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

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.

25 pages, 14 figures

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Cited by in corpus (4)

• Tailoring Structure-borne Sound Through Bandgap Engineering in Phononic Crystals and Metamaterials: A Comprehensive Review
• Acoustic Cloak Design via Machine Learning
• Design of arbitrarily shaped acoustic cloaks through PDE-constrained optimization satisfying sonic-metamaterial design requirements
• Acoustic Structure Inverse Design and Optimization Using Deep Learning