On dynamic mode decomposition:  Theory and applications

Jonathan H. Tu; Clarence W. Rowley; Dirk M. Luchtenburg; Steven L. Brunton; J. Nathan Kutz

doi:10.3934/jcd.2014.1.391

Article Contents

2014, Volume 1, Issue 2: 391-421. Doi: 10.3934/jcd.2014.1.391

This issue Previous Article Equation-free computation of coarse-grained center manifolds of microscopic simulators Next Article

On dynamic mode decomposition: Theory and applications

1.
Dept. of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544
2.
Dept. of Applied Mathematics, University of Washington, Seattle, WA 98195

Received: November 2013

Revised: November 2014

Published: June 2014

Abstract / Introduction Related Papers Cited by

Abstract

Originally introduced in the fluid mechanics community, dynamic mode decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of nonlinear systems. However, existing DMD theory deals primarily with sequential time series for which the measurement dimension is much larger than the number of measurements taken. We present a theoretical framework in which we define DMD as the eigendecomposition of an approximating linear operator. This generalizes DMD to a larger class of datasets, including nonsequential time series. We demonstrate the utility of this approach by presenting novel sampling strategies that increase computational efficiency and mitigate the effects of noise, respectively. We also introduce the concept of linear consistency, which helps explain the potential pitfalls of applying DMD to rank-deficient datasets, illustrating with examples. Such computations are not considered in the existing literature but can be understood using our more general framework. In addition, we show that our theory strengthens the connections between DMD and Koopman operator theory. It also establishes connections between DMD and other techniques, including the eigensystem realization algorithm (ERA), a system identification method, and linear inverse modeling (LIM), a method from climate science. We show that under certain conditions, DMD is equivalent to LIM.

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Mathematics Subject Classification: Primary: 37M10, 65P99; Secondary: 47B33.

