Abstract
The Koopman operator induced by a dynamical system is inherently linear and provides an alternate method of studying many properties of the system, including attractor reconstruction and forecasting. Koopman eigenfunctions represent the non-mixing component of the dynamics. They factor the dynamics, which can be chaotic, into quasiperiodic rotations on tori. Here, we describe a method through which these eigenfunctions can be obtained from a kernel integral operator, which also annihilates the continuous spectrum. We show that incorporating a large number of delay coordinates in constructing the kernel of that operator results, in the limit of infinitely many delays, in the creation of a map into the point spectrum subspace of the Koopman operator. This enables efficient approximation of Koopman eigenfunctions in systems with pure point or mixed spectra. We illustrate our results with applications to product dynamical systems with mixed spectra.
Similar content being viewed by others
References
Anosov, D.V., Katok, A.B.: New examples in smooth ergodic theory: ergodic diffeomorphisms. Trans. Mosc. Math. Soc. 23, 1–35 (1970)
Ahues, M., Largillier, A., Limaye, B.: Spectral computations for bounded operators. Chapman and Hall/CRC, Boca Raton (2001)
Arbabi, H., Mezić, I.: Ergodic theory, dynamic mode decomposition and computation of spectral properties of the Koopman operator. SIAM J. Appl. Dyn. Sys. 16(4), 2096–2126 (2017). https://doi.org/10.1137/17M1125236
Aubry, N., Guyonnet, R., Lima, R.: Spatiotemporal analysis of complex signals: theory and applications. J. Stat. Phys. 64, 683–739 (1991). https://doi.org/10.1007/bf01048312
Babuška, I., Osborn, J.: Eigenvalue Problems, Handbook of Numerical Analysis, vol. 2. North Holland, Amsterdam (1991)
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15, 1373–1396 (2003). https://doi.org/10.1162/089976603321780317
Belkin, M., Niyogi, P.: Convergence of Laplacian eigenmaps. In: Advances in Neural Information Processing Systems, pp. 129–136 (2007). http://papers.nips.cc/paper/2989-convergence-of-laplacian-eigenmaps.pdf
Berry, T., Harlim, J.: Variable bandwidth diffusion kernels. Appl. Comput. Harmon. Anal. (2015). https://doi.org/10.1016/j.acha.2015.01.001
Berry, T., Sauer, T.: Consistent manifold representation for topological data analysis (2016). https://arxiv.org/pdf/1606.02353.pdf
Berry, T., Sauer, T.: Local kernels and the geometric structure of data. Appl. Comput. Harmon. Anal. 40, 439–469 (2016). https://doi.org/10.1016/j.acha.2015.03.002
Berry, T., Cressman, R., Gregurić-Ferenček, Z., Sauer, T.: Time-scale separation from diffusion-mapped delay coordinates. SIAM J. Appl. Dyn. Sys. 12, 618–649 (2013). https://doi.org/10.1137/12088183x
Berry, T., Giannakis, D., Harlim, J.: Nonparametric forecasting of low-dimensional dynamical systems. Phys. Rev. E 91, 032,915 (2015). https://doi.org/10.1103/PhysRevE.91.032915
Broomhead, D.S., King, G.P.: Extracting qualitative dynamics from experimental data. Phys. D 20(2–3), 217–236 (1986). https://doi.org/10.1016/0167-2789(86)90031-x
Brunton, S.L., Brunton, B.W., Proctor, J.L., Kaiser, E., Kutz, J.N.: Chaos as an intermittently forced linear system. Nat. Commun. 8(19) (2017). https://doi.org/10.1038/s41467-017-00030-8
Budisić, M., Mohr, R., Mezić, I.: Applied Koopmanism. Chaos 22, 047,510 (2012). https://doi.org/10.1063/1.4772195
Coifman, R., Lafon, S.: Diffusion maps. Appl. Comput. Harmon. Anal. 21, 5–30 (2006). https://doi.org/10.1016/j.acha.2006.04.006
Coifman, R., Shkolnisky, Y., Sigworth, F., Singer, A.: Graph Laplacian tomography from unknown random projections. IEEE Trans. Image Process. 17(10), 1891–1899 (2008). https://doi.org/10.1109/tip.2008.2002305
Constantin, P., Foias, C., Nicolaenko, B., Témam, R.: Integral Manifolds and Inertial Manifolds for Dissipative Partial Differential Equations. Springer, New York (1989). https://doi.org/10.1007/978-1-4612-3506-4
Constantin, P., Kiselev, A., Ryzhik, L., Zlatoš, A.: Diffusion and mixing in fluid flow. Ann. Math. 168, 643–674 (2008). https://www.jstor.org/stable/40345422
Das, S., Giannakis, D.: Koopman spectra in reproducing kernel Hilbert spaces (2018). https://arxiv.org/pdf/1801.07799.pdf
