Statistical modeling and an adaptive averaging technique for strong convergence of the dynamic mode decomposition

DOI10.1016/j.cam.2022.114551OpenAlexW4283793439MaRDI QIDQ2088764

Kensuke Aishima

Publication date: 6 October 2022

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2022.114551

zbMATH Keywords

strong convergencesingular value decompositioneigenvalue problemsRayleigh-Ritz proceduredynamic mode decomposition

Mathematics Subject Classification ID

Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Preconditioners for iterative methods (65F08)

Cites Work

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• Implicitly-weighted total least squares
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• Optimal mode decomposition for unsteady flows
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• Dynamic mode decomposition of numerical and experimental data
• Spectral analysis of nonlinear flows
• Ergodic Theory, Dynamic Mode Decomposition, and Computation of Spectral Properties of the Koopman Operator
• Variable Projection Methods for an Optimized Dynamic Mode Decomposition
• Centering Data Improves the Dynamic Mode Decomposition
• Strong convergence for the dynamic mode decomposition based on the total least squares to noisy datasets
• Consistent Dynamic Mode Decomposition

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