This letter proposes a novel a-posteriori error estimator suitable for the reduced order modeling of microwave circuits. Unlike the existing error estimators based on impedance function residuals, the new one exploits the residual error associated with the computation of the scattering matrix. The estimator can be effectively used in the Reduced Basis Method (RBM) to automatically generate reduced-order models. The results of numerical...

To make decisions, cognitive radar must rely on predictions of its own performance. In the literature, these predictions are usually based on some form of Cram\'er-Rao lower bound. This approach is scientifically sound, but it also brings a possibility of the cognitive controller overestimating radar performance. It therefore makes sense to back theoretical predictions with careful experiments which will verify their applicability....

An automatic reduction-order selection algorithm for macromodels in finite-element analysis is presented. The algorithm is based on a goal-oriented a posteriori error estimator that operates on low-order reduced blocks of matrices, and hence, it can be evaluated extremely quickly.

This letter proposes to automate generation of reduced-order models used for accelerated -parameter computation by applying a posteriori model error estimators. So far,a posteriori error estimators were used in Reduced Basis Method (RBM) and Proper Orthogonal Decomposition (POD) to select frequency points at which basis vectors are generated. This letter shows how a posteriori error estimators can be applied to automatically select...

This paper attempts to develop a new automated multipoint model-order reduction (MOR) technique, based on matching moments of the system input–output function, which would be suited for fast and accurate computation of scattering parameters for electromagnetic (EM) systems over a wide frequency band. To this end, two questions are addressed. Firstly, the cost of the wideband reduced model generation is optimized by automating a...