Application of the reduced basis method to ID quasi-periodic array modeling

Abstract

The reduced basis method is a model-order reduction technique that converts the original system equations into smaller ones using a reduced basis set. This basis set is usually derived from a set of solutions of the system equation with different values of control parameters. If the system model changes affinely with the parameter values, a reduced-order model can be derived. In this abstract, we apply the reduced basis method to method of moments for quasi-periodic array modeling. With this method, a new basis set for a single element is constructed through an offline process. Because of the similarities among elements, the number of basis functions for each element can be much less than directing modeling from geometrical mesh. Numerical example shows that both the computational and memory efficiency is improved compared with direct modeling using method of moments.