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On Alternative Algorithms for Computing Dynamic Mode Decomposition
Computation, 2022 Dynamic mode decomposition (DMD) is a data-driven, modal decomposition technique that describes spatiotemporal features of high-dimensional dynamic data.
Gyurhan Nedzhibov
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Bilinear dynamic mode decomposition for quantum control
New Journal of Physics, 2021 Data-driven methods for establishing quantum optimal control (QOC) using time-dependent control pulses tailored to specific quantum dynamical systems and desired control objectives are critical for many emerging quantum technologies.
Andy Goldschmidt +4 more
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Higher Order Dynamic Mode Decomposition [PDF]
SIAM Journal on Applied Dynamical Systems, 2017 This paper deals with an extension of dynamic mode decomposition (DMD), which is appropriate to treat general periodic and quasi-periodic dynamics, and transients decaying to periodic and quasiperiodic attractors, including cases (not accessible to standard DMD) that show limited spatial complexity but a very large number of involved frequencies.
Le Clainche Martínez, Soledad +1 more
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DYNAMIC BANDWIDTH VARIATIONAL MODE DECOMPOSITION
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 <p>Signal decomposition techniques aim to break down nonstationary signals into their oscillatory components, serving as a preliminary step in various practical signal processing applications. This has motivated researchers to explore different strategies, yielding several distinct approaches.
Andreas Angelou +2 more
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Deep learning enhanced dynamic mode decomposition
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2022 Koopman operator theory shows how nonlinear dynamical systems can be represented as an infinite-dimensional, linear operator acting on a Hilbert space of observables of the system. However, determining the relevant modes and eigenvalues of this infinite-dimensional operator can be difficult.
D. J. Alford-Lago +3 more
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Randomized Projection Learning Method for Dynamic Mode Decomposition
Mathematics, 2021 A data-driven analysis method known as dynamic mode decomposition (DMD) approximates the linear Koopman operator on a projected space. In the spirit of Johnson–Lindenstrauss lemma, we will use a random projection to estimate the DMD modes in a reduced ...
Sudam Surasinghe, Erik M. Bollt
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A characteristic dynamic mode decomposition [PDF]
Theoretical and Computational Fluid Dynamics, 2019 Temporal or spatial structures are readily extracted from complex data by modal decompositions like Proper Orthogonal Decomposition (POD) or Dynamic Mode Decomposition (DMD). Subspaces of such decompositions serve as reduced order models and define either spatial structures in time or temporal structures in space.
Sesterhenn, Jörn, Shahirpour, Amir
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Higher order dynamic mode decomposition beyond aerospace engineering
Results in Engineering, 2023 It is a well known fact that fluid dynamics play a crucial rule in countless fields in scientific and industrial applications, including nature and medicine (ocean currents, fluid motion around jellyfish, blood circulation...), in energy production (wind
N. Groun +4 more
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Dynamic Mode Decomposition with Control [PDF]
SIAM Journal on Applied Dynamical Systems, 2016 We develop a new method which extends Dynamic Mode Decomposition (DMD) to incorporate the effect of control to extract low-order models from high-dimensional, complex systems. DMD finds spatial-temporal coherent modes, connects local-linear analysis to nonlinear operator theory, and provides an equation-free architecture which is compatible with ...
Proctor, Joshua L. +2 more
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Comparative study of modal decomposition and dynamic equation reconstruction in data-driven modeling
AIP Advances, 2021 Due to the increasing complexity of dynamic systems, it is increasingly difficult for traditional mathematical methods to meet the modeling requirements of complex dynamic systems.
Zhenglong Yin +4 more
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