A Machine Learning Approach for Design of Frequency Selective Surface based Radar Absorbing Material via Image Prediction

TL;DR

This paper introduces a machine learning-based method to design frequency selective surface radar absorbing materials by predicting FSS images from absorption coefficients, enabling faster design and optimization across a broad frequency range.

Contribution

It proposes a novel ML approach that predicts FSS unit cell images from absorption data, facilitating accelerated electromagnetic design processes.

Findings

Six ML models achieved over 90% training accuracy.

Predicted FSS images effectively evaluated with electromagnetic simulations.

Method accelerates FSS design for high-frequency applications.

Abstract

The paper presents an innovative methodology for designing frequency selective surface (FSS) based radar absorbing materials using machine learning (ML) technique. In conventional electromagnetic design, unit cell dimensions of FSS are used as input and absorption coefficient is then predicted for a given design. In this paper, absorption coefficient is considered as input to ML model and image of FSS unit cell is predicted. Later, this image is used for generating the FSS unit cell parameters. Eleven different ML models are studied over a wide frequency band of 1GHz to 30GHz. Out of which six ML models (i.e. (a) Random Forest classification, (b) K- Neighbors Classification, (c) Grid search regression, (d) Random Forest regression, (e) Decision tree classification, and (f) Decision tree regression) show training accuracy more than 90%. The absorption coefficients with varying frequencies of these predicted images are subsequently evaluated using commercial electromagnetic solver. The performance of these ML models is encouraging, and it can be used for accelerating design and optimization of high performance FSS based radar absorbing material for advanced electromagnetic applications in future.