[118] Y. Kiarashinejad, S. Abdollahramezani, A. Adibi. Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures(2019).
[119] C. Yeung, J.-M. Tsai, B. King, Y. Kawagoe, D. Ho, M. Knight, A. P. Raman. Elucidating the behavior of nanophotonic structures through explainable machine learning algorithms. ACS Photonics, 7, 2309-2318(2020).
[120] R. Iten, T. Metger, H. Wilming, L. del Rio, R. Renner. Discovering physical concepts with neural networks. Phys. Rev. Lett., 124, 010508(2020).
[121] Y. Kiarashinejad, M. Zandehshahvar, S. Abdollahramezani, O. Hemmatyar, R. Pourabolghasem, A. Adibi. Knowledge discovery in nanophotonics using geometric deep learning. Adv. Intell. Syst., 2, 1900132(2020).
[122] S. An, B. Zheng, M. Y. Shalaginov, H. Tang, H. Li, L. Zhou, J. Ding, A. M. Agarwal, A. M. Agarwal, C. Rivero-Baleine, M. Kang, K. A. Richardson, T. Gu, J. Hu, C. Fowler, C. Fowler, H. Zhang, H. Zhang. Deep learning modeling approach for metasurfaces with high degrees of freedom. Opt. Express, 28, 31932-31942(2020).
[123] C. Yeung, J.-M. Tsai, Y. Kawagoe, B. King, D. Ho, A. P. Raman. Elucidating the design and behavior of nanophotonic structures through explainable convolutional neural networks(2020).
[124] M. Elzouka, C. Yang, A. Albert, S. Lubner, R. S. Prasher. Interpretable inverse design of particle spectral emissivity using machine learning(2020).
[125] B. Han, Y. Lin, Y. Yang, N. Mao, W. Li, H. Wang, V. Fatemi, L. Zhou, J. I.-J. Wang, Q. Ma, Y. Cao, D. Rodan-Legrain, Y.-Q. Bie, E. Navarro-Moratalla, D. Klein, D. MacNeill, S. Wu, W. S. Leong, H. Kitadai, X. Ling, P. Jarillo-Herrero, T. Palacios, J. Yin, J. Kong. Deep learning enabled fast optical characterization of two-dimensional materials(2019).
[126] M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, S. V. Kalinin. Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations. ACS Nano, 11, 12742-12752(2017).
[127] S. Shao, S. Shao, K. Mallery, K. Mallery, S. S. Kumar, S. S. Kumar, J. Hong, J. Hong. Machine learning holography for 3D particle field imaging. Opt. Express, 28, 2987-2999(2020).
[128] P. Zhang, S. Liu, A. Chaurasia, D. Ma, M. J. Mlodzianoski, E. Culurciello, F. Huang. Analyzing complex single-molecule emission patterns with deep learning. Nat. Methods, 15, 913-916(2018).
[129] A. M. Palmieri, E. Kovlakov, F. Bianchi, D. Yudin, S. Straupe, J. D. Biamonte, S. Kulik. Experimental neural network enhanced quantum tomography. npj Quantum Inf., 6, 20(2020).
[130] P. R. Wiecha, A. Lecestre, N. Mallet, G. Larrieu. Pushing the limits of optical information storage using deep learning. Nat. Nanotechnol., 14, 237-244(2019).
[131] Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M.-H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, Y. Park. Holographic deep learning for rapid optical screening of anthrax spores. Sci. Adv., 3, e1700606(2017).
[132] A. Yevick, M. Hannel, D. G. Grier. Machine-learning approach to holographic particle characterization. Opt. Express, 22, 26884-26890(2014).
[133] B. Midtvedt, E. Olsén, F. Eklund, F. Höök, C. B. Adiels, G. Volpe, D. Midtvedt. Holographic characterisation of subwavelength particles enhanced by deep learning(2020).
[134] A. Argun, T. Thalheim, S. Bo, F. Cichos, G. Volpe. Enhanced force-field calibration via machine learning. Appl. Phys. Rev., 7, 041404(2020).
