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Concealing an object from incoming waves (light and/or sound) remainedscience fiction for a long time due to the absence of wave-shielding materialsin nature. Yet, the invention of artificial materials and new physicalprinciples for optical and sound wave manipulation translated this abstractconcept into reality by making an object acoustically invisible. Here, wepresent the notion of a machine learning-driven acoustic cloak and demonstratean example of such a cloak with a multilayered core-shell configuration.Importantly, we develop deterministic and probabilistic deep learning modelsbased on autoencoder-like neural network structure to retrieve the structuraland material properties of the cloaking shell surrounding the object thatsuppresses scattering of sound in a broad spectral range, as if it was notthere. The probabilistic model enhances the generalization ability of designprocedure and uncovers the sensitivity of the cloak parameters on the spectralresponse for practical implementation. This proposal opens up new avenues toexpedite the design of intelligent cloaking devices for tailored spectralresponse and offers a feasible solution for inverse scattering problems.

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