Autonomous aeroamphibious invisibility cloak with stochastic-evolution learning

Being invisible ad libitum has long captivated the popular imagination,particularly in terms of safeguarding modern high-end instruments from potential threats.Decades ago,the advent of metamaterials and transformation optics sparked considerable interest in invisibility cloaks,which have been mainly demonstrated in ground and waveguide modalities.However,an omnidirectional flying cloak has not been achieved,primarily due to the challenges associated with dynamic synthesis of metasurface dispersion.We demonstrate an autonomous aeroamphibious invisibility cloak that incorporates a suite of perception,decision,and execution modules,capable of maintaining invisibility amidst kaleidoscopic backgrounds and neutralizing external stimuli.The physical breakthrough lies in the spatiotemporal modulation imparted on tunable metasurfaces to sculpt the scattering field in both space and frequency domains.To intelligently control the spatiotemporal metasurfaces,we introduce a stochastic-evolution learning that automatically aligns with the optimal solution through maximum probabilistic inference.In a fully self-driving experiment,we implement this concept on an unmanned drone and showcase adaptive invisibility in three canonical landscapes-sea,land,and air-with a similarity rate of up to 95%.Our work extends the family of invisibility cloaks to flying modality and inspires other research on material discoveries and homeostatic meta-devices.

Realizing transmitted metasurface cloak by a tandem neural network

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.

A knowledge-inherited learning for intelligent metasurface design and assembly

Recent breakthroughs in deep learning have ushered in an essential tool for optics and photonics,recurring in various applications of material design,system optimization,and automation control.Deep learning-enabled on-demand metasurface design has been the subject of extensive expansion,as it can alleviate the time-consuming,low-efficiency,and experience-orientated shortcomings in conventional numerical simulations and physics-based methods.However,collecting samples and training neural networks are fundamentally confined to predefined individual metamaterials and tend to fail for large problem sizes.Inspired by object-oriented C++programming,we propose a knowledge-inherited paradigm for multi-object and shape-unbound metasurface inverse design.Each inherited neural network carries knowledge from the"parent"metasurface and then is freely assembled to construct the"offspring"metasurface;such a process is as simple as building a container-type house.We benchmark the paradigm by the free design of aperiodic and periodic metasurfaces,with accuracies that reach 86.7%.Furthermore,we present an intelligent origami metasurface to facilitate compatible and lightweight satellite communication facilities.Our work opens up a new avenue for automatic metasurface design and leverages the assemblability to broaden the adaptability of intelligent metadevices.