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Inverse design of phononic crystals for anticipated wave propagation by integrating deep learning and semi-analytical approach

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Abstract

Inversely designing and optimizing topological structures of phononic crystals that dominate extraordinary wave characteristics has become a research hotspot. In this study, a joint framework combining a data-driven deep learning model with the semi-analytical two-dimensional periodic spectral finite element method is applied to achieve the inverse design and optimization of topology. A convolutional neural network and a generative adversarial network are trained for inverse design. Meanwhile, the semi-analytical periodic approach is utilized to analyze the wave characteristics of two-dimensional phononic crystals. Through the proactive tuning of the band structures, the topologies of phononic crystals are inversely on-demand designed and optimized for the anticipated partial bandgap or complete bandgap, respectively, which revealed the unidirectional wave transmission or vibration isolation characteristics within the desired frequency segment. The unidirectional wave propagation and vibration isolation performance are validated through numerical simulations. This work holds the potential to benefit the design, optimization, and application of metamaterials.

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Acknowledgements

The authors wish to acknowledge the support from National Natural Science Foundation of China (11972160), Guangdong Basic and Applied Basic Research Foundation(2022A1515010143), Young Talent Support Project of Guangzhou Association for Science and Technology (QT2023013) and Science and Technology Program of Guangzhou (2023A04J1302).

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  1. Department of Engineering Mechanics, School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, Guangdong Province, People’s Republic of China

    Sihao Han, Qiang Han, Tengjiao Jiang & Chunlei Li

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  1. Sihao Han
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Han, S., Han, Q., Jiang, T. et al. Inverse design of phononic crystals for anticipated wave propagation by integrating deep learning and semi-analytical approach. Acta Mech 234, 4879–4897 (2023). https://doi.org/10.1007/s00707-023-03634-y

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