Randomized Dynamic Mode Decomposition for Non-Intrusive Reduced Order Modelling

2016/11/15 by Bistrian, D. A., Navon, I. M. · 1 citation
#65P99 #76B75 #FOS: Mathematics #Numerical Analysis (math.NA)

paper · doi:10.48550/arxiv.1611.04884

Abstract

This paper focuses on a new framework for reduced order modelling of non-intrusive data with application to 2D flows. To overcome the shortcomings of intrusive model order reduction usually derived by combining the POD and the Galerkin projection methods, we developed a novel technique based on Randomized Dynamic Mode Decomposition as a fast and accurate option in model order reduction of non-intrusive data originating from Saint-Venant systems. Combining efficiently the Randomized Dynamic Mode Decomposition algorithm with Radial Basis Function interpolation, we produced an efficient tool in developing the linear model of a complex flow field described by non-intrusive (or experimental) data. The rank of the reduced DMD model is given as the unique solution of a constrained optimization problem. We emphasize the excellent behavior of the non-intrusive reduced order models by performing a qualitative analysis. In addition, we gain a significantly reduction of CPU time in computation of the reduced order models (ROMs) for non-intrusive numerical data.

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