REFERENCES

1. Kumar R, Kumar M, Chohan JS, Kumar S. Overview on metamaterial: history, types and applications. Mater Today Proc 2022;56:3016-24.

2. Ma G, Sheng P. Acoustic metamaterials: from local resonances to broad horizons. Sci Adv 2016;2:e1501595.

3. Yu X, Zhou J, Liang H, Jiang Z, Wu L. Mechanical metamaterials associated with stiffness, rigidity and compressibility: a brief review. Prog Mater Sci 2018;94:114-73.

4. Cui T. Electromagnetic metamaterials - from effective media to field programmable systems. Sci Sin Inf 2020;50:1427.

5. Jin Y, Pennec Y, Bonello B, et al. Physics of surface vibrational resonances: pillared phononic crystals, metamaterials, and metasurfaces. Rep Prog Phys 2021;84:086502.

6. Wu X, Wen Z, Jin Y, Rabczuk T, Zhuang X, Djafari-rouhani B. Broadband rayleigh wave attenuation by gradient metamaterials. Int J Mech Sci 2021;205:106592.

7. Cao L, Yang Z, Xu Y, et al. Flexural wave absorption by lossy gradient elastic metasurface. J Mech Phy Solids 2020;143:104052.

8. Huang S, Zhou Z, Li D, et al. Compact broadband acoustic sink with coherently coupled weak resonances. Sci Bull 2020;65:373-9.

9. Jin Y, Wang W, Khelif A, Djafari-rouhani B. Elastic metasurfaces for deep and robust subwavelength focusing and imaging. Phys Rev Appl 2021;15:024005.

10. Wang W, Iglesias J, Jin Y, Djafari-rouhani B, Khelif A. Experimental realization of a pillared metasurface for flexural wave focusing. APL Mater 2021;9:051125.

11. Wen Z, Jin Y, Gao P, Zhuang X, Rabczuk T, Djafari-rouhani B. Topological cavities in phononic plates for robust energy harvesting. Mech Syst Signal Process 2022;162:108047.

12. Wen Z, Wang W, Khelif A, Djafari-rouhani B, Jin Y. A perspective on elastic metastructures for energy harvesting. Appl Phys Lett 2022;120:020501.

13. He H, Qiu C, Ye L, et al. Topological negative refraction of surface acoustic waves in a Weyl phononic crystal. Nature 2018;560:61-4.

14. Jin Y, Fang X, Li Y, Torrent D. Engineered diffraction gratings for acoustic cloaking. Phys Rev Appl 2019;11:011004.

15. Zhou H, Fu W, Wang Y, Wang Y, Laude V, Zhang C. Ultra-broadband passive acoustic metasurface for wide-angle carpet cloaking. Mater Des 2021;199:109414.

16. Zhang X, Xiao M, Cheng Y, Lu M, Christensen J. Topological sound. Commun Phys 2018;1:97.

17. Kushwaha MS, Halevi P, Dobrzynski L, Djafari-Rouhani B. Acoustic band structure of periodic elastic composites. Phys Rev Lett 1993;71:2022-5.

18. Martínez-sala R, Sancho J, Sánchez JV, Gómez V, Llinares J, Meseguer F. Sound attenuation by sculpture. Nature 1995;378:241.

19. Liu Z, Zhang X, Mao Y, et al. Locally resonant sonic materials. Science 2000;289:1734-6.

20. Liao G, Luan C, Wang Z, Liu J, Yao X, Fu J. Acoustic metamaterials: a review of theories, structures, fabrication approaches, and applications. Adv Mater Technol 2021;6:2000787.

21. Yang Z, Mei J, Yang M, Chan NH, Sheng P. Membrane-type acoustic metamaterial with negative dynamic mass. Phys Rev Lett 2008;101:204301.

22. Fang N, Xi D, Xu J, et al. Ultrasonic metamaterials with negative modulus. Nat Mater 2006;5:452-6.

23. Ding C, Hao L, Zhao X. Two-dimensional acoustic metamaterial with negative modulus. J Appl Phys 2010;108:074911.

24. Lee SH, Park CM, Seo YM, Wang ZG, Kim CK. Composite acoustic medium with simultaneously negative density and modulus. Phys Rev Lett 2010;104:054301.

