Results 11 to 20 of about 3,075 (261)

Challenges in dynamic mode decomposition [PDF]

Journal of The Royal Society Interface, 2021
Dynamic mode decomposition (DMD) is a powerful tool for extracting spatial and temporal patterns from multi-dimensional time series, and it has been used successfully in a wide range of fields, including fluid mechanics, robotics and neuroscience. Two of the main challenges remaining in DMD research are noise sensitivity and issues related to Krylov ...
Ziyou Wu, Steven L. Brunton, Shai Revzen
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Singular Dynamic Mode Decomposition

SIAM Journal on Applied Dynamical Systems, 2023
11 pages. YouTube playlist supporting this manuscript can be found here: https://youtube.com/playlist?list=PLldiDnQu2phsZdFP3nHoGnk_Aq ...
Joel A. Rosenfeld, Rushikesh Kamalapurkar   +1 more
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Constrained Dynamic Mode Decomposition

IEEE Transactions on Visualization and Computer Graphics, 2022
Frequency-based decomposition of time series data is used in many visualization applications. Most of these decomposition methods (such as Fourier transform or singular spectrum analysis) only provide interaction via pre- and post-processing, but no means to influence the core algorithm.
Tim, Krake, Daniel, Klotzl, Bernhard, Eberhardt, Daniel, Weiskopf   +3 more
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Dynamic-mode decomposition and optimal prediction [PDF]

Physical Review E, 2021
The Dynamic-Mode Decomposition (DMD) is a well established data-driven method of finding temporally evolving linear-mode decompositions of nonlinear time series. Traditionally, this method presumes that all relevant dimensions are sampled through measurement.
Christopher W. Curtis, Daniel Jay Alford-Lago   +1 more
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Consistent Dynamic Mode Decomposition [PDF]

SIAM Journal on Applied Dynamical Systems, 2019
We propose a new method for computing Dynamic Mode Decomposition (DMD) evolution matrices, which we use to analyze dynamical systems. Unlike the majority of existing methods, our approach is based on a variational formulation consisting of data alignment penalty terms and constitutive orthogonality constraints.
Azencot, Omri, Yin, Wotao, Bertozzi, Andrea   +2 more
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Multiresolution Dynamic Mode Decomposition [PDF]

SIAM Journal on Applied Dynamical Systems, 2016
Summary: We demonstrate that the integration of the recently developed dynamic mode decomposition (DMD) with a multiresolution analysis allows for a decomposition method capable of robustly separating complex systems into a hierarchy of multiresolution time-scale components. A one-level separation allows for background (low-rank) and foreground (sparse)
Kutz, J. Nathan, Fu, Xing, Brunton, Steven L.   +2 more
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Randomized Dynamic Mode Decomposition [PDF]

SIAM Journal on Applied Dynamical Systems, 2019
This paper presents a randomized algorithm for computing the near-optimal low-rank dynamic mode decomposition (DMD). Randomized algorithms are emerging techniques to compute low-rank matrix approximations at a fraction of the cost of deterministic algorithms, easing the computational challenges arising in the area of `big data'. The idea is to derive a
N. Benjamin Erichson, Lionel Mathelin, J. Nathan Kutz, Steven L. Brunton   +3 more
openaire   +4 more sources

Application of dynamic mode decomposition and compatible window-wise dynamic mode decomposition in deciphering COVID-19 dynamics of India

Computational and Mathematical Biophysics, 2023
The COVID-19 pandemic recently caused a huge impact on India, not only in terms of health but also in terms of economy. Understanding the spatio-temporal patterns of the disease spread is crucial for controlling the outbreak.
Rana Kanav Singh, Kumari Nitu
doaj   +1 more source

Generalized eigenvalue approach for dynamic mode decomposition

AIP Advances, 2021
Traditional dynamic mode decomposition (DMD) methods inevitably involve matrix inversion, which often brings in numerical instability and spurious modes.
Wei Zhang, Mingjun Wei
doaj   +1 more source

Port-Hamiltonian Dynamic Mode Decomposition

SIAM Journal on Scientific Computing, 2023
We present a novel physics-informed system identification method to construct a passive linear time-invariant system. In more detail, for a given quadratic energy functional, measurements of the input, state, and output of a system in the time domain, we find a realization that approximates the data well while guaranteeing that the energy functional ...
Riccardo Morandin, Jonas Nicodemus, Benjamin Unger   +2 more
openaire   +2 more sources