Efficient Gaussian process modelling and optimization of slot antennas using a multi-fidelity approach for training data reduction

A methodology for reducing the computational cost of setting up Gaussian process (GP) models of coplanar waveguide (CPW)-fed slot antennas is presented. Our approach exploits finite-element frequency-domain simulations of different mesh densities with the coarse-mesh simulations used to find a reduced number of fine-mesh-simulated training points, eventually utilized to construct the GP surrogate model of the antenna. The surrogate is successfully applied to optimize the geometry parameters of the antenna within the adaptively-adjusted design specifications framework, even when the training data was reduced by as much as 70%. In the latter case, the computational cost of setting up the surrogate was only 40% of that of setting up a surrogate model using the full training data, while the total optimization time could be reduced by a third.