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 

作者机构：Key Laboratory of Modern AcousticsMOEInstitute of Acoustics Department of PhysicsNanjing UniversityNanjing 210093China Collaborative Innovation Center of Advanced MicrostructuresNanjing University.Nanjing 210093China 

出 版 物：《Science China(Physics,Mechanics & Astronomy)》 (中国科学：物理学、力学、天文学（英文版）)

年 卷 期：2023年第66卷第2期

页      面：87-94页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 070206[理学-声学] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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。 

主　　题：inverse probabilistic network 

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(MAE0.06) 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.