Skip to main content

News about Scitation

In December 2016 Scitation will launch with a new design, enhanced navigation and a much improved user experience.

To ensure a smooth transition, from today, we are temporarily stopping new account registration and single article purchases. If you already have an account you can continue to use the site as normal.

For help or more information please visit our FAQs.

banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
1. P. Schmid, “Dynamic mode decomposition of numerical and experimental data,” J. Fluid Mech. 656, 528 (2010).
2. S. Bagheri, “Koopman-mode decomposition of the cylinder wake,” J. Fluid Mech. 726, 596623 (2013).
3. C. W. Rowley, I. Mezić, S. Bagheri, P. Schlatter, and D. S. Henningson, “Spectral analysis of nonlinear flows,” J. Fluid Mech. 641, 115127 (2009).
4. M. R. Jovanović, P. J. Schmid, and J. W. Nichols, “Sparsity-promoting dynamic mode decomposition,” Phys. Fluids 26, 024103 (2014).
5. M. Grilli, A. Vázquez-Quesada, and M. Ellero, “Transition to turbulence and mixing in a viscoelastic fluid flowing inside a channel with a periodic array of cylindrical obstacles,” Phys. Rev. Lett. 110, 174501 (2013).
6. I. Mezić, “Analysis of fluid flows via spectral properties of the Koopman operator,” Annu. Rev. Fluid Mech. 45, 357378 (2013).
7. P. Holmes, J. L. Lumley, G. Berkooz, and C. W. Rowley, Turbulence, Coherent Structures, Dynamical Systems and Symmetry, 2nd ed., Cambridge Monographs on Mechanics (Cambridge University Press, Cambridge, 2012).
8. H. Yu, M. Leeser, G. Tadmor, and S. Siegel, “Real-time particle image velocimetry for feedback loops using FPGA implementation,” J. Aerospace Comput., Inform., Commun. 3, 5262 (2006).
9.See supplementary material at for Matlab implementations of the algorithms presented in this letter. [Supplementary Material]
10. J. H. Tu, C. W. Rowley, D. M. Luchtenburg, S. L. Brunton, and J. N. Kutz, “On dynamic mode decomposition: Theory and applications,” J. Comput. Dyn. (in press).
11. W. Hoffman, “Iterative algorithms for Gram-Schmidt orthogonalization,” Computing 41, 335348 (1989).
12. J. H. Tu, C. W. Rowley, J. N. Kutz, and J. Shang, “Toward compressed DMD: Spectral analysis of fluid flows using sub-Nyquist-rate PIV data,” Exp. Fluids 55, 113 (2014).
13. B. A. Belson, J. H. Tu, and C. W. Rowley, “A parallelized model reduction library,” ACM Trans. Math. Software 40, 3013023 (2014).

Data & Media loading...


Article metrics loading...



We formulate a low-storage method for performing dynamic mode decomposition that can be updated inexpensively as new data become available; this formulation allows dynamical information to be extracted from large datasets and data streams. We present two algorithms: the first is mathematically equivalent to a standard “batch-processed” formulation; the second introduces a compression step that maintains computational efficiency, while enhancing the ability to isolate pertinent dynamical information from noisy measurements. Both algorithms reliably capture dominant fluid dynamic behaviors, as demonstrated on cylinder wake data collected from both direct numerical simulations and particle image velocimetry experiments.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd