On-demand inverse design of acoustic metamaterials using probabilistic generation network  

作　　者：Ze-Wei Wang An Chen Zi-Xiang Xu Jing Yang Bin Liang Jian-Chun Cheng 

机构地区：[1]Key Laboratory of Modern Acoustics,MOE,Institute of Acoustics, Department of Physics,Nanjing University,Nanjing 210093,China [2]Collaborative Innovation Center of Advanced Microstructures,Nanjing University.Nanjing 210093,China

出　　处：《Science China(Physics,Mechanics & Astronomy)》2023年第2期87-94,共8页中国科学：物理学、力学、天文学（英文版）

基　　金：supported by the National Key R&D Program of China(Grant No. 2017YFA0303700);the National Natural Science Foundation of China (Grant Nos. 12174190, 11634006, 12074286, and 81127901);the Innovation Special Zone of National Defense Science and Technology,High-Performance Computing Center of Collaborative Innovation Center of Advanced Microstructures;a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions。

摘　　要：On-demand inverse design of acoustic metamaterials(AMs),which aims to retrieve the optimal structure according to given requirements,is still a challenging task owing to the non-unique relationship between physical structures and spectral responses.Here,we propose a probabilistic generation network(PGN) model to unveil this implicit relationship and implement this concept with an acoustic magic-cube absorber.By employing the auto-encoder-like configuration composed of a gate recurrent unit(GRU) and a deep neural network,our PGN model encodes the required spectral response into a latent space.The memory or feedback loop contained in the proposed GRU allows it to effectively recognize sequence characteristics of a spectrum.The method of modeling the inverse problem and retrieving multiple meta structures in a probabilistic generative manner skillfully solves the one-to-many mapping issue that is intractable in deterministic models.Moreover,to meet different sound absorption requirements,we tailored several representative spectra with low-frequency sound absorption characteristics,generating highprecision(MAE<0.06) predicted spectra with multiple meta structures.To further verify the effective prediction of the proposed PGN strategy,the experiment was carried out in a tailored broadband example,whose results coincide with both theoretical and numerical ones.Compared with other 5 networks,the PGN model exhibits higher accuracy and efficiency.Our work offers flexible and diversified solutions for multivalued inverse problems,opening up avenues to realize the on-demand de sign of AMs.

关 键 词：INVERSE PROBABILISTIC NETWORK 

分 类 号：O42[理学—声学]