Results 11 to 20 of about 13,527,272 (221)
Challenges in dynamic mode decomposition. [PDF]
J R Soc 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
Wu Z, Brunton SL, Revzen S.
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Robust Dynamic Mode Decomposition [PDF]
IEEE Access, 2022 This paper develops a robust dynamic mode decomposition (RDMD) method endowed with statistical and numerical robustness. Statistical robustness ensures estimation efficiency at the Gaussian and non-Gaussian probability distributions, including heavy ...
Amir Hossein Abolmasoumi +2 more
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Non-Stationary Dynamic Mode Decomposition [PDF]
IEEE Access, 2023 Many physical processes display complex high-dimensional time-varying behavior, from global weather patterns to brain activity. An outstanding challenge is to express high dimensional data in terms of a dynamical model that reveals their spatiotemporal ...
John Ferre +4 more
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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, R. Kamalapurkar
semanticscholar +6 more sources
Kernel learning for robust dynamic mode decomposition: linear and nonlinear disambiguation optimization. [PDF]
Proc Math Phys Eng Sci, 2022 Research in modern data-driven dynamical systems is typically focused on the three key challenges of high dimensionality, unknown dynamics and nonlinearity.
Baddoo PJ +3 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 ...
Joshua L. Proctor +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)
J. Nathan Kutz +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 +3 more
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MEASURING THE PRESSURE IN DYNAMIC MODE
Сучасні інформаційні системи, 2019 The subject study of the article is to measure the pressure in the dynamic mode used in parts of the regional center of standardization and metrology during the verification and calibration of measuring equipment and during their operation.
Vladimir Kononov +2 more
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On dynamic mode decomposition: Theory and applications [PDF]
Journal of Computational Dynamics, 2013 Originally introduced in the fluid mechanics community, dynamic mode decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of nonlinear systems.
Jonathan H. Tu +4 more
semanticscholar +4 more sources