The Challenge: Electromagnetic systems exhibit complex frequency-dependent behavior governed by resonances. Characterizing these systems requires solving parameterdependent finite element models with millions of degrees of freedom across 10–20 input parameters—a computationally intractable problem for design exploration and optimization.

Modern surrogate modeling techniques (data-driven approximations trained on highdimensional datasets) offer a solution, but their success depends critically on the quality and structure of training data.