Page 187 - Read Online

He et al. Microstructures 2023;3:xxx https://dx.doi.org/10.20517/microstructures.2023.29 Page 23 of 24

82. Liu D, Tan Y, Khoram E, Yu Z. Training deep neural networks for the inverse design of nanophotonic structures. ACS Photonics
2018;5:1365-9. DOI
83. Kaelbling LP, Littman ML, Moore AW. Reinforcement learning: a survey. J Artif Intell Res 1996;4:237-85. Available from: https://
arxiv.org/abs/cs/9605103 [Last accessed on 14 Aug 2023].
84. Silver D, Schrittwieser J, Simonyan K, et al. Mastering the game of go without human knowledge. Nature 2017;550:354-9. DOI
85. Kiran BR, Sobh I, Talpaert V, et al. Deep reinforcement learning for autonomous driving: a survey. IEEE Trans Intell Transport Syst
2022;23:4909-26. DOI
86. Zhu R, Qiu T, Wang J, et al. Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer
learning. Nat Commun 2021;12:2974. DOI PubMed PMC
87. Kim Y, Kim Y, Yang C, Park K, Gu GX, Ryu S. Deep learning framework for material design space exploration using active transfer
learning and data augmentation. NPJ Comput Mater 2021;140:7. DOI
88. Alzubi J, Nayyar A, Kumar A. Machine learning from theory to algorithms: an overview. J Phys Conf Ser 2018;1142:012012. DOI
89. Mahesh B. Machine learning algorithms - a review. Int J Sci Res 2020;9:381-6. Available from: https://www.ijsr.net/
getabstract.php?paperid=ART20203995 [Last accessed on 9 Oct 2023].
90. Abadi Mı, Agarwal A, Barham P, et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. Available
from: https://arxiv.org/abs/1603.04467 [Last accessed on 14 Aug 2023].
91. Paszke A, Gross S, Massa F, et al. PyTorch: an imperative style, high-performance deep learning library. Available from: https://
arxiv.org/abs/1912.01703 [Last accessed on 14 Aug 2023].
92. Liu CX, Yu GL. Predicting the dispersion relations of one-dimensional phononic crystals by neural networks. Sci Rep 2019;9:15322.
DOI PubMed PMC
93. Zhang J, Li Y, Zhao T, Zhang Q, Zuo L, Zhang K. Machine-learning based design of digital materials for elastic wave control.
Extreme Mech Lett 2021;48:101372. DOI
94. Jiang W, Zhu Y, Yin G, Lu H, Xie L, Yin M. Dispersion relation prediction and structure inverse design of elastic metamaterials via
deep learning. Mater Today Phys 2022;22:100616. DOI
95. Han S, Han Q, Li C. Deep-learning-based inverse design of phononic crystals for anticipated wave attenuation. J Appl Phys
2022;132:154901. DOI
96. Liu C, Yu G, Zhao G. Neural networks for inverse design of phononic crystals. AIP Adv 2019;9:085223. DOI
97. Dong J, Qin Q, Xiao Y. Nelder-mead optimization of elastic metamaterials via machine-learning-aided surrogate modeling. Int J Appl
Mech 2020;12:2050011. DOI
98. Wu L, Liu L, Wang Y, et al. A machine learning-based method to design modular metamaterials. Extreme Mech Lett
2020;36:100657. DOI
99. Li X, Ning S, Liu Z, Yan Z, Luo C, Zhuang Z. Designing phononic crystal with anticipated band gap through a deep learning based
data-driven method. Comput Methods Appl Mech Eng 2020;361:112737. DOI
100. Miao X, Dong HW, Wang Y. Deep learning of dispersion engineering in two-dimensional phononic crystals. Eng Optim
2023;55:125-39. DOI
101. Jin Y, Zeng S, Wen Z, He L, Li Y, Li Y. Deep-subwavelength lightweight metastructures for low-frequency vibration isolation.
Mater Des 2022;215:110499. DOI
102. On S, Moon H, Yeon Park S, et al. Design of periodic arched structures integrating the structural nonlinearity and band gap effect
for vibration isolation. Mater Des 2022;224:111397. DOI
103. Luo C, Ning S, Liu Z, Zhuang Z. Interactive inverse design of layered phononic crystals based on reinforcement learning. Extreme
Mech Lett 2020;36:100651. DOI
104. Wu R, Liu T, Jahanshahi MR, Semperlotti F. Design of one-dimensional acoustic metamaterials using machine learning and cell
concatenation. Struct Multidisc Optim 2021;63:2399-423. DOI
105. He L, Guo H, Jin Y, Zhuang X, Rabczuk T, Li Y. Machine-learning-driven on-demand design of phononic beams. Sci China Phys
Mech Astron 2022;65:214612. DOI
106. Donda K, Zhu Y, Merkel A, et al. Ultrathin acoustic absorbing metasurface based on deep learning approach. Smart Mater Struct
2021;30:085003. DOI
107. Donda K, Zhu Y, Merkel A, Wan S, Assouar B. Deep learning approach for designing acoustic absorbing metasurfaces with high
degrees of freedom. Extreme Mech Lett 2022;56:101879. DOI
108. Zhang H, Wang Y, Zhao H, Lu K, Yu D, Wen J. Accelerated topological design of metaporous materials of broadband sound
absorption performance by generative adversarial networks. Mater Des 2021;207:109855. DOI
109. Liu L, Xie L, Huang W, Zhang XJ, Lu M, Chen Y. Broadband acoustic absorbing metamaterial via deep learning approach. Appl
Phys Lett 2022;120:251701. DOI
110. Jin Y, Yang Y, Wen Z, et al. Lightweight sound-absorbing metastructures with perforated fish-belly panels. Int J Mech Sci
2022;226:107396. DOI
111. Gu T, Wen Z, He L, et al. A lightweight metastructure for simultaneous low-frequency broadband sound absorption and vibration
isolation. J Acoust Soc Am 2023;153:96-104. DOI
112. Mahesh K, Kumar Ranjith S, Mini RS. Inverse design of a Helmholtz resonator based low-frequency acoustic absorber using deep
neural network. J Appl Phys 2021;129:174901. DOI