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References

[1]	S. Bagheri, Koopman-mode decomposition of the cylinder wake, J. Fluid Mech., 726 (2013), 596-623.doi: 10.1017/jfm.2013.249.
[2]	B. A. Belson, J. H. Tu and C. W. Rowley, A Parallelized Model Reduction Library, ACM T. Math. Software, 2013 (accepted).
[3]	M. B. Blumenthal, Predictability of a coupled ocean-atmosphere model, J. Climate, 4 (1991), 766-784.doi: 10.1175/1520-0442(1991)004<0766:POACOM>2.0.CO;2.
[4]	K. K. Chen, J. H. Tu and C. W. Rowley, Variants of dynamic mode decomposition: Boundary condition, Koopman, and Fourier analyses, J. Nonlinear Sci., 22 (2012), 887-915.doi: 10.1007/s00332-012-9130-9.
[5]	T. Colonius and K. Taira, A fast immersed boundary method using a nullspace approach and multi-domain far-field boundary conditions, Comput. Method Appl. M., 197 (2008), 2131-2146.doi: 10.1016/j.cma.2007.08.014.
[6]	D. Duke, D. Honnery and J. Soria, Experimental investigation of nonlinear instabilities in annular liquid sheets, J. Fluid Mech., 691 (2012), 594-604.doi: 10.1017/jfm.2011.516.
[7]	D. Duke, J. Soria and D. Honnery, An error analysis of the dynamic mode decomposition, Exp. Fluids, 52 (2012), 529-542.doi: 10.1007/s00348-011-1235-7.
[8]	P. J. Goulart, A. Wynn and D. Pearson, Optimal mode decomposition for high dimensional systems, In Proceedings of the 51st IEEE Conference on Decision and Control, 2012 (2012), 4965-4970.doi: 10.1109/CDC.2012.6426995.
[9]	M. Grilli, P. J. Schmid, S. Hickel and N. A. Adams, Analysis of unsteady behaviour in shockwave turbulent boundary layer interaction, J. Fluid Mech., 700 (2012), 16-28.doi: 10.1017/jfm.2012.37.
[10]	K. Hasselmann, PIPs and POPs: The reduction of complex dynamical-systems using Principal Interaction and Oscillation Patterns, J. Geophys. Res.-Atmos., 93 (1988), 11015-11021.doi: 10.1029/JD093iD09p11015.
[11]	B. L. Ho and R. E. Kalman, Effective construction of linear state-variable models from input/output data, Proceedings of the Third Annual Allerton Conference on Circuit and System Theory, (1965), 449-459.
[12]	P. J. Holmes, J. L. Lumley, G. Berkooz and C. W. Rowley, Turbulence, Coherent Structures, Dynamical Systems and Symmetry, Cambridge University Press, Cambridge, UK, 2nd edition, 2012.doi: 10.1017/CBO9780511919701.
[13]	H. Hotelling, Analysis of a complex of statistical variables into principal components, J. Educ. Psychol., 24 (1933), 417-441.doi: 10.1037/h0071325.
[14]	H. Hotelling, Analysis of a complex of statistical variables into principal components, J. Educ. Psychol., 24 (1933), 498-520.
[15]	M. R. Jovanović, P. J. Schmid and J. W. Nichols, Sparsity-promoting dynamic mode decomposition, Phys. Fluids, 26 (2014), 024103, arXiv:1309.4165v1.doi: 10.1063/1.4863670.
[16]	J. N. Juang and R. S. Pappa, An eigensystem realization-algorithm for modal parameter-identification and model-reduction, J. Guid. Control Dynam., 8 (1985), 620-627.doi: 10.2514/3.20031.
[17]	E. N. Lorenz, Empirical orthogonal functions and statistical weather prediction, Technical report, Massachusetts Institute of Technology, Dec. 1956.
[18]	Z. Ma, S. Ahuja and C. W. Rowley, Reduced-order models for control of fluids using the eigensystem realization algorithm, Theor. Comp. Fluid Dyn., 25 (2011), 233-247.doi: 10.1007/s00162-010-0184-8.
[19]	L. Massa, R. Kumar and P. Ravindran, Dynamic mode decomposition analysis of detonation waves, Phys. Fluids, 24, June 2012.
[20]	I. Mezić, Spectral properties of dynamical systems, model reduction and decompositions, Nonlin. Dynam., 41 (2005), 309-325.doi: 10.1007/s11071-005-2824-x.
[21]	I. Mezić, Analysis of fluid flows via spectral properties of the Koopman operator, Annu. Rev. Fluid Mech., 45 (2013), 357-378.doi: 10.1146/annurev-fluid-011212-140652.
[22]	T. W. Muld, G. Efraimsson and D. S. Henningson, Flow structures around a high-speed train extracted using proper orthogonal decomposition and dynamic mode decomposition, Comput. Fluids, 57 (2012), 87-97.doi: 10.1016/j.compfluid.2011.12.012.
[23]	B. R. Noack, K. Afanasiev, M. Morzyński, G. Tadmor and F. Thiele, A hierarchy of low-dimensional models for the transient and post-transient cylinder wake, J. Fluid Mech., 497 (2003), 335-363.doi: 10.1017/S0022112003006694.
