Fetching the paper…
Reading the bibliography…
Dynamic mode decomposition (DMD) provides a regression framework for adaptively learning a best-fit linear dynamics model over snapshots of temporal, or spatio-temporal, data.
On vortex shedding from a circular cylinder in the critical reynolds number regime
P.W. Bearman · 1969
Earlier work this paper cites.
Classification and regression trees
Leo Breiman, Jerome Friedman, Charles J Stone, and Richard A Olshen · 1984
Earlier work this paper cites.
A hierarchy of low-dimensional models for the transient and post-transient cylinder wake
B. R. Noack, K. Afanasiev, M. Morzynski, G. Tadmor, and F. Thiele · 2003
Earlier work this paper cites.
Dynamic mode decomposition of numerical and experimental data
P. J. Schmid and J. Sesterhenn · 2008
Earlier work this paper cites.
Spectral analysis of nonlinear flows
C. W. Rowley, I. Mezić, S. Bagheri, P. Schlatter, and D.S. Henningson · 2009
Earlier work this paper cites.
Coherent Dynamics and Instability of Power Grids
Yoshihiko Susuki, Igor Mezić, and Takashi Hikihara · 2009
Earlier work this paper cites.
Dynamic mode decomposition of numerical and experimental data
P. J. Schmid · 2010
Earlier work this paper cites.
Nonlinear Koopman modes and coherency identification of coupled swing dynamics
Yoshihiko Susuki and Igor Mezic · 2011
Earlier work this paper cites.
Reduced-order modelling for flow control
Bernd R Noack, Marek Morzynski, and Gilead Tadmor · 2011
Earlier work this paper cites.
Variants of dynamic mode decomposition: Boundary condition, Koopman, and Fourier analyses
K. K. Chen, J. H. Tu, and C. W. Rowley · 2012
Earlier work this paper cites.
An error analysis of the dynamic mode decomposition
Daniel Duke, Julio Soria, and Damon Honnery · 2012
Earlier work this paper cites.
Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data
J. N. Kutz · 2013
Earlier work this paper cites.
Koopman-mode decomposition of the cylinder wake
Shervin Bagheri · 2013
Earlier work this paper cites.
Dynamic mode decomposition for real-time background/foreground separation in video
Jacob Grosek and J Nathan Kutz · 2014
Earlier work this paper cites.
Effects of weak noise on oscillating flows: Linking quality factor, Floquet modes, and Koopman spectrum
Shervin Bagheri · 2014
Earlier work this paper cites.
On dynamic mode decomposition: theory and applications
J. H. Tu, C. W. Rowley, D. M. Luchtenburg, S. L. Brunton, and J. N. Kutz · 2014
Earlier work this paper cites.
Sparsity-promoting dynamic mode decomposition
Mihailo R Jovanović, Peter J Schmid, and Joseph W Nichols · 2014
Earlier work this paper cites.
Compressed sensing and dynamic mode decomposition
S. L. Brunton, J. L. Proctor, J. H. Tu, and J. N. Kutz · 2015
Cited alongside, same era.
Discovering dynamic patterns from infectious disease data using dynamic mode decomposition
Joshua L Proctor and Philip A Eckhoff · 2015
Cited alongside, same era.
Uncertainty quantification of the dynamic mode decomposition
Anthony DeGennaro, Scott Dawson, and Clarence Rowley · 2015
Cited alongside, same era.
Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems
J. N. Kutz, S. L. Brunton, B. W. Brunton, and J. L. Proctor · 2016
Cited alongside, same era.
Dynamic mode decomposition with control
Joshua L Proctor, Steven L Brunton, and J Nathan Kutz · 2016
Cited alongside, same era.
Multiresolution dynamic mode decomposition
J Nathan Kutz, Xing Fu, and Steven L Brunton · 2016
Cited alongside, same era.
Dynamic mode decomposition for plasma diagnostics and validation
Roy Taylor, J Nathan Kutz, Kyle Morgan, and Brian A Nelson · 2018
Later among the works it cites.
