Scalable macromodelling of microwave system responses using sequential sampling with path-simplexes

A scattered sequential sampling algorithm for the automatic construction of stable and passive scalable macromodels of parameterised system responses with a well-conditioned refinement strategy using path-simplexes is proposed. The method is tailored towards the local scalable macromodelling schemes on scattered grids. A pertinent numerical example validates the proposed approach

Auto-generation of passive scalable macromodels for microwave components using scattered sequential sampling

This paper presents a method for automatic construction of stable and passive scalable macromodels for parameterized frequency responses. The method requires very little prior knowledge to build the scalable macromodels thereby considerably reducing the burden on the designers. The proposed method uses an efficient scattered sequential sampling strategy with as few expensive simulations as possible to generate accurate macromodels for the system using state-of-the-art scalable macromodeling methods. The scalable macromodels can be used as a replacement model for the actual simulator in overall design processes. Pertinent numerical results validate the proposed sequential sampling strategy

Sensitivity analysis using data-driven parametric macromodels

An accurate parametric macromodeling method which builds the parameterized frequency behavior of systems from frequency data samples is presented. The method aims to calculate parametric sensitivity responses of the model with respect to design parameters over the entire design space. A judiciously chosen interpolation scheme is used to parameterize state-space matrices such that parametric sensitivities can be computed analytically. The modeling capability of the proposed method is validated by a pertinent numerical example

Optimization of high-speed electromagnetic systems with accurate parametric macromodels generated using sequential sampling of the design space

This paper presents a design optimization approach for electromagnetic systems using parametric macromodels. The parametric macromodels are generated using an efficient sequential sampling of the design space of interest which ensures optimal sample selection for a required level of accuracy. The proposed method is validated on a microwave notch filter example for which the parametric macromodel is used in a minimax optimization algorithm so that the design parameters are optimized for some specific electrical design performances

Parametric macromodeling for sensitivity responses from tabulated data

This letter presents a parametric macromodeling technique which accurately describes the parameterized frequency behavior of electromagnetic systems and their corresponding parameterized sensitivity responses with respect to design parameters. The technique is based on the interpolation of a set of state-space matrices with a proper choice of the interpolation scheme, so that parametric sensitivity macromodels can be computed. Pertinent numerical results validate the proposed parametric macromodeling approach