Deep learning enables accurate sound redistribution via nonlocal metasurfaces

TL;DR

This paper demonstrates that deep learning can effectively model and predict the complex nonlocal interactions in acoustic metasurfaces, enabling precise sound redistribution with high accuracy and experimental validation.

Contribution

It introduces a deep learning approach to efficiently learn nonlocal coupling effects in acoustic metasurfaces, enhancing control over sound manipulation beyond local strategies.

Findings

Deep learning predicts multi-channel sound reflection ratios with less than 1% error.

Experimental results confirm accurate sound redistribution into three channels with various energy ratios.

The method enables complex wave control, advancing acoustic device design.

Abstract

Conventional acoustic metasurfaces are constructed with gradiently ``local'' phase shift profiles provided by subunits. The local strategy implies the ignorance of the mutual coupling between subunits, which limits the efficiency of targeted sound manipulation, especially in complex environments. By taking into account the ``nonlocal'' interaction among subunits, nonlocal metasurface offers an opportunity for accurate control of sound propagation, but the requirement of the consideration of gathering coupling among all subunits, not just the nearest-neighbor coupling, greatly increases the complexity of the system and therefore hinders the explorations of functionalities of nonlocal metasurfaces. In this work, empowered by deep learning algorithms, the complex gathering coupling can be learned efficiently from the preset dataset so that the functionalities of nonlocal metasurfaces can be significantly uncovered. As an example, we demonstrate that nonlocal metasurfaces, which can redirect an incident wave into multi-channel reflections with arbitrary energy ratios, can be accurately predicted by deep learning algorithms. Compared to the theory, the relative error of the energy ratios is less than 1\%. Furthermore, experiments witness three-channel reflection with three types of energy ratios of (1, 0, 0), (1/2, 0, 1/2), and (1/3, 1/3, 1/3), proving the validity of the deep learning enabled nonlocal metasurfaces. Our work might blaze a new trail in the design of acoustic functional devices, especially for the cases containing complex wave-matter interactions.