Broadband Acoustic Metamaterial 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. In this research, we develop a new machine learning framework for predicting optimal metastructures such as planar configurations of scatterers with specific functionalities. Specifically, we implement this framework by combining probabilistic generative modeling with deep learning and propose two models: a conditional variational autoencoder (CVAE) and a supervised variational autoencoder (SVAE) model. As an application of the method, here we design an acoustic cloak considering a minimization of total scattering cross-section (TSCS) for a set of cylindrical obstacles. We work with the sets of cylindrical objects confined in a region of space and streamline the design of configurations with minimal TSCS, demonstrating broadband cloaking effect at discrete sets of wavenumbers. After establishing the artificial neural networks that are capable of learning the TSCS based on the location of cylinders, we discuss our inverse design algorithms, 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.