Page 186 - Read Online
P. 186

Page 22 of 24              He et al. Microstructures 2023;3:xxx  https://dx.doi.org/10.20517/microstructures.2023.29

                    Mater Des 2017;117:72-83.  DOI
               48.       Molesky S, Lin Z, Piggott AY, Jin W, Vucković J, Rodriguez AW. Inverse design in nanophotonics. Nat Photon 2018;12:659-70.
                    DOI
               49.       Goldberg DE, Holland JH. Genetic algorithms and machine learning. Mach Learn 1988;3:95-9.  DOI
               50.       Zhao Y, Cao X, Gao J, et al. Broadband diffusion metasurface based on a single anisotropic element and optimized by the simulated
                    annealing algorithm. Sci Rep 2016;6:23896.  DOI  PubMed  PMC
               51.       Robinson J, Rahmat-Samii Y. Particle swarm optimization in electromagnetics. IEEE Trans Antennas Propag 2004;52:397-407.  DOI
               52.       Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol 2022;23:40-55.
                    DOI  PubMed
               53.       Fischer T, Krauss C. Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res
                    2018;270:654-69.  DOI
               54.       Li W, Chen P, Xiong B, et al. Deep learning modeling strategy for material science: from natural materials to metamaterials. J Phys
                    Mater 2022;5:014003.  DOI
               55.       Goh GB, Hodas NO, Vishnu A. deep learning for computational chemistry. J Comput Chem 2017;38:1291-307.  DOI  PubMed
               56.       Oishi A, Yagawa G. Computational mechanics enhanced by deep learning. Comput Methods Appl Mech Eng 2017;327:327-51.  DOI
               57.       Yao K, Unni R, Zheng Y. Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale. Nanophotonics
                    2019;8:339-66.  DOI  PubMed  PMC
               58.       Ma W, Liu Z, Kudyshev ZA, Boltasseva A, Cai W, Liu Y. Deep learning for the design of photonic structures. Nat Photonics
                    2021;15:77-90.  DOI
               59.       Jiang J, Chen M, Fan JA. Deep neural networks for the evaluation and design of photonic devices. Nat Rev Mater 2021;6:679-700.
                    DOI
               60.       Wang N, Yan W, Qu Y, Ma S, Li SZ, Qiu M. Intelligent designs in nanophotonics: from optimization towards inverse creation.
                    PhotoniX 2021;2:22.  DOI
               61.       Piccinotti D, MacDonald KF, A Gregory S, Youngs I, Zheludev NI. Artificial intelligence for photonics and photonic materials. Rep
                    Prog Phys 2021;84:012401.  DOI  PubMed
               62.       Chen J, Hu S, Zhu S, Li T. Metamaterials: from fundamental physics to intelligent design. Interdiscip Mater 2023;2:5-29.  DOI
               63.       Zhang Q, Yu H, Barbiero M, Wang B, Gu M. Artificial neural networks enabled by nanophotonics. Light Sci Appl 2019;8:42.  DOI
                    PubMed  PMC
               64.       Xu Y, Zhang X, Fu Y, Liu Y. Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures
                    and devices based on artificial neural networks. Photon Res 2021;9:B135-52.  DOI
               65.       Wiecha PR, Arbouet A, Girard C, Muskens OL. Deep learning in nano-photonics: inverse design and beyond. Photon Res
                    2021;9:B182-200.  DOI
               66.       So S, Badloe T, Noh J, Bravo-abad J, Rho J. Deep learning enabled inverse design in nanophotonics. Nanophotonics 2020;9:1041-57.
                    DOI
               67.       Khatib O, Ren S, Malof J, Padilla WJ. Deep learning the electromagnetic properties of metamaterials - a comprehensive review. Adv
                    Funct Mater 2021;31:2101748.  DOI
               68.       Jiao P, Alavi AH. Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends. Int Mater Rev
                    2021;66:365-93.  DOI
               69.      Jin Y, He L, Wen Z, et al. Intelligent on-demand design of phononic metamaterials. Nanophotonics 2022;11:439-60.  DOI
               70.       Muhammad, Kennedy J, Lim C. Machine learning and deep learning in phononic crystals and metamaterials - a review. Mater
                    Today Commun 2022;33:104606.  DOI
               71.      Liu C, Yu G. Deep learning for the design of phononic crystals and elastic metamaterials. J Computat Des Eng 2023;10:602-14.  DOI
               72.       Russell S, Norvig P. Artificial intelligence: a modern approach, 4th US ed. Prentice Hall 2009. Available from: http://aima.cs.
                    berkeley.edu/index.html [Last accessed on 14 Aug 2023].
               73.      Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science 2015;349:255-60.  DOI  PubMed
               74.      Mcculloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 1943;5:115-33.  DOI
               75.       Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev
                    1958;65:386-408.  DOI  PubMed
               76.       Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature 1986;323:533-6.  DOI
               77.       Elman J. Finding structure in time. Cogn Sci 1990;14:179-211.  DOI
               78.       LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE 1998;86:2278-324.
                    DOI
               79.      Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput 2006;18:1527-54.  DOI  PubMed
               80.       Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND,
                    Weinberger KQ editors. In Proceedings of the 27th International Conference on Neural Information Processing Systems; 2014 Dec
                    8-13;  Montreal,  Canada.  Cambridge:  MIT  Press;  2014.  pp.  2672-80.
               81.       Mirza M, Osindero S. Conditional generative adversarial nets. Available from: https://arxiv.org/abs/1411.1784 [Last accessed
                    on 14 Aug 2023].
   181   182   183   184   185   186   187   188   189   190   191