Vibration transmission characteristic prediction and structure inverse design of acoustic metamaterial beams based on deep learning

Abstract

In this paper, an intelligent prediction and design method of acoustic metamaterial beams based on deep learning is presented. A theoretical model of acoustic metamaterial beams is derived by using the spectral element method to calculate the band structure and transmission characteristic of beam-type acoustic metamaterial. A high-degree-of-freedom design space dataset of band structure and transmission characteristics is constructed. In addition, the vibration transmission characteristics of acoustic metamaterial beams are predicted and the on-demand inverse design of acoustic metamaterial beams is realized by constructing a fully connected deep learning neural network model. Furthermore, the forward prediction and reverse design network model is verified by using the autoencoder neural network model. Results show that the predicted value of the autoencoder neural network structure is in good agreement with the target value, which indicates the feasibility of the intelligent design method of acoustic metamaterials based on deep learning. The presented intelligent design method could be potentially utilized in the field of fast and efficient acoustic metamaterial design for low frequency vibration isolation in engineering structures.

Machine Intelligence in Metamaterials Design

“…With time, it will be integrated into all levels of technology readiness, becoming commonplace as an assistive tool in the computational design of metamaterials. Weng, Ding, Hu, et al [266] 2020 Deep Learning Classification 10 Melo Filho, Angeli, Ophem, et al [267] 2020 ANN Inverse Design 11 Chen, Lu, Karniadakis, et al [268] 2020 Deep Learning Inverse Design 12 Kollmann, Abueidda, Koric, et al [269] 2020 Deep Learning Optimization Framework 13 Qu, Zhu, Shen, et al [270] 2020 ANN Optimization Framework 14 Lai, Amirkulova, and Gerstoft [271] 2021 CNN, GAN Inverse Design 15 Gurbuz, Kronowetter, Dietz, et al [272] 2021 GAN Inverse Design 16 Amirkulova, Tran, and Khatami [273] 2021 Deep Learning Inverse Design 17 Wu, Liu, Jahanshahi, et al [274] 2021 ANN Inverse Design 18 Shah, Zhuo, Lai, et al [275] 2021 RL Optimization Framework 19 Tran, Amirkulova, and Khatami [276] 2022 ANN Inverse Design 20 Wiest, Seepersad, and Haberman [277] 2022 GNN Inverse Design 21 Amirkulova, Zhou, Abbas, et al [278] 2022 Deep Learning Inverse Design 22 Tran, Khatami, and Amirkulova [279] 2022 CNN Inverse Design 23 Li, Chen, Li, et al [280] 2023 CNN Inverse Design 24 Li, Chen, Li, et al [281] 2023 Deep Learning Inverse Design 25 Wang, Chen, Xu, et al [282] 2023 ANN Inverse Design Application field: Electromagnetics 26 Jiang, Xiao, Liu, et al [283] 2010 ANN and scaled conjugate Surrogate model gradient 27 Freitas, Rêgo, and Vasconcelos [140] 2011 ANN Surrogate model 28 Vasconcelos, Rêgo, and Cruz [139] 2012 ANN Surrogate model 29 Sarmah, Sarma, and Baruah [172] 2015 ANN optimization framework 30 Saha and Maity [138] 2016 ANN Surrogate model 31 Nanda, Sahu, and Mishra [108] 2019 ANN Inverse design 32 An, Fowler, Shalaginov, et al [144] 2019 ANN Surrogate model 33 Yuze, Hai, and Qinglin [157] 2019 CNN Classification and clustering 34 Liu, Zhang, and Cui [156] 2019 CNN optimization framework 35 Hodge, Mishra, and Zaghloul [284] 2019 DC-GAN Inverse design 36 Hodge, Mishra, and Zaghloul…”

Neural Network-Based Inverse Design of Nonlinear Phononic Crystals

“…For the longitudinal wave propagation of this model, the material density ratio ζ, wave velocity ratio η, and normalized interface linear stiffness K are the main physical parameters affecting the second harmonic behavior, and the material thickness ratio Λ is also an important factor. ζ, η, K, Λ are defined as (12). K max is the largest value in the K data set.…”