Efficient Frequency Selective Surface Analysis via End-to-End Model-Based Learning
This work addresses efficient electromagnetic analysis for researchers and engineers in fields like antenna design, but it is incremental as it builds on existing model-based learning approaches.
The paper tackled the problem of electromagnetic analysis for frequency selective surfaces by introducing an end-to-end model-based deep learning approach, resulting in improved computational efficiency, model size, and generalization compared to traditional methods.
This paper introduces an innovative end-to-end model-based deep learning approach for efficient electromagnetic analysis of high-dimensional frequency selective surfaces (FSS). Unlike traditional data-driven methods that require large datasets, this approach combines physical insights from equivalent circuit models with deep learning techniques to significantly reduce model complexity and enhance prediction accuracy. Compared to previously introduced model-based learning approaches, the proposed method is trained end-to-end from the physical structure of the FSS (geometric parameters) to its electromagnetic response (S-parameters). Additionally, an improvement in phase prediction accuracy through a modified loss function is presented. Comparisons with direct models, including deep neural networks (DNN) and radial basis function networks (RBFN), demonstrate the superiority of the model-based approach in terms of computational efficiency, model size, and generalization capability.