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Dynamic mode decomposition: an alternative algorithm for full-rank datasets

Volume 50 / 2023

G. H. Nedzhibov Applicationes Mathematicae 50 (2023), 55-65 MSC: Primary 65P99; Secondary 37M10. DOI: 10.4064/am2465-4-2023 Published online: 8 May 2023

Abstract

Dynamic mode decomposition (DMD) is a modal decomposition technique that describes high-dimensional dynamic data using coupled spatial-temporal modes. It combines the main features of performing principal component analysis (PCA) in space, and power spectral analysis in time. The method is equation-free in the sense that it does not require knowledge of the underlying governing equations and is entirely data-driven. The purpose of this paper is to introduce a new algorithm for computing the dynamic mode decomposition in the case of full rank data. The new approach is more economical from a computational point of view, which is an advantage when working with large datasets.

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