Single-model versus ensemble-model strategies for efficient Gaussian process surrogate modeling of antenna input characteristics

摘要

Gaussian process regression has been shown to be a highly effective tool for modeling the input characteristics of antennas. This study presents, for the first time, a rigorous comparison of two strategies for modeling Re{S11}, Im{S11}, and |S11|: the standard single-model method, and an approach that employs an ensemble of independent single models, one per equally-spaced frequency value in the range of interest. In spite of the fact that it uses far less training data, the singlemodel technique for the most approximately matched or even outdid the ensemble of GPR models in predictive performance — this appears to be due to the fact that the ensemble model disregards important covariance information regarding the latent function associated with the frequency dimension.