Becoming invisible at will has fascinated humanity for centuries and in the past decade it has attracted a great deal of attention owing to the advent of metamaterials. However, state-of-the-art invisibility cloaks typically work in a deterministic system or in conjunction with outside help to achieve active cloaking. Here, we propose the concept of an intelligent (that is, self-adaptive) cloak driven by deep learning and present a metasurface cloak as an example implementation. In the experiment, the metasurface cloak exhibits a millisecond response time to an ever-changing incident wave and the surrounding environment, without any human intervention. Our work brings the available cloaking strategies closer to a wide range of real-time, in situ applications, such as moving stealth vehicles. The approach opens the way to facilitating other intelligent metadevices in the microwave regime and across the wider electromagnetic spectrum and, more generally, enables automatic solutions of electromagnetic inverse design problems.

随意成为隐形人已经使人类着迷了几个世纪，并且在过去的十年中，由于超材料的出现，它引起了极大的关注。但是，最先进的隐形斗篷通常在确定性系统中工作，或者与外部帮助配合使用以实现主动的隐形斗篷。在这里，我们提出了由深度学习驱动的智能（即自适应）隐身衣的概念，并提出了超表面隐身衣作为示例实现。在实验中，超表面隐身衣对瞬息万变的入射波和周围环境表现出毫秒响应时间，而无需任何人工干预。我们的工作使可用的隐身策略更接近各种实时，原地应用程序，例如移动隐形车。