Efficient Frequency Selective Surface Analysis via End-to-End Model-Based Learning

TL;DR

This paper presents an end-to-end deep learning method that integrates physical models with neural networks to efficiently analyze frequency selective surfaces, reducing complexity and improving accuracy over traditional data-driven approaches.

Contribution

It introduces a novel end-to-end model-based learning framework that combines physical circuit models with deep learning for electromagnetic analysis of FSS, enhancing efficiency and accuracy.

Findings

Outperforms direct deep learning models in efficiency and accuracy.

Reduces model complexity compared to traditional data-driven methods.

Improves phase prediction accuracy with a modified loss function.

Abstract

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.