Skip to main content
Log in

Delay-Coordinate Maps and the Spectra of Koopman Operators

  • Published:
Journal of Statistical Physics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Anosov, D.V., Katok, A.B.: New examples in smooth ergodic theory: ergodic diffeomorphisms. Trans. Mosc. Math. Soc. 23, 1–35 (1970)

    MathSciNet  MATH  Google Scholar 

  2. Ahues, M., Largillier, A., Limaye, B.: Spectral computations for bounded operators. Chapman and Hall/CRC, Boca Raton (2001)

    Book  MATH  Google Scholar 

  3. 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

    Article  MathSciNet  MATH  Google Scholar 

  4. 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

    Article  MathSciNet  MATH  ADS  Google Scholar 

  5. Babuška, I., Osborn, J.: Eigenvalue Problems, Handbook of Numerical Analysis, vol. 2. North Holland, Amsterdam (1991)

    MATH  Google Scholar 

  6. Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15, 1373–1396 (2003). https://doi.org/10.1162/089976603321780317

    Article  MATH  Google Scholar 

  7. 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

  8. Berry, T., Harlim, J.: Variable bandwidth diffusion kernels. Appl. Comput. Harmon. Anal. (2015). https://doi.org/10.1016/j.acha.2015.01.001

    Article  MATH  Google Scholar 

  9. Berry, T., Sauer, T.: Consistent manifold representation for topological data analysis (2016). https://arxiv.org/pdf/1606.02353.pdf

  10. 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

    Article  MathSciNet  MATH  Google Scholar 

  11. 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

    Article  MathSciNet  MATH  Google Scholar 

  12. 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

    Article  Google Scholar 

  13. 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

    Article  MathSciNet  MATH  Google Scholar 

  14. 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

  15. Budisić, M., Mohr, R., Mezić, I.: Applied Koopmanism. Chaos 22, 047,510 (2012). https://doi.org/10.1063/1.4772195

    Article  MathSciNet  MATH  Google Scholar 

  16. Coifman, R., Lafon, S.: Diffusion maps. Appl. Comput. Harmon. Anal. 21, 5–30 (2006). https://doi.org/10.1016/j.acha.2006.04.006

    Article  MathSciNet  MATH  Google Scholar 

  17. 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

    Article  MathSciNet  MATH  ADS  Google Scholar 

  18. 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

  19. 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

  20. Das, S., Giannakis, D.: Koopman spectra in reproducing kernel Hilbert spaces (2018). https://arxiv.org/pdf/1801.07799.pdf

  21. 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

    Article  MathSciNet  MATH  ADS  Google Scholar 

  22. Dellnitz, M., Junge, O.: On the approximation of complicated dynamical behavior. SIAM J. Numer. Anal. 36, 491 (1999). https://doi.org/10.1137/S0036142996313002

    Article  MathSciNet  MATH  Google Scholar 

  23. Eisner, T., Farkas, B., Haase, M., Nagel, R.: Operator Theoretic Aspects of Ergodic Theory, Graduate Texts in Mathematics, vol. 272. Springer, Berlin (2015)

    Book  MATH  Google Scholar 

  24. Fayad, B.: Analytic mixing reparametrizations of irrational flows. Ergod. Theory Dyn. Sys. 22, 437–468 (2002). https://doi.org/10.1017/s0143385702000214

    Article  MathSciNet  MATH  Google Scholar 

  25. 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

    Article  MathSciNet  MATH  Google Scholar 

  26. 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

    Article  MathSciNet  MATH  Google Scholar 

  27. 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

    Article  MathSciNet  MATH  Google Scholar 

  28. Genton, M.C.: Classes of kernels for machine learning: a statistics perspective. J. Mach. Learn. Res. 2, 299–312 (2001)

    MathSciNet  MATH  Google Scholar 

  29. Giannakis, D.: Dynamics-adapted cone kernels. SIAM J. Appl. Dyn. Sys. 14(2), 556–608 (2015). https://doi.org/10.1137/140954544