Dellnitz, M., Froyland, G., Sertl, S.: On the isolated spectrum of the Perron-Frobenius operator. Nonlinearity 13(4), 1171–1188 (2000). https://doi.org/10.1088/0951-7715/13/4/310
Dellnitz, M., Junge, O.: On the approximation of complicated dynamical behavior. SIAM J. Numer. Anal. 36, 491 (1999). https://doi.org/10.1137/S0036142996313002
Eisner, T., Farkas, B., Haase, M., Nagel, R.: Operator Theoretic Aspects of Ergodic Theory, Graduate Texts in Mathematics, vol. 272. Springer, Berlin (2015)
Fayad, B.: Analytic mixing reparametrizations of irrational flows. Ergod. Theory Dyn. Sys. 22, 437–468 (2002). https://doi.org/10.1017/s0143385702000214
Ferreira, J.C., Menegatto, V.A.: Eigenvalues of integral operators defined by smooth positive definite kernels. Integral Equations Operator Theory 64(1), 61–81 (2009). https://doi.org/10.1007/s00020-009-1680-3
Ferreira, J.C., Menegatto, V.A.: Eigenvalue decay rates for positive integral operators. Ann. Mat. Pura Appl. 192(6), 1–17 (2013). https://doi.org/10.1007/s10231-012-0256-z
Froyland, G., González-Tokman, C., Quas, A.: Detecting isolated spectrum of transfer and Koopman operators with Fourier analytic tools. J. Comput. Dyn. 1(2), 249–278 (2014). https://doi.org/10.3934/jcd.2014.1.249
Genton, M.C.: Classes of kernels for machine learning: a statistics perspective. J. Mach. Learn. Res. 2, 299–312 (2001)
Giannakis, D.: Dynamics-adapted cone kernels. SIAM J. Appl. Dyn. Sys. 14(2), 556–608 (2015). https://doi.org/10.1137/140954544
Giannakis, D.: Data-driven spectral decomposition and forecasting of ergodic dynamical systems. Appl. Comput. Harmon. Anal. (2017). https://doi.org/10.1016/j.acha.2017.09.001
Giannakis, D., Das, S.: Extraction and prediction of coherent patterns in incompressible flows through space-time Koopman analysis (2017). https://arxiv.org/pdf/1706.06450.pdf
Giannakis, D., Majda, A.J.: Time series reconstruction via machine learning: Revealing decadal variability and intermittency in the North Pacific sector of a coupled climate model. In: Conference on Intelligent Data Understanding 2011. Mountain View, California (2011)
Giannakis, D., Majda, A.J.: Nonlinear Laplacian spectral analysis for time series with intermittency and low-frequency variability. Proc. Natl. Acad. Sci. 109(7), 2222–2227 (2012). https://doi.org/10.1073/pnas.1118984109
Giannakis, D., Slawinska, J., Zhao, Z.: Spatiotemporal feature extraction with data-driven Koopman operators. J. Mach. Learn. Res. Proc. 44, 103–115 (2015)
Halmos, P.: Lectures on Ergodic Theory, vol. 142. American Mathematical Society, Providence (1956)
Holmes, P., Lumley, J.L., Berkooz, G.: Turbulence, Coherent Structures, Dynamical Systems and Symmetry. Cambridge University Press, Cambridge (1996)
Koopman, B.O.: Hamiltonian systems and transformation in Hilbert space. Proc. Natl. Acad. Sci. 17(5), 315–318 (1931). https://doi.org/10.1073/pnas.17.5.315
Korda, M., Mezić, I.: On convergence of extended dynamic mode decomposition to the Koopman operator. J. Nonlinear Sci. 28(2), 687–710 (2018). https://doi.org/10.1007/s00332-017-9423-0
Korda, M., Putinar, M., Mezić, I.: Data-Driven Spectral Analysis of the Koopman Operator. Appl. Comput. Harmon. Anal. (2018). https://doi.org/10.1016/j.acha.2018.08.002
Krengel, U.: Ergodic Theorems, vol. 6. Walter de Gruyter, Berlin (1985)
Law, K., Shukla, A., Stuart, A.M.: Analysis of the 3DVAR filter for the partially observed Lorenz’63 model. Discret. Contin. Dyn. Syst. 34(3), 1061–10,178 (2013). https://doi.org/10.3934/dcds.2014.34.1061
Lian, Z., Liu, P., Lu, K.: SRB measures for a class of partially hyperbolic attractors in Hilbert spaces. J. Differ. Equ. 261, 1532–1603 (2016). https://doi.org/10.1016/j.jde.2016.04.006
Lorenz, E.N.: Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130–141 (1963). https://doi.org/10.1175/1520-0469(1963)020%3c0130:DNF%3e2.0.CO;2
Lu, K., Wang, Q., Young, L.S.: Strange attractors for periodically forced parabolic equations. Mem. Am. Math. Soc. 224(1054), 1–85 (2013). https://doi.org/10.1090/S0065-9266-2012-00669-1
Luzzatto, S., Melbourne, I., Paccaut, F.: The Lorenz attractor is mixing. Commun. Math. Phys. 260(2), 393–401 (2005). https://doi.org/10.1007/s00220-005-1411-9