[24]	B. R. Noack, G. Tadmor and M. Morzyński, Actuation models and dissipative control in empirical Galerkin models of fluid flows, In Proceedings of the American Control Conference, (2004), 5722-5727.
[25]	K. Pearson, LIII. on lines and planes of closest fit to systems of points in space, Philos. Mag., 2 (1901), 559-572.doi: 10.1080/14786440109462720.
[26]	C. Penland, Random forcing and forecasting using Principal Oscillation Pattern analysis, Mon. Weather Rev., 117 (1989), 2165-2185.
[27]	C. Penland and T. Magorian, Prediction of Niño 3 sea-surface temperatures using linear inverse modeling, J. Climate, 6 (1993), 1067-1076.
[28]	C. W. Rowley, Model reduction for fluids, using balanced proper orthogonal decomposition, Int. J. Bifurcat. Chaos, 15 (2005), 997-1013.doi: 10.1142/S0218127405012429.
[29]	C. W. Rowley, I. Mezic, S. Bagheri, P. Schlatter and D. S. Henningson, Spectral analysis of nonlinear flows, J. Fluid Mech., 641 (2009), 115-127.doi: 10.1017/S0022112009992059.
[30]	P. J. Schmid, Dynamic mode decomposition of numerical and experimental data, J. Fluid Mech., 656 (2010), 5-28.doi: 10.1017/S0022112010001217.
[31]	P. J. Schmid, Application of the dynamic mode decomposition to experimental data, Exp. Fluids, 50 (2011), 1123-1130.doi: 10.1007/s00348-010-0911-3.
[32]	P. J. Schmid, L. Li, M. P. Juniper and O. Pust, Applications of the dynamic mode decomposition, Theor. Comp. Fluid Dyn., 25 (2011), 249-259.doi: 10.1007/s00162-010-0203-9.
[33]	P. J. Schmid and J. Sesterhenn, Dynamic mode decomposition of numerical and experimental data, Journal of Fluid Mechanics, 656 (2010), 5-28.doi: 10.1017/S0022112010001217.
[34]	P. J. Schmid, D. Violato and F. Scarano, Decomposition of time-resolved tomographic PIV, Exp. Fluids, 52 (2012), 1567-1579.doi: 10.1007/s00348-012-1266-8.
[35]	A. Seena and H. J. Sung, Dynamic mode decomposition of turbulent cavity flows for self-sustained oscillations, Int. J. Heat Fluid Fl., 32 (2011), 1098-1110.doi: 10.1016/j.ijheatfluidflow.2011.09.008.
[36]	O. Semeraro, G. Bellani and F. Lundell, Analysis of time-resolved PIV measurements of a confined turbulent jet using POD and Koopman modes, Exp. Fluids, 53 (2012), 1203-1220.doi: 10.1007/s00348-012-1354-9.
[37]	J. R. Singler, Optimality of balanced proper orthogonal decomposition for data reconstruction, Numerical Functional Analysis and Optimization, 31 (2010), 852-869.doi: 10.1080/01630563.2010.500022.
[38]	L. Sirovich, Turbulence and the dynamics of coherent structures. 2. Symmetries and transformations, Q. Appl. Math., 45 (1987), 573-582.
[39]	K. Taira and T. Colonius, The immersed boundary method: A projection approach, J. Comput. Phys., 225 (2007), 2118-2137.doi: 10.1016/j.jcp.2007.03.005.
[40]	L. N. Trefethen and D. Bau III, Numerical Linear Algebra, SIAM, Philadelphia, 1997.doi: 10.1137/1.9780898719574.
[41]	J. H. Tu, J. Griffin, A. Hart, C. W. Rowley, L. N. Cattafesta III and L. S. Ukeiley, Integration of non-time-resolved PIV and time-resolved velocity point sensors for dynamic estimation of velocity fields, Exp. Fluids, 54 (2013), pp1429.doi: 10.2514/6.2012-33.
[42]	J. H. Tu and C. W. Rowley, An improved algorithm for balanced POD through an analytic treatment of impulse response tails, J. Comput. Phys., 231 (2012), 5317-5333.doi: 10.1016/j.jcp.2012.04.023.
[43]	J. H. Tu, C. W. Rowley, E. Aram and R. Mittal, Koopman spectral analysis of separated flow over a finite-thickness flat plate with elliptical leading edge, AIAA Paper 2011-38, 49th AIAA Aerospace Sciences Meeting and Exhibit, Jan. 2011.doi: 10.2514/6.2011-38.
[44]	H. von Storch, G. Bürger, R. Schnur and J. S. von Storch, Principal oscillation patterns: A review, J. Climate, 8 (1995), 377-400.
[45]	M. O. Williams, I. G. Kevrekidis and C. W. Rowley, A data-driven approximation of the Koopman operator: extending dynamic mode decomposition, arXiv:1408.4408, 2014.
[46]	A. Wynn, D. Pearson, B. Ganapathisubramani and P. J. Goulart, Optimal mode decomposition for unsteady flows, J. Fluid Mech., 733 (2013), 473-503.doi: 10.1017/jfm.2013.426.