Being invisible at will has been a long-standing dream for centuries, epitomized by numerous legends; humans have never stopped their exploration steps to realize this dream. Recent years have witnessed a breakthrough in this search due to the advent of transformation optics, metamaterials, and metasurfaces. However, the previous metasurface cloaks typically work in a reflection manner that relies on a high-reflection background, thus limiting the applications. Here, we propose an easy yet viable approach to realize the transmitted metasurface cloak, just composed of two planar metasurfaces to hide an object inside, such as a cat. To tackle the hard-to-converge issue caused by the nonuniqueness phenomenon, we deploy a tandem neural network (T-NN) to efficiently streamline the inverse design. Once pretrained, the T-NN can work for a customer-desired electromagnetic response in one single forward computation, saving a great amount of time. Our work opens a new avenue to realize a transparent invisibility cloak, and the tandem-NN can also inspire the inverse design of other metamaterials and photonics.

Optical illusion has always attracted extensive attention, as it provides a superior self‐protection ability for both natural animals and human beings. A decade ago, this motivated the study and application of transformation optics, which provides a universal tool to manipulate light for invisibility cloaking and optical illusion. However, mainstream transformation‐optics‐based optical illusions are inherently hindered by the extreme requirements of metamaterial compositions in practice and unfavorably limited by the very large computational cost caused by their bulky state. To overcome these grand challenges, a novel and intelligent optical illusion supported by form‐free metasurfaces via a deep learning architecture is reported, which can not only render a similar illusion effect but also greatly reduces the parameter space in physics. Illustrative examples of conformal metasurfaces are presented, with a high‐fidelity inverse design from either the near‐ or far‐field in the simulation and experiment. Furthermore, a full set of intelligent systems is developed to benchmark the real‐world optical illusion applicability. The work brings the available illusion strategies closer to a wide range of in situ practical‐oriented applications and lays a foundation for the next generation of intelligent metamaterials.

The physical basis of a smart city, the wireless channel, plays an important role in coordinating functions across a variety of systems and disordered environments, with numerous applications in wireless communication. However, conventional wireless channel typically necessitates high-complexity and energy-consuming hardware, and it is hindered by lengthy and iterative optimization strategies. Here, we introduce the concept of homeostatic neuro-metasurfaces to automatically and monolithically manage wireless channel in dynamics. These neuro-metasurfaces relieve the heavy reliance on traditional radio frequency components and embrace two iconic traits: They require no iterative computation and no human participation. In doing so, we develop a flexible deep learning paradigm for the global inverse design of large-scale metasurfaces, reaching an accuracy greater than 90%. In a full perception-decision-action experiment, our concept is demonstrated through a preliminary proof-of-concept verification and an on-demand wireless channel management. Our work provides a key advance for the next generation of electromagnetic smart cities.