McGuinness, M.J.: The fractal dimension of the Lorenz attractor. Philos. Trans. R. Soc. Lond. Ser. A 262, 413–458 (1968). https://doi.org/10.1098/rsta.1968.0001
Mezić, I.: Spectral properties of dynamical systems, model reduction and decompositions. Nonlinear Dyn. 41, 309–325 (2005). https://doi.org/10.1007/s11071-005-2824-x
Mezić, I., Banaszuk, A.: Comparison of systems with complex behavior. Physica D 197, 101–133 (2004). https://doi.org/10.1016/j.physd.2004.06.015
Nadkarni, M.G.: The spectral theorem for unitary operators. Springer, Berlin (1998)
Packard, N.H., et al.: Geometry from a time series. Phys. Rev. Lett. 45, 712–716 (1980). https://doi.org/10.1103/physrevlett.45.712
Rowley, C.W., Mezić, I., Bagheri, S., Schlatter, P., Henningson, D.S.: Spectral analysis of nonlinear flows. J. Fluid Mech. 641, 115–127 (2009). https://doi.org/10.1017/s0022112009992059
Sauer, T., Yorke, J.A., Casdagli, M.: Embedology. J. Stat. Phys. 65(3–4), 579–616 (1991). https://doi.org/10.1007/bf01053745
Schmid, P.J.: Dynamic mode decomposition of numerical and experimental data. J. Fluid Mech. 656, 5–28 (2010). https://doi.org/10.1017/S0022112010001217
Schmid, P.J., Sesterhenn, J.L.: Dynamic mode decomposition of numerical and experimental data. In: Bulletin of American Physical Society (BAPS), 61st APS Meeting, p. 208. San Antonio (2008)
Scholkopf, B., Smola, A., Mu, K.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10, 1299–1319 (1998). https://doi.org/10.1162/089976698300017467
Slawinska, J., Giannakis, D.: Indo-Pacific variability on seasonal to multidecadal time scales. Part I: intrinsic SST modes in models and observations. J. Climate 30, 5265–5294 (2017). https://doi.org/10.1175/JCLI-D-16-0176.1
Stone, M.H.: On one-parameter unitary groups in Hilbert space. Ann. Math. 33, 643–648 (1932). https://doi.org/10.2307/1968538
Trillos, N., Slepčev, D.: A variational approach to the consistency of spectral clustering. Appl. Comput. Harmon. Anal. 45(2), 239–281 (2018). https://doi.org/10.1016/j.acha.2016.09.003
Tu, J.H., Rowley, C.W., Lucthenburg, C.M., Brunton, S.L., Kutz, J.N.: On dynamic mode decomposition: theory and applications. J. Comput. Dyn. 1(2), 391–421 (2014). https://doi.org/10.3934/jcd.2014.1.391
Tucker, W.: The Lorenz attractor exists. C. R. Acad. Sci. Paris Ser. I 328, 1197–1202 (1999)
Vautard, R., Ghil, M.: Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series. Physica D 35, 395–424 (1989). https://doi.org/10.1016/0167-2789(89)90077-8
von Luxburg, U., Belkin, M., Bousquet, O.: Consistency of spectral clustering. Ann. Stat. 26(2), 555–586 (2008). https://doi.org/10.1214/009053607000000640
Wang, C., Deser, C., Yu, J.Y., DiNezio, P., Clement, A.: El Niño and Southern Oscillation (ENSO): a review. In: P.W. Glynn, D.P. Manzello, I.C. Enoch (eds.) Coral Reefs of the Eastern Tropical Pacific: Persistence and Loss in a Dynamic Environment, Coral Reefs of the World, vol. 8, pp. 85–106. Springer Netherlands, Dordrecht (2017). https://doi.org/10.1007/978-94-017-7499-4_4
Williams, M.O., Kevrekidis, I.G., Rowley, C.W.: A data-driven approximation of the Koopman operator: extending dynamic mode decomposition. J. Nonlinear Sci. (2015). https://doi.org/10.1007/s00332-015-9258-5
Young, L.S.: What are SRB measures, and which dynamical systems have them? J. Stat. Phys. 108, 733–754 (2002). https://doi.org/10.1023/A:1019762724717
Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. Adv. Neural Inf. Process. Syst. 17, 1601–1608 (2004)
Acknowledgements
Dimitrios Giannakis received support from ONR YIP Grant N00014-16-1-2649, NSF Grant DMS-1521775, and DARPA Grant HR0011-16-C-0116. Suddhasattwa Das is supported as a postdoctoral research fellow from the first grant. The authors are grateful to L S Young for her suggestions.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Das, S., Giannakis, D. Delay-Coordinate Maps and the Spectra of Koopman Operators. J Stat Phys 175, 1107–1145 (2019). https://doi.org/10.1007/s10955-019-02272-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10955-019-02272-w
Keywords
- Koopman operators
- Delay-coordinate maps
- Point spectrum
- Koopman eigenfunctions
- Kernel methods
- Galerkin approximation