MWCAWE: A multivariate WCAWE approach for parametric model order reduction, and a sampling strategy for the bivariate case

Abstract

In this paper, the multivariate well-conditioned asymptotic waveform evaluation (MWCAWE) algorithm is presented. It is based on a univariate version of the algorithm, previously introduced for fast frequency sweeps. Taking advantage of the robustness of this univariate algorithm, the MWCAWE enables to effectively generate multi-parameter reduced-order models. Additionally, a residue-based contour following approach is introduced for a two-stage generation of a projection basis, here applied on bi-variate acoustic problems where frequency and a material parameter are in focus. The effectiveness of the proposed method is demonstrated on two poro-acoustic problems, highlighting both the good convergence property of the algorithm and the potential of the multi-patch strategy for parametric applications.