[135] M. D. Hannel, A. Abdulali, M. O’Brien, D. G. Grier. Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles. Opt. Express, 26, 15221-15231(2018).
[136] J. M. Newby, A. M. Schaefer, P. T. Lee, M. G. Forest, S. K. Lai. Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D. Proc. Natl. Acad. Sci. USA, 115, 9026-9031(2018).
[137] S. Helgadottir, A. Argun, G. Volpe. Digital video microscopy enhanced by deep learning. Optica, 6, 506-513(2019).
[138] I. C. D. Lenton, G. Volpe, A. B. Stilgoe, T. A. Nieminen, H. Rubinsztein-Dunlop. Machine learning reveals complex behaviours in optically trapped particles. Mach. Learn. Sci. Technol., 1, 045009(2020).
[139] Y. Rivenson, Y. Zhang, H. Günaydn, D. Teng, A. Ozcan. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light Sci. Appl., 7, 17141(2018).
[140] Y. Nishizaki, R. Horisaki, K. Kitaguchi, M. Saito, J. Tanida. Analysis of non-iterative phase retrieval based on machine learning. Opt. Rev., 27, 136-141(2020).
[141] K. H. Jin, M. T. McCann, E. Froustey, M. Unser. Deep convolutional neural network for inverse problems in imaging. IEEE Trans. Image Process., 26, 4509-4522(2017).
[142] G. Ongie, A. Jalal, C. A. Metzler, R. G. Baraniuk, A. G. Dimakis, R. Willett. Deep learning techniques for inverse problems in imaging. IEEE J. Sel. Areas Inform. Theor., 1, 39-56(2020).
[143] G. Barbastathis, A. Ozcan, G. Situ. On the use of deep learning for computational imaging. Optica, 6, 921-943(2019).
[144] Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, A. Ozcan. Deep learning microscopy. Optica, 4, 1437-1443(2017).
[145] E. Nehme, L. E. Weiss, T. Michaeli, Y. Shechtman. Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica, 5, 458-464(2018).
[146] W. Ouyang, A. Aristov, M. Lelek, X. Hao, C. Zimmer. Deep learning massively accelerates super-resolution localization microscopy. Nat. Biotechnol., 36, 460-468(2018).
[147] E. Nehme, D. Freedman, R. Gordon, B. Ferdman, L. E. Weiss, O. Alalouf, T. Naor, R. Orange, T. Michaeli, Y. Shechtman. DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning. Nat. Methods, 17, 734-740(2020).
[148] T. Pu, J.-Y. Ou, V. Savinov, G. Yuan, N. Papasimakis, N. Zheludev. Unlabeled far-field deeply subwavelength topological microscopy (DSTM). Adv. Sci., 8, 2002886(2020).
[149] T. Pu, J. Y. Ou, N. Papasimakis, N. I. Zheludev. Label-free deeply subwavelength optical microscopy. Appl. Phys. Lett., 116, 131105(2020).
[150] D. Bouchet, J. Seifert, A. P. Mosk. Optimizing illumination for precise multi-parameter estimations in coherent diffractive imaging. Opt. Lett., 46, 254-257(2021).
[151] A. Ghosh, D. J. Roth, L. H. Nicholls, W. P. Wardley, A. V. Zayats, V. A. Podolskiy. Machine learning—based diffractive imaging with subwavelength resolution(2020).
[152] U. Kürüm, P. R. Wiecha, R. French, O. L. Muskens. Deep learning enabled real time speckle recognition and hyperspectral imaging using a multimode fiber array. Opt. Express, 27, 20965-20979(2019).
[153] R. Horisaki, R. Takagi, J. Tanida. Learning-based imaging through scattering media. Opt. Express, 24, 13738-13743(2016).
[154] L. Yunzhe, X. Yujia, T. Lei. Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media. Optica, 5, 1181-11819(2018).
[155] B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, C. Moser. Multimode optical fiber transmission with a deep learning network. Light Sci. Appl., 7, 69(2018).
