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.
