Multifrequency spherical cloak in microwave frequencies enabled by deep learning

Designing invisibility devices for required frequency bands is important in anti-detection methods in various fields such as communications, construction, and others. However, traditional design methods are time-consuming, with manual adjustment of parameters and continuous trial and error. Fortunately, the data-driven approach based on deep learning has revolutionized the field. In this article, we demonstrate that utilizing a trained deep neural network can handle the problem efficiently. It can accurately predict the scattering cross section (SCS) of a multilayer sphere under given structural parameters and reversely design the structural parameters corresponding to the target spectrum. Using the predicted parameters, three-dimensional full-wave simulations are conducted, achieving perfect invisibility performance under transverse electric (TE), transverse magnetic (TM) waves, and point source illumination at multiple frequencies. Our results provide a compelling case for utilizing deep learning in cloaking design.