Intelligent electromagnetic mapping via physics driven and neural networks on frequency selective surfaces

Abstract The high mapping efficiency between various structures and electromagnetic (EM) properties of frequency selective surfaces (FSSs) is the state-of-the-art in the EM community. The most straightforward approaches for beam analysis depend on measurements and conventional EM calculation methods, which are inefficient and time-consuming. Equivalent circuit models (ECMs) with excellent intuitiveness and simplicity have been put forward extensively. Despite several applications, bottlenecks in ECM still exist, i.e. the application scope is restricted to narrow bands and specific structures, which is triggered by the ignorance of EM nonlinear coupling. In this study, for the first time, a lightweight physical model based on neural network (ECM-NN) is proposed , which exhibits great physical interpretability and spatial generalization abilities. The nonlinear mapping relationship between structure and beam behavior is interpreted by corresponding simulations. Specifically, two deep parametric factors obtained by multi-layer perceptron networks are introduced to serve as the core of lightweight strategies and compensate for the absence of nonlinearity. Experimental results of single square loop (SL) and double SL indicate that compared with related works, better agreements of the frequency responses and resonant frequencies are achieved with ECM-NN in broadband (0–30 GHz) as well as oblique incident angles (0°–60°). The average accuracy of the mapping is higher than 98.6%. The findings of this study provide a novel strategy for further studies of complex FSSs.