  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.