25. Li J, Chan CT. Double-negative acoustic metamaterial. Phys Rev E 2004;70:055602.

26. Yu N, Genevet P, Kats MA, et al. Light propagation with phase discontinuities: generalized laws of reflection and refraction. Science 2011;334:333-7.

27. Assouar B, Liang B, Wu Y, Li Y, Cheng J, Jing Y. Acoustic metasurfaces. Nat Rev Mater 2018;3:460-72.

28. Qi S, Li Y, Assouar B. Acoustic focusing and energy confinement based on multilateral metasurfaces. Phys Rev Appl 2017;7:054006.

29. Faure C, Richoux O, Félix S, Pagneux V. Experiments on metasurface carpet cloaking for audible acoustics. Appl Phys Lett 2016;108:064103.

30. Sounas DL, Fleury R, Alù A. Unidirectional cloaking based on metasurfaces with balanced loss and gain. Phys Rev Appl 2015;4:014005.

31. Huang S, Fang X, Wang X, Assouar B, Cheng Q, Li Y. Acoustic perfect absorbers via spiral metasurfaces with embedded apertures. Appl Phys Lett 2018;113:233501.

32. Ji J, Li D, Li Y, Jing Y. Low-frequency broadband acoustic metasurface absorbing panels. Front Mech Eng 2020;6:586249.

33. Li Y, Shen C, Xie Y, et al. Tunable asymmetric transmission via lossy acoustic metasurfaces. Phys Rev Lett 2017;119:035501.

34. Fang X, Wang X, Li Y. Acoustic splitting and bending with compact coding metasurfaces. Phys Rev Appl 2019;11:064033.

35. Wang P, Lu L, Bertoldi K. Topological phononic crystals with one-way elastic edge waves. Phys Rev Lett 2015;115:104302.

36. Torrent D, Mayou D, Sánchez-dehesa J. Elastic analog of graphene: dirac cones and edge states for flexural waves in thin plates. Phys Rev B 2013;87:115143.

37. Lera N, Torrent D, San-jose P, Christensen J, Alvarez JV. Valley hall phases in kagome lattices. Phys Rev B 2019;99:134102.

38. Chaunsali R, Chen C, Yang J. Subwavelength and directional control of flexural waves in zone-folding induced topological plates. Phys Rev B 2018;97:054307.

39. Lu J, Qiu C, Ye L, et al. Observation of topological valley transport of sound in sonic crystals. Nat Phys 2017;13:369-74.

40. Wang H, Guo G, Jiang J. Band topology in classical waves: wilson-loop approach to topological numbers and fragile topology. New J Phys 2019;21:093029.

41. Fukui T, Hatsugai Y, Suzuki H. Chern numbers in discretized brillouin zone: efficient method of computing (spin) hall conductances. J Phys Soc Jpn 2005;74:1674-7.

42. Huo SY, Chen JJ, Huang HB. Topologically protected edge states for out-of-plane and in-plane bulk elastic waves. J Phys Condens Matter 2018;30:145403.

43. He C, Ni X, Ge H, et al. Acoustic topological insulator and robust one-way sound transport. Nat Phys 2016;12:1124-9.

44. Zhai Z, Wu L, Jiang H. Mechanical metamaterials based on origami and kirigami. Appl Phys Rev 2021;8:041319.

45. Wu W, Hu W, Qian G, Liao H, Xu X, Berto F. Mechanical design and multifunctional applications of chiral mechanical metamaterials: a review. Mater Des 2019;180:107950.

46. Zheng X, Lee H, Weisgraber TH, et al. Ultralight, ultrastiff mechanical metamaterials. Science 2014;344:1373-7.

47. Ingrole A, Hao A, Liang R. Design and modeling of auxetic and hybrid honeycomb structures for in-plane property enhancement. Mater Des 2017;117:72-83.

48. Molesky S, Lin Z, Piggott AY, Jin W, Vucković J, Rodriguez AW. Inverse design in nanophotonics. Nat Photon 2018;12:659-70.

49. Goldberg DE, Holland JH. Genetic algorithms and machine learning. Mach Learn 1988;3:95-9.

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.

