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 high-precision (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 design of AMs.