    Article  MathSciNet  MATH  Google Scholar 

  30. 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

  31. 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

  32. 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)

  33. 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

    Article  MathSciNet  MATH  ADS  Google Scholar 

  34. Giannakis, D., Slawinska, J., Zhao, Z.: Spatiotemporal feature extraction with data-driven Koopman operators. J. Mach. Learn. Res. Proc. 44, 103–115 (2015)

    Google Scholar 

  35. Halmos, P.: Lectures on Ergodic Theory, vol. 142. American Mathematical Society, Providence (1956)

    MATH  Google Scholar 

  36. Holmes, P., Lumley, J.L., Berkooz, G.: Turbulence, Coherent Structures, Dynamical Systems and Symmetry. Cambridge University Press, Cambridge (1996)

    Book  MATH  Google Scholar 

  37. 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

    Article  MATH  ADS  Google Scholar 

  38. 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

    Article  MathSciNet  MATH  ADS  Google Scholar 

  39. 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

    Article  MATH  Google Scholar 

  40. Krengel, U.: Ergodic Theorems, vol. 6. Walter de Gruyter, Berlin (1985)

    Book  MATH  Google Scholar 

  41. 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

  42. 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

    Article  MathSciNet  MATH  ADS  Google Scholar 

  43. 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

    Article  MATH  ADS  Google Scholar 

  44. 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

    Article  MathSciNet  MATH  Google Scholar 

  45. 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

    Article  MathSciNet  MATH  ADS  Google Scholar 

  46. 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

    Article  Google Scholar 

  47. 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

    Article  MathSciNet  MATH  Google Scholar 

  48. 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

    Article  MathSciNet  MATH  ADS  Google Scholar 

  49. Nadkarni, M.G.: The spectral theorem for unitary operators. Springer, Berlin (1998)

    Google Scholar 

  50. 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

    Article  ADS  Google Scholar 

  51. 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

    Article  MathSciNet  MATH  ADS  Google Scholar 

  52. Sauer, T., Yorke, J.A., Casdagli, M.: Embedology. J. Stat. Phys. 65(3–4), 579–616 (1991). https://doi.org/10.1007/bf01053745

    Article  MathSciNet  MATH  ADS  Google Scholar 

  53. Schmid, P.J.: Dynamic mode decomposition of numerical and experimental data. J. Fluid Mech. 656, 5–28 (2010). https://doi.org/10.1017/S0022112010001217

    Article  MathSciNet  MATH  ADS  Google Scholar 

  54. 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)

  55. 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

    Article  Google Scholar 

  56. 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

    Article  ADS  Google Scholar 

  57. Stone, M.H.: On one-parameter unitary groups in Hilbert space. Ann. Math. 33, 643–648 (1932). https://doi.org/10.2307/1968538

    Article  MathSciNet  MATH  Google Scholar 

  58. 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

    Article  MathSciNet  MATH  Google Scholar 

  59. 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

    Article  MathSciNet  MATH  Google Scholar 

  60. Tucker, W.: The Lorenz attractor exists. C. R. Acad. Sci. Paris Ser. I 328, 1197–1202 (1999)

    Article  MathSciNet  MATH  ADS  Google Scholar 

  61. 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

    Article  MathSciNet  MATH  ADS  Google Scholar 

  62. von Luxburg, U., Belkin, M., Bousquet, O.: Consistency of spectral clustering. Ann. Stat. 26(2), 555–586 (2008). https://doi.org/10.1214/009053607000000640

    Article  MathSciNet  MATH  Google Scholar 

  63. 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

  64. 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

    Article  MathSciNet  MATH  Google Scholar 

  65. 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

    Article  MathSciNet  MATH  Google Scholar 

  66. Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. Adv. Neural Inf. Process. Syst. 17, 1601–1608 (2004)

    Google Scholar 

Download references

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

Authors

Corresponding author

Correspondence to Suddhasattwa Das.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10955-019-02272-w

Keywords

Mathematics Subject Classification

Navigation