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

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June 2014, 1(2): 391-421. doi: 10.3934/jcd.2014.1.391

On dynamic mode decomposition: Theory and applications

Jonathan H. Tu ^1, , Clarence W. Rowley ^1, , Dirk M. Luchtenburg ^1, , Steven L. Brunton ^2, and J. Nathan Kutz ^2,

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

Received November 2013 Revised November 2014 Published December 2014

Abstract
Related Papers

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.

Keywords: spectral analysis, Dynamic mode decomposition, Koopman operator, time series analysis, reduced-order models..

Mathematics Subject Classification: Primary: 37M10, 65P99; Secondary: 47B3.

Citation: Jonathan H. Tu, Clarence W. Rowley, Dirk M. Luchtenburg, Steven L. Brunton, J. Nathan Kutz. On dynamic mode decomposition: Theory and applications. Journal of Computational Dynamics, 2014, 1 (2) : 391-421. doi: 10.3934/jcd.2014.1.391

References:

[1]	S. Bagheri, Koopman-mode decomposition of the cylinder wake, J. Fluid Mech., 726 (2013), 596-623. doi: 10.1017/jfm.2013.249. Google Scholar
[2]	B. A. Belson, J. H. Tu and C. W. Rowley, A Parallelized Model Reduction Library, ACM T. Math. Software, 2013 (accepted). Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[13]	H. Hotelling, Analysis of a complex of statistical variables into principal components, J. Educ. Psychol., 24 (1933), 417-441. doi: 10.1037/h0071325. Google Scholar
[14]	H. Hotelling, Analysis of a complex of statistical variables into principal components, J. Educ. Psychol., 24 (1933), 498-520. Google Scholar
[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. Google Scholar
[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. Google Scholar
[17]	E. N. Lorenz, Empirical orthogonal functions and statistical weather prediction, Technical report, Massachusetts Institute of Technology, Dec. 1956. Google Scholar
[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. Google Scholar
[19]	L. Massa, R. Kumar and P. Ravindran, Dynamic mode decomposition analysis of detonation waves, Phys. Fluids, 24, June 2012. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[26]	C. Penland, Random forcing and forecasting using Principal Oscillation Pattern analysis, Mon. Weather Rev., 117 (1989), 2165-2185. Google Scholar
[27]	C. Penland and T. Magorian, Prediction of Niño 3 sea-surface temperatures using linear inverse modeling, J. Climate, 6 (1993), 1067-1076. Google Scholar
[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. Google Scholar
[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. Google Scholar
[30]	P. J. Schmid, Dynamic mode decomposition of numerical and experimental data, J. Fluid Mech., 656 (2010), 5-28. doi: 10.1017/S0022112010001217. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[38]	L. Sirovich, Turbulence and the dynamics of coherent structures. 2. Symmetries and transformations, Q. Appl. Math., 45 (1987), 573-582. Google Scholar
[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. Google Scholar
[40]	L. N. Trefethen and D. Bau III, Numerical Linear Algebra, SIAM, Philadelphia, 1997. doi: 10.1137/1.9780898719574. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[44]	H. von Storch, G. Bürger, R. Schnur and J. S. von Storch, Principal oscillation patterns: A review, J. Climate, 8 (1995), 377-400. Google Scholar
[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. Google Scholar
[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. Google Scholar

show all references

References:

[1]	S. Bagheri, Koopman-mode decomposition of the cylinder wake, J. Fluid Mech., 726 (2013), 596-623. doi: 10.1017/jfm.2013.249. Google Scholar
[2]	B. A. Belson, J. H. Tu and C. W. Rowley, A Parallelized Model Reduction Library, ACM T. Math. Software, 2013 (accepted). Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[13]	H. Hotelling, Analysis of a complex of statistical variables into principal components, J. Educ. Psychol., 24 (1933), 417-441. doi: 10.1037/h0071325. Google Scholar
[14]	H. Hotelling, Analysis of a complex of statistical variables into principal components, J. Educ. Psychol., 24 (1933), 498-520. Google Scholar
[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. Google Scholar
[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. Google Scholar
[17]	E. N. Lorenz, Empirical orthogonal functions and statistical weather prediction, Technical report, Massachusetts Institute of Technology, Dec. 1956. Google Scholar
[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. Google Scholar
[19]	L. Massa, R. Kumar and P. Ravindran, Dynamic mode decomposition analysis of detonation waves, Phys. Fluids, 24, June 2012. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[26]	C. Penland, Random forcing and forecasting using Principal Oscillation Pattern analysis, Mon. Weather Rev., 117 (1989), 2165-2185. Google Scholar
[27]	C. Penland and T. Magorian, Prediction of Niño 3 sea-surface temperatures using linear inverse modeling, J. Climate, 6 (1993), 1067-1076. Google Scholar
[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. Google Scholar
[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. Google Scholar
[30]	P. J. Schmid, Dynamic mode decomposition of numerical and experimental data, J. Fluid Mech., 656 (2010), 5-28. doi: 10.1017/S0022112010001217. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[38]	L. Sirovich, Turbulence and the dynamics of coherent structures. 2. Symmetries and transformations, Q. Appl. Math., 45 (1987), 573-582. Google Scholar
[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. Google Scholar
[40]	L. N. Trefethen and D. Bau III, Numerical Linear Algebra, SIAM, Philadelphia, 1997. doi: 10.1137/1.9780898719574. Google Scholar
[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. Google Scholar
[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. Google Scholar
[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. Google Scholar
[44]	H. von Storch, G. Bürger, R. Schnur and J. S. von Storch, Principal oscillation patterns: A review, J. Climate, 8 (1995), 377-400. Google Scholar
[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. Google Scholar
[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. Google Scholar

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