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 optically and 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. We develop deterministic and probabilistic deep learning models based on autoencoderlike 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's parameters on the spectral response for practical implementation. This proposal opens up avenues to expedite the design of intelligent cloaking devices for tailored spectral response and offers a feasible solution for inverse scattering problems.
- Received 23 November 2020
- Revised 19 January 2021
- Accepted 22 January 2021
DOI:https://doi.org/10.1103/PhysRevResearch.3.013142
Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
Published by the American Physical Society