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What is Machine Learning for Microwave Optimization about?

This paper presents a novel machine-learning framework for optimizing microwave passive components using variable-fidelity electromagnetic (EM) simulations and response feature technology. The proposed method significantly reduces computational costs by primarily utilizing low-fidelity simulations and employing a co-kriging surrogate model, achieving competitive design quality with only sixty high-fidelity EM analyses. The approach addresses the challenges of complex geometries and nonlinear system characte

Author

Ibrahim Ahmed

Language

EN