51. Robinson J, Rahmat-Samii Y. Particle swarm optimization in electromagnetics. IEEE Trans Antennas Propag 2004;52:397-407.

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.

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.

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.

55. Goh GB, Hodas NO, Vishnu A. deep learning for computational chemistry. J Comput Chem 2017;38:1291-307.

56. Oishi A, Yagawa G. Computational mechanics enhanced by deep learning. Comput Methods Appl Mech Eng 2017;327:327-51.

57. Yao K, Unni R, Zheng Y. Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale. Nanophotonics 2019;8:339-66.

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.

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.

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.

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.

62. Chen J, Hu S, Zhu S, Li T. Metamaterials: from fundamental physics to intelligent design. Interdiscip Mater 2023;2:5-29.

63. Zhang Q, Yu H, Barbiero M, Wang B, Gu M. Artificial neural networks enabled by nanophotonics. Light Sci Appl 2019;8:42.

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.

65. Wiecha PR, Arbouet A, Girard C, Muskens OL. Deep learning in nano-photonics: inverse design and beyond. Photon Res 2021;9:B182.

66. So S, Badloe T, Noh J, Bravo-abad J, Rho J. Deep learning enabled inverse design in nanophotonics. Nanophotonics 2020;9:1041-57.

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.

68. Jiao P, Alavi AH. Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends. Int Mater Rev 2021;66:365-93.

69. Jin Y, He L, Wen Z, et al. Intelligent on-demand design of phononic metamaterials. Nanophotonics 2022;11:439-60.

70. Kennedy J, Lim C. Machine learning and deep learning in phononic crystals and metamaterials - a review. Mater Today Commun 2022;33:104606.

71. Liu C, Yu G. Deep learning for the design of phononic crystals and elastic metamaterials. J Computat Des Eng 2023;10:602-14.

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.

74. Mcculloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 1943;5:115-33.

75. ROSENBLATT F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 1958;65:386-408.

76. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature 1986;323:533-6.

77. Elman J. Finding structure in time. Cogn Sci 1990;14:179-211.

78. LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE 1998;86:2278-324.

79. Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput 2006;18:1527-54.

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. Available from: https://dl.acm.org/doi/10.5555/2969033.2969125 [Last accessed on 14 Aug 2023].

81. Mirza M, Osindero S. Conditional generative adversarial nets. Available from: https://arxiv.org/abs/1411.1784 [Last accessed on 14 Aug 2023].

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.

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.

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.

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.

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.

88. Alzubi J, Nayyar A, Kumar A. Machine learning from theory to algorithms: an overview. J Phys Conf Ser 2018;1142:012012.

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.

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.

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.

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.

96. Liu C, Yu G, Zhao G. Neural networks for inverse design of phononic crystals. AIP Adv 2019;9:085223.

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.

98. Wu L, Liu L, Wang Y, et al. A machine learning-based method to design modular metamaterials. Extreme Mech Lett 2020;36:100657.

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.

100. Miao X, Dong HW, Wang Y. Deep learning of dispersion engineering in two-dimensional phononic crystals. Eng Optim 2023;55:125-39.

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.

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.

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.

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.

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.

106. Donda K, Zhu Y, Merkel A, et al. Ultrathin acoustic absorbing metasurface based on deep learning approach. Smart Mater Struct 2021;30:085003.

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.

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.

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.

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.

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.

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.

113. Mahesh K, Ranjith SK, Mini RS. A deep autoencoder based approach for the inverse design of an acoustic-absorber. Eng Comput 2023; doi: 10.1007/s00366-023-01789-9.

114. Luo YT, Li PQ, Li DT, et al. Probability-density-based deep learning paradigm for the fuzzy design of functional metastructures. Research 2020;2020:8757403.

115. Gurbuz C, Kronowetter F, Dietz C, Eser M, Schmid J, Marburg S. Generative adversarial networks for the design of acoustic metamaterials. J Acoust Soc Am 2021;149:1162.

116. Ding H, Fang X, Jia B, Wang N, Cheng Q, Li Y. Deep learning enables accurate sound redistribution via nonlocal metasurfaces. Phys Rev Appl 2021;16:064035.

