Deterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak

TL;DR

This paper introduces machine learning models, including deterministic and probabilistic neural networks, to design broadband acoustic cloaks that effectively hide objects from sound waves, advancing the field of wave manipulation and cloaking technology.

Contribution

It develops novel deep learning models for inverse design of acoustic cloaks, enabling efficient and broad spectral range cloaking with improved generalization and sensitivity analysis.

Findings

Probabilistic model improves design generalization.

Deep learning enables broadband acoustic cloaking.

Cloak design suppresses sound scattering across wide spectrum.

Abstract

Concealing an object from incoming waves (light and/or sound) remained science fiction for a long time due to the absence of wave-shielding materials in nature. Yet, the invention of artificial materials and new physical principles for optical and sound wave manipulation translated this abstract concept into reality by making an object acoustically invisible. Here, we present the notion of a machine learning-driven acoustic cloak and demonstrate an example of such a cloak with a multilayered core-shell configuration. Importantly, we develop deterministic and probabilistic deep learning models based on autoencoder-like neural network structure to retrieve the structural and material properties of the cloaking shell surrounding the object that suppresses scattering of sound in a broad spectral range, as if it was not there. The probabilistic model enhances the generalization ability of design procedure and uncovers the sensitivity of the cloak parameters on the spectral response for practical implementation. This proposal opens up new avenues to expedite the design of intelligent cloaking devices for tailored spectral response and offers a feasible solution for inverse scattering problems.