Radar Cross-Section Reduction Metamaterial Design Using Machine Learning Methods

Abstract

The synthesis of metamaterials presents a high-dimensional inverse-design problem characterized by stringent demands on accuracy, flexibility, and computational efficiency. Conventional analytical and numerical methods often incur prohibitive computational intensity, susceptibility to local minima, and lack the versatility needed for complex metamaterial geometries. Early attempts to leverage machine learning for metamaterial design have likewise fallen short, relying on elaborate sub-network architectures, external simulations, or manual intervention, and oversimplified assumptions about design universality. Motivated by the growing importance of radar-cross-section (RCS)–reducing metamaterials in civilian and commercial settings, this study introduces an ML-driven inverse design framework addressing these gaps. Key contributions begin with a systematic review consolidating design methodologies and emerging applications. Leveraging these insights, a novel strategy is developed to construct an extensive database of optimal metamaterial layouts and design specifications. To mitigate data imbalance and enhance generalizability, a data augmentation framework extends circuit model inversion principles from square-loop to absorbing metamaterials. A reverse convolutional neural network (CNN) is subsequently developed for inverse design, augmented by a deep generative model employing encoder-decoder networks to compress image representations and improve generalizability. To address inherent deep learning limitations, feature-based and similarity-guided algorithms were implemented. Further, a reinforcement learning (RL) framework was devised to refine ML outputs. This evolves into a collaborative multi-agent RL (MARL) architecture, decomposing optimization into subtasks managed by specialized agents, each targeting parameter subsets. Adaptive heuristics are embedded into reward functions to prioritize scalability and adaptability. Validated through RCS-reduction case studies, the proposed framework demonstrates enhanced precision, computational efficiency, and flexibility over existing methods. This work advances ML-driven metamaterial synthesis by bridging gaps in data representation, generalizability, and collaborative optimization, offering a scalable paradigm for complex electromagnetic applications.

Date of Award	2025
Original language	American English
Supervisor	Ibrahim Elfadel (Supervisor)