  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.