[156] G. D. Bruce, L. O’Donnell, M. Chen, M. Facchin, K. Dholakia. Femtometer-resolved simultaneous measurement of multiple laser wavelengths in a speckle wavemeter. Opt. Lett., 45, 1926-1929(2020).
[157] S. Popoff, G. Lerosey, M. Fink, A. C. Boccara, S. Gigan. Image transmission through an opaque material. Nat. Commun., 1, 81(2010).
[158] R. French, S. Gigan, O. L. Muskens. Snapshot fiber spectral imaging using speckle correlations and compressive sensing. Opt. Express, 26, 32302-32316(2018).
[159] H. Pinkard, Z. Phillips, A. Babakhani, D. A. Fletcher, L. Waller. Deep learning for single-shot autofocus microscopy. Optica, 6, 794-797(2019).
[160] C. L. Cortes, S. Adhikari, X. Ma, S. K. Gray. Accelerating quantum optics experiments with statistical learning. Appl. Phys. Lett., 116, 184003(2020).
[161] Z. A. Kudyshev, S. I. Bogdanov, T. Isacsson, A. V. Kildishev, A. Boltasseva, V. M. Shalaev. Rapid classification of quantum sources enabled by machine learning. Adv. Quantum Technol., 3, 2000067(2020).
[162] C. You, M. A. Quiroz-Juárez, A. Lambert, N. Bhusal, C. Dong, A. Perez-Leija, A. Javaid, R. de. J. León-Montiel, O. S. Magaña-Loaiza. Identification of light sources using machine learning. Appl. Phys. Rev., 7, 021404(2020).
[163] Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydn, Y. Zhang, Z. Göröcs, K. Liang, D. Tseng, A. Ozcan. Deep learning enhanced mobile-phone microscopy. ACS Photonics, 5, 2354-2364(2018).
[164] X. Li, J. Dong, B. Li, Y. Zhang, Y. Zhang, A. Veeraraghavan, X. Ji. Fast confocal microscopy imaging based on deep learning. IEEE International Conference on Computational Photography (ICCP), 1-12(2020).
[165] J. M. Ede, R. Beanland. Partial scanning transmission electron microscopy with deep learning. Sci. Rep., 10, 8332(2020).
[166] S. L. Brunton, X. Fu, J. N. Kutz. Self-tuning fiber lasers. IEEE J. Sel. Top. Quantum Electron., 20, 464-471(2014).
[167] J. N. Kutz, S. L. Brunton. Intelligent systems for stabilizing mode-locked lasers and frequency combs: machine learning and equation-free control paradigms for self-tuning optics. Nanophotonics, 4, 459-471(2015).
[168] T. Baumeister, S. L. Brunton, J. N. Kutz. Deep learning and model predictive control for self-tuning mode-locked lasers. J. Opt. Soc. Am. B, 35, 617-626(2018).
[169] A. Youssry, R. J. Chapman, A. Peruzzo, C. Ferrie, M. Tomamichel. Modeling and control of a reconfigurable photonic circuit using deep learning. Quantum Sci. Technol., 5, 025001(2020).
[170] B. Wang, J. C. Cancilla, J. S. Torrecilla, H. Haick. Artificial sensing intelligence with silicon nanowires for ultraselective detection in the gas phase. Nano Lett., 14, 933-938(2014).
[171] . Google says sorry for racist auto-tag in photo app(2015).
[172] M. Schuld, I. Sinayskiy, F. Petruccione. An introduction to quantum machine learning. Contemp. Phys., 56, 172-185(2015).
[173] M. Krenn, M. Malik, R. Fickler, R. Lapkiewicz, A. Zeilinger. Automated search for new quantum experiments. Phys. Rev. Lett., 116, 090405(2016).
[174] A. A. Melnikov, H. P. Nautrup, M. Krenn, V. Dunjko, M. Tiersch, A. Zeilinger, H. J. Briegel. Active learning machine learns to create new quantum experiments. Proc. Natl. Acad. Sci. USA, 115, 1221-1226(2018).
[175] M. Krenn, M. Erhard, A. Zeilinger. Computer-inspired quantum experiments. Nat. Rev. Phys., 2, 649-661(2020).
