Acoustic Cloak Design via Machine Learning

Abstract

Acoustic metamaterials are engineered microstructures with special mechanical and acoustic properties enabling exotic effects such as wave steering, focusing and cloaking. The design of acoustic cloaks using scattering cancellation has traditionally involved the optimization of metamaterial structure based on direct computer simulations of the total scattering cross section (TSCS) for a large number of configurations. Here, we work with sets of cylindrical objects confined in a region of space and use machine learning methods to streamline the design of 2D configurations of scatterers with minimal TSCS demonstrating cloaking effect at discrete sets of wavenumbers. After establishing that artificial neural networks are capable of learning the TSCS based on the location of cylinders, we develop an inverse design algorithm, combining variational autoencoders and the Gaussian process, for predicting optimal arrangements of scatterers given the TSCS. We show results for up to eight cylinders and discuss the efficiency and other advantages of the machine learning approach.