Dynamic mode decomposition: an alternative algorithm for full-rank datasets
Applicationes Mathematicae, Tome 50 (2023) no. 1, pp. 55-65
Voir la notice de l'article provenant de la source Institute of Mathematics Polish Academy of Sciences
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.
Keywords:
dynamic mode decomposition dmd modal decomposition technique describes high dimensional dynamic using coupled spatial temporal modes combines main features performing principal component analysis pca space power spectral analysis time method equation free sense does require knowledge underlying governing equations entirely data driven purpose paper introduce algorithm computing dynamic mode decomposition full rank approach economical computational point view which advantage working large datasets
Affiliations des auteurs :
G. H. Nedzhibov 1
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author = {G. H. Nedzhibov},
title = {Dynamic mode decomposition: an alternative algorithm for full-rank datasets},
journal = {Applicationes Mathematicae},
pages = {55--65},
publisher = {mathdoc},
volume = {50},
number = {1},
year = {2023},
doi = {10.4064/am2465-4-2023},
language = {en},
url = {http://geodesic.mathdoc.fr/articles/10.4064/am2465-4-2023/}
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G. H. Nedzhibov. Dynamic mode decomposition: an alternative algorithm for full-rank datasets. Applicationes Mathematicae, Tome 50 (2023) no. 1, pp. 55-65. doi: 10.4064/am2465-4-2023
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