Comparison of Surrogate Modeling Approaches for Estimation of EMI Filter Insertion Loss

The increasing application of silicon carbide and gallium nitride transistors decreases the size of power electronic systems because of faster switching and clock times. However, both increase the electromagnetic emission, causing additional cost for the design of EMI filters. Finding a joint optimum requires long computation time. Meta modeling techniques reduce the computation time by approximating physical models with a simple mathematical equivalent function, generated from training data. If the physical models are computationally expensive, it is important to identify particularly efficient methods. We compare the surrogate modeling techniques of Kriging, Polynomial Chaos Expansion, Polynomial Chaos Kriging and Wideband Kriging. We find that Wideband Kriging is most accurate, but also has the most excessive model generation time.