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

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.