Reliable Surrogate Modeling of Antenna Input Characteristics by Means of Domain Confinement and Principal Components

Abstract

A reliable design of contemporary antenna structures necessarily involves full-wave electromagnetic (EM) analysis which is the only tool capable of accounting, for example, for element coupling or the effects of connectors. As EM simulations tend to be CPU-intensive, surrogate modeling allows for relieving the computational overhead of design tasks that require numerous analyses, for example, parametric optimization or uncertainty quantification. Notwithstanding, conventional data-driven surrogates are not suitable for handling highly nonlinear antenna characteristics over multidimensional parameter spaces. This paper proposes a novel modeling approach that employs a recently introduced concept of domain confinement, as well as principal component analysis. In our approach, the modeling process is restricted to the region containing high-quality designs with respect to the performance figures of antennas under design, identified using a set of pre-optimized reference designs. The model domain is spanned by the selected principal components of the reference design set, which reduces both its volume and dimensionality. As a result, a reliable surrogate can be constructed over wide ranges of both operating conditions and antenna parameters, using small training datasets. Our technique is demonstrated using two antenna examples and is favorably compared to both conventional and constrained modeling approaches. Application case studies (antenna optimization) are also discussed.