ORCIBS Seminar - Mustafa Baydoğan

The use of surrogate models (SMs) has become popular in electromagnetic (EM) design and optimization. Traditional SMs, while beneficial, are often hindered by the inherent complexity and nonlinearity of EM systems, leading to challenges in data representation and design space exploration. Addressing these challenges, we introduce a novel tree-based learning strategy for sampling within high-dimensional EM design spaces. Our method emphasizes the localized exploration to accurately capture the unique output behaviors at various frequencies in multi-frequency EM simulations. The proposed method focuses on refining the sampling process instead of optimizing an objective function...