Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition

Abstract

This note proposes a simple and general framework of dynamic mode decomposition (DMD) and a mode selection for large datasets. The proposed framework explicitly introduces a preconditioning step using an incremental proper orthogonal decomposition to DMD and mode selection algorithms. By performing the preconditioning step, the DMD and the mode selection can be performed with low memory consumption and small computational complexity and can be applied to large datasets. In addition, a simple mode selection algorithm based on a greedy method is proposed. The proposed framework is applied to the analysis of a three-dimensional flows around a circular cylinder.

References in corpus (3)

• Characterizing and correcting for the effect of sensor noise in the dynamic mode decomposition
• Dynamic Mode Decomposition for Large and Streaming Datasets
• Modal Analysis of Fluid Flows: An Overview

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