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Optimal low-rank Dynamic Mode Decomposition [PDF]
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASPP), New Orleans, USA ...
Héas, Patrick, Herzet, Cédric
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Dynamic Mode Decomposition with Control Liouville Operators
IFAC-PapersOnLine, 2021 This paper builds the theoretical foundations for dynamic mode decomposition (DMD) of control-affine dynamical systems by leveraging the theory of vector-valued reproducing kernel Hilbert spaces (RKHSs). Specifically, control Liouville operators and control occupation kernels are introduced to separate the drift dynamics from the input dynamics.
Joel A. Rosenfeld +1 more
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Tensor Train-Based Higher-Order Dynamic Mode Decomposition for Dynamical Systems
Mathematics, 2023 Higher-order dynamic mode decomposition (HODMD) has proved to be an efficient tool for the analysis and prediction of complex dynamical systems described by data-driven models.
Keren Li, Sergey Utyuzhnikov
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Dynamic mode decomposition of numerical data in natural circulation
Brazilian Journal of Radiation Sciences, 2021 Dynamic mode decomposition (DMD) has been used for experimental and numerical data analysis in fluid dynamics. Despite of its advantages, the application of the DMD methodology to investigate the natural circulation in nuclear reactors are very scarce in
José Luiz Horacio Faccini
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Identification of Linear Time-Invariant Systems with Dynamic Mode Decomposition
Mathematics, 2022 Dynamic mode decomposition (DMD) is a popular data-driven framework to extract linear dynamics from complex high-dimensional systems. In this work, we study the system identification properties of DMD.
Jan Heiland, Benjamin Unger
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Delay-Embedding Spatio-Temporal Dynamic Mode Decomposition
MathematicsSpatio-temporal dynamic mode decomposition (STDMD) is an extension of dynamic mode decomposition (DMD) designed to handle spatio-temporal datasets. It extends the framework so that it can analyze data that have both spatial and temporal variations.
Gyurhan Nedzhibov
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Multiplicative Dynamic Mode Decomposition
SIAM Journal on Applied Dynamical Systems24 pages, 13 figures.
Nicolas Boullé, Matthew J. Colbrook
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Automatic Seizure Detection Using Multi-Resolution Dynamic Mode Decomposition
IEEE Access, 2019 Epilepsy is one of the most prevalent neurological issues faced by a large population around the globe. Epilepsy is marked by intermittent seizures, the detection of which can be a challenging problem.
Muhammad Bilal +5 more
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Physics-informed dynamic mode decomposition
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2023 In this work, we demonstrate how physical principles—such as symmetries, invariances and conservation laws—can be integrated into the dynamic mode decomposition (DMD). DMD is a widely used data analysis technique that extracts low-rank modal structures and dynamics from high-dimensional ...
Peter J. Baddoo +4 more
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Centering Data Improves the Dynamic Mode Decomposition [PDF]
SIAM Journal on Applied Dynamical Systems, 2020 Dynamic mode decomposition (DMD) is a data-driven method that models high-dimensional time series as a sum of spatiotemporal modes, where the temporal modes are constrained by linear dynamics. For nonlinear dynamical systems exhibiting strongly coherent structures, DMD can be a useful approximation to extract dominant, interpretable modes.
Seth M. Hirsh +3 more
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