Superscattering of Underwater Sound via Deep Learning Approach

Abstract

We design a multilayer cylindrical structure to realize superscattering of underwater sound. Because of the near degeneracy of resonances in multiple channels of the structure, the scattering contributions from these resonances can overlap to break the single-channel limit of subwavelength objects. However, tuning the design parameters to achieve the target response is an optimization process that is tedious and time-consuming. Here, we demonstrate that a well-trained tandem neural network can deal with this problem efficiently, which can not only forwardly predict the scattering spectra of the multilayer structure with high precision, but also inversely design the required structural parameters efficiently.