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

Electromagnetic (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.