Results 11 to 20 of about 47,248 (288)
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 +2 more
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Swarm Modeling With Dynamic Mode Decomposition
IEEE Access, 2022 Modelling biological or engineering swarms is challenging due to the inherently high dimension of the system, despite the often low-dimensional emergent dynamics.
Emma Hansen +2 more
doaj +3 more sources
Towards an Adaptive Dynamic Mode Decomposition
Results in Control and Optimization, 2022 Dynamic Mode Decomposition (DMD) is a tool that creates an approximate model from spatio-temporal data. We have developed an architecture of this tool that will adapt to the data from a given problem by leveraging time delay coordinates, projections, and
Mohammad N. Murshed, M. Monir Uddin
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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 ...
Vega De Prada, Jose Manuel +3 more
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Dynamic mode decomposition of extreme events [PDF]
Nonlinear Processes in GeophysicsMost data-driven methods, among them Dynamic Mode Decomposition (DMD), focus on analysing and reconstructing the average behaviour of a system. However, the primary interest often lies in the anomalous behaviour, known as extreme events.
M. Ann, J. Behrens, J. Sillmann
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DYNAMIC BANDWIDTH VARIATIONAL MODE DECOMPOSITION
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 Signal decomposition techniques aim to break down nonstationary signals into their oscillatory components, serving as a preliminary step in various practical signal processing applications.
Georgios Apostolidis (16622763) +2 more
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Bayesian Dynamic Mode Decomposition [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Dynamic mode decomposition (DMD) is a data-driven method for calculating a modal representation of a nonlinear dynamical system, and it has been utilized in various fields of science and engineering. In this paper, we propose Bayesian DMD, which provides
Yasuo Tabei +3 more
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Applications of the dynamic mode decomposition
Theoretical and Computational Fluid Dynamics, 2010 International audienceThe decomposition of experimental data into dynamic modes using a data-based algorithm is applied to Schlieren snapshots of a helium jet and to time-resolved PIV-measurements of an unforced and harmonically forced jet. The algorithm
Peter J Schmid, Matthew P Juniper
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Randomized Dynamic Mode Decomposition [PDF]
SIAM Journal on Applied Dynamical Systems, 2019 International audienceThis 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 ...
Brunton, Steven L. +3 more
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Latent Diffeomorphic Dynamic Mode Decomposition
Applied Mathematics LettersWe present Latent Diffeomorphic Dynamic Mode Decomposition (LDDMD), a new data reduction approach for the analysis of non-linear systems that combines the interpretability of Dynamic Mode Decomposition (DMD) with the predictive power of Recurrent Neural ...
Bertozzi, Andrea L +2 more
core +4 more sources