Two-stage framework for efficient gaussian process modeling of antenna input characteristics

Abstract

A two-stage approach based on Gaussian process regression that achieves significantly reduced requirements for computationally expensive high-fidelity training data is presented for the modeling of planar antenna input characteristics. Our method involves variable-fidelity electromagnetic simulations. In the first stage, a mapping between electromagnetic models (simulations) of low and high fidelity is learned, which allows us to substantially reduce (by 80% or more) the computational effort necessary to set up the high-fidelity training data sets for the actual surrogate models (second stage), with negligible loss in predictive power. We illustrate our method by modeling the input characteristics of three antenna structures with up to seven design variables. The accuracy of the two-stage method is confirmed by the successful use of the surrogates within a space-mapping-based optimization/design framework.