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arXiv · 1611.04884

Randomized Dynamic Mode Decomposition for Non-Intrusive Reduced Order Modelling

D. A. Bistrian·I. M. Navon
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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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D. A. Bistrian, I. M. Navon. 2016-11-15. Randomized Dynamic Mode Decomposition for Non-Intrusive Reduced Order Modelling. https://arxiv.org/abs/1611.04884

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