117. Du Z, Mei J. Metagrating-based acoustic wavelength division multiplexing enabled by deterministic and probabilistic deep learning models. Phys Rev Res 2022;4:033165.

118. Ahmed WW, Farhat M, Zhang X, Wu Y. Deterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak. Phys Rev Res 2021;3:013142.

119. Zhao T, Li Y, Zuo L, Zhang K. Machine-learning optimized method for regional control of sound fields. Extreme Mech Lett 2021;45:101297.

120. Chen J, Chen Y, Xu X, Zhou W, Huang G. A physics-guided machine learning for multifunctional wave control in active metabeams. Extreme Mech Lett 2022;55:101827.

121. Long Y, Ren J, Chen H. Unsupervised manifold clustering of topological phononics. Phys Rev Lett 2020;124:185501.

122. He L, Wen Z, Jin Y, Torrent D, Zhuang X, Rabczuk T. Inverse design of topological metaplates for flexural waves with machine learning. Mater Des 2021;199:109390.

123. Ogun O, Kennedy J. Inverse design of a topological phononic beam with interface modes. J Phys D Appl Phys 2022;56:015106.

124. Du Z, Ding X, Chen H, et al. Optimal design of topological waveguides by machine learning. Front Mater 2022;9:1075073.

125. Yu LW, Deng DL. Unsupervised learning of non-Hermitian topological phases. Phys Rev Lett 2021;126:240402.

126. Cheng Z, Yu Z. Supervised machine learning topological states of one-dimensional non-Hermitian systems. Chin Phys Lett 2021;38:070302.

127. Narayan B, Narayan A. Machine learning non-Hermitian topological phases. Phys Rev B 2021:103.

128. Zhang L, Tang L, Huang Z, Zhang G, Huang W, Zhang D. Machine learning topological invariants of non-Hermitian systems. Phys Rev A 2021:103.

129. Miri MA, Alù A. Exceptional points in optics and photonics. Science 2019;363:eaar7709.

130. Reja MA, Narayan A. Characterizing exceptional points using neural networks. Available from: https://arxiv.org/abs/2305.00776 [Last accessed on 14 Aug 2023].

131. Gu GX, Chen C, Richmond DJ, Buehler MJ. Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment. Mater Horiz 2018;5:939-45.

132. Hanakata PZ, Cubuk ED, Campbell DK, Park HS. Accelerated search and design of stretchable graphene kirigami using machine learning. Phys Rev Lett 2018;121:255304.

133. Hanakata PZ, Cubuk ED, Campbell DK, Park HS. Forward and inverse design of kirigami via supervised autoencoder. Phys Rev Res 2020;2:042006.

134. Kollmann HT, Abueidda DW, Koric S, Guleryuz E, Sobh NA. Deep learning for topology optimization of 2D metamaterials. Mater Des 2020;196:109098.

135. Tan RK, Zhang NL, Ye W. A deep learning - based method for the design of microstructural materials. Struct Multidisc Optim 2020;61:1417-38.

136. Garland AP, White BC, Jensen SC, Boyce BL. Pragmatic generative optimization of novel structural lattice metamaterials with machine learning. Mater Des 2021;203:109632.

137. Tian J, Tang K, Chen X, Wang X. Machine learning-based prediction and inverse design of 2D metamaterial structures with tunable deformation-dependent Poisson's ratio. Nanoscale 2022;14:12677-91.

138. Liu F, Jiang X, Wang X, Wang L. Machine learning-based design and optimization of curved beams for multistable structures and metamaterials. Extreme Mech Lett 2020;41:101002.

139. Challapalli A, Patel D, Li G. Inverse machine learning framework for optimizing lightweight metamaterials. Mater Des 2021;208:109937.

140. Wang Y, Zeng Q, Wang J, Li Y, Fang D. Inverse design of shell-based mechanical metamaterial with customized loading curves based on machine learning and genetic algorithm. Comput Methods Appl Mech Eng 2022;401:115571.

141. Chang Y, Wang H, Dong Q. Machine learning-based inverse design of auxetic metamaterial with zero Poisson's ratio. Mater Today Commun 2022;30:103186.

Microstructures
ISSN 2770-2995 (Online)

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/