Variable projection methods for an optimized dynamic mode decomposition
Travis Askham and J Nathan Kutz · 2018
Later among the works it cites.
PyDMD: Python dynamic mode decomposition
N. Demo, M. Tezzele, and G. Rozza · 2018
Later among the works it cites.
Randomized matrix decompositions using R
N. B. Erichson, S. Voronin, S. L. Brunton, and J. N. Kutz · 2019
Later among the works it cites.
Discovery of nonlinear multiscale systems: Sampling strategies and embeddings
Kathleen P Champion, Steven L Brunton, and J Nathan Kutz · 2019
Later among the works it cites.
Scalable diagnostics for global atmospheric chemistry using ristretto library (version 1.0)
Meghana Velegar, N Benjamin Erichson, Christoph A Keller, and J Nathan Kutz · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition
B. W. Brunton, L. A. Johnson, J. G. Ojemann, and J. N. Kutz · 2016
Cited alongside, same era.
Compressed dynamic mode decomposition for real-time object detection
N. B. Erichson, S. L. Brunton, and J. N. Kutz · 2016
Cited alongside, same era.
Dynamic mode decomposition for financial trading strategies
Jordan Mann and J Nathan Kutz · 2016
Cited alongside, same era.
Characterizing and correcting for the effect of sensor noise in the dynamic mode decomposition
Scott TM Dawson, Maziar S Hemati, Matthew O Williams, and Clarence W Rowley · 2016
Cited alongside, same era.
Nonlinear model order reduction via dynamic mode decomposition
Alessandro Alla and J Nathan Kutz · 2017
Cited alongside, same era.
De-biasing the dynamic mode decomposition for applied koopman spectral analysis of noisy datasets
Maziar S Hemati, Clarence W Rowley, Eric A Deem, and Louis N Cattafesta · 2017
Cited alongside, same era.
Later among the works it cites.
Local Koopman operators for data-driven control of robotic systems
X Tan G Mamakoukas, M Castano and Todd Murphey · 2019
Later among the works it cites.
Centering Data Improves the Dynamic Mode Decomposition
Seth M. Hirsh, Kameron Decker Harris, J. Nathan Kutz, and Bingni W. Brunton · 2019
Later among the works it cites.
Consistent dynamic mode decomposition
Omri Azencot, Wotao Yin, and Andrea Bertozzi · 2019
Later among the works it cites.
From Fourier to Koopman: Spectral methods for long-term time series prediction
Henning Lange, Steven L Brunton, and Nathan Kutz · 2020
Later among the works it cites.
Derivative-based Koopman operators for real-time control of robotic systems
X Tan G Mamakoukas, M Castano and Todd Murphey · 2020
Later among the works it cites.
Characterizing magnetized plasmas with dynamic mode decomposition
Alan A Kaptanoglu, Kyle D Morgan, Chris J Hansen, and Steven L Brunton · 2020
Later among the works it cites.
Time-delay observables for Koopman: Theory and applications
M. Kamb, E. Kaiser, S. L. Brunton, and J. N. Kutz · 2020
Later among the works it cites.
Robust principal component analysis for particle image velocimetry
Isabel Scherl, Benjamin Strom, Jessica K Shang, Owen Williams, Brian L Polagye, and Steven L Brunton · 2020
Later among the works it cites.
Towards understanding ensemble, knowledge distillation and self-distillation in deep learning
Zeyuan Allen-Zhu and Yuanzhi Li · 2020
Later among the works it cites.
Modal analysis of turbulent flow near an inclined bank–longitudinal structure junction
Nasser Heydari, Panayiotis Diplas, J Nathan Kutz, and Soheil Sadeghi Eshkevari · 2021
Closest in time.
Structured time-delay models for dynamical systems with connections to frenet-serret frame
Seth M Hirsh, Sara M Ichinaga, Steven L Brunton, J Nathan Kutz, and Bingni W Brunton · 2021
Closest in time.