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On-the-fly algorithm for Dynamic Mode Decomposition using Incremental Singular Value Decomposition and Total Least Squares
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Dynamic Mode Decomposition (DMD) is a useful tool to effectively extract the dominant dynamic flow structure from a unsteady flow field. However, DMD requires massive computational resources with respect to memory consumption and the usage of storage. In this paper, an alternative incremental algorithm of Total DMD (Incremental TDMD) is proposed which is based on Incremental Singular Value Decomposition (SVD). The advantage of Incremental TDMD compared to the existing on-the-fly algorithms of DMD is that Sparsity-Promoting DMD (SPDMD) can be performed after the incremental process without saving huge datasets on the disk space. SPDMD combined with Incremental TDMD enable the effective identification of dominant modes which are relevant to the results from conventional TDMD combined with SPDMD.
Forward citations
Cited by 2 Pith papers
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An Incremental Approach to Online Dynamic Mode Decomposition for Time-Varying Systems with Applications to EEG Data Modeling
Incremental SVD-based online DMD and DMD-with-control algorithms for time-varying systems, demonstrated on EEG error-related potentials.
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Efficient streaming dynamic mode decomposition
The authors propose esDMD, a single-basis streaming DMD variant, but the central theorem is false and the pseudocode has an apparent update error.
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