Reduced-cost microwave filter modeling using a two-stage Gaussian process regression approach

Abstract

A technique for the reduced-cost modeling of microwave filters is presented. Our approach exploits variable-fidelity electromagnetic (EM) simulations, and Gaussian process regression (GPR) carried out in two stages. In the first stage of the modeling process, a mapping between EM simulation filter models of low and high fidelity is established. The mapping is subsequently used in the second stage, making it possible for the final surrogate model to be constructed from training data obtained using only a fraction of the number of highfidelity simulations normally required. As demonstrated using three examples of microstrip filters, the proposed technique allows us to reduce substantially (by up to 80%) the central processing unit (CPU) cost of the filter model setup, as compared to conventional (single-stage) GPR—the benchmark modeling method in this study. This is achieved without degrading the model generalization capability. The reliability of the two-stage modeling method is demonstrated through the successful application of the surrogates to surrogate-based filter design optimization.