Cost‐efficient performance‐driven modelling of multi‐band antennas by variable‐fidelity electromagnetic simulations and customized space mapping

Abstract

AbstractElectromagnetic (EM) simulations have become an indispensable tool in the design of contemporary antennas. EM‐driven tasks, for example, parametric optimization, entail considerable computational efforts, which may be reduced by employing surrogate models. Yet, data‐driven modelling of antenna characteristics is largely hindered by the curse of dimensionality. This may be addressed using the recently reported domain‐confinement techniques, especially the nested‐kriging framework, which permits rendering of reliable surrogates over wide ranges of antenna parameters while greatly reducing the computational overhead of training data acquisition. Focused on modelling of multi‐band antennas, this paper attempts to reduce the cost of surrogate construction even further by incorporating variable‐fidelity simulations into the nested kriging. The principal challenge being design‐dependent frequency shifts between the models of various fidelities is handled through the development of a customized frequency scaling and output space mapping. Validation is carried out using a dual‐band dipole antenna modeled over broad ranges of operating conditions. A small training data set is sufficient to secure the predictive power comparable to that of the nested kriging model set up using solely high‐fidelity data, and by far exceeding the accuracy of conventional surrogates. Application examples for antenna optimization and experimental verification of the selected designs are also provided.