Interpolation-Based Model Order Reduction for Polynomial Parametric Systems
For researchers in model order reduction, this work extends interpolation-based reduction to polynomial parametric systems, but the approach is incremental as it adapts existing Loewner framework.
This work proposes a model order reduction scheme for polynomial parametric systems by interpolating generalized multivariate transfer functions at selected points, and introduces an automatic method inspired by the Loewner approach to determine a reduced-order system. Numerical examples demonstrate the efficiency of the proposed methods.
In this work, we investigate a model order reduction scheme for polynomial parametric systems. We begin with defining the generalized multivariate transfer functions for the system. Based on this, we aim at constructing a reduced-order system, interpolating the defined generalized transfer functions at a given set of interpolation points. Furthermore, we provide a method, inspired by the Loewner approach for linear and (quadratic-)bilinear systems, to determine a good-quality reduced-order system in an automatic way. We also discuss the computational issues related to the proposed method and a potential application of CUR matrix approximation in order to further speed-up simulations of reduced-order systems. We test the efficiency of the proposed methods via several numerical examples.