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.

中文翻译：

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确定性和概率深度学习模型用于宽带声学披风的逆设计

由于自然界中没有防波材料，将物体从入射波（光和/或声）中隐藏起来很长时间仍然是科幻小说。然而，人造材料的发明和用于光波和声波操纵的新物理原理将物体变成光学上和听觉上“不可见的”，从而将这一抽象概念变为现实。在这里，我们介绍了机器学习驱动的声学披风的概念，并展示了这种具有多层核壳结构的披风的示例。我们基于类似自编码器的神经网络结构开发了确定性和概率深度学习模型，以检索围绕物体的隐身外壳的结构和材料属性，该隐身外壳可在较宽的频谱范围内抑制声音的散射，就好像它不在那里一样。概率模型增强了设计程序的泛化能力，并揭示了披风参数对光谱响应的敏感性，可用于实际实施。该提议为加快针对特定光谱响应的智能隐身装置的设计开辟了道路，并为逆散射问题提供了可行的解决方案。