A Surrogate Model for the Forward Design of Multi-layered Metasurface-based Radar Absorbing Structures

TL;DR

This paper introduces a CNN-based surrogate model that rapidly predicts electromagnetic responses of multi-layered metasurface radar absorbing structures, reducing computational costs while maintaining high accuracy, thus facilitating efficient design and optimization.

Contribution

The paper presents a novel CNN-based surrogate model for EM response prediction of metasurface RAS, significantly speeding up the design process compared to traditional full wave simulations.

Findings

Achieved 99.9% cosine similarity in predictions

Reduced computational time significantly

Maintained high accuracy in EM response estimation

Abstract

Metasurface-based radar absorbing structures (RAS) are highly preferred for applications like stealth technology, electromagnetic (EM) shielding, etc. due to their capability to achieve frequency selective absorption characteristics with minimal thickness and reduced weight penalty. However, the conventional approach for the EM design and optimization of these structures relies on forward simulations, using full wave simulation tools, to predict the electromagnetic (EM) response of candidate meta atoms. This process is computationally intensive, extremely time consuming and requires exploration of large design spaces. To overcome this challenge, we propose a surrogate model that significantly accelerates the prediction of EM responses of multi-layered metasurface-based RAS. A convolutional neural network (CNN) based architecture with Huber loss function has been employed to estimate the reflection characteristics of the RAS model. The proposed model achieved a cosine similarity of 99.9% and a mean square error of 0.001 within 1000 epochs of training. The efficiency of the model has been established via full wave simulations as well as experiment where it demonstrated significant reduction in computational time while maintaining high predictive accuracy.