Skip to main content

Abstract

A preliminary version of a data driven reduced order model (ROM) for dynamical systems is presented in this Chapter. This ROM synergically and adaptively combines a black-box full model (FM) of the system and extrapolate conveniently using a recent extension of standard dynamic mode decomposition called higher order dynamic mode decomposition (HODMD). These two are applied in interspersed time intervals, called the FM-intervals and the HODMD-intervals, respectively. The data for the each HODMD-interval is obtained from the application of the FM in the previous FM-interval. The main question is when extrapolation from HODMD is no longer valid and switching to a new FM-interval is necessary. This is made attending to two criteria, ensuring that an estimate of the extrapolation error and a measure of consistency are both conveniently small. In this sense, the present method is similar to a previous method called POD on the Fly, which was not a purely data driven method. Instead, POD on the Fly was based on a Galerkin projection of the governing equations that thus should be known. The new method presented in this paper is illustrated with several transient dynamics for the complex Ginzburg-Landau equation that converges to either periodic or quasi-periodic attractors. The resulting CPU accelerations factors (compared to the full model) are quite large.

Supported by the Spanish Ministry of Economy and Competitiveness, under Grant TRA-2016-75075-R.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lovgren, A.E., Maday, Y., Ronquist, E.M.: A reduced basis element method for complex flow systems. In: Wesseling, P., Oñate, E., Périaux, J. (eds.) European Conference on Computational Fluid Dynamics, ECCOMAS CFD 2006, pp. 1–17. TU Delft, The Netherlands (2006)

    Google Scholar 

  2. Chinesta, F., Keunings, R., Leygue, A.: The Proper Generalized Decomposition for Advanced Numerical Simulations. SpringerBriefs in Applied Sciences and Technology. Springer, Berlin (2014)

    Book  Google Scholar 

  3. Rapún, M.-L., Terragni, F., Vega, J.M.: Adaptive POD-based low-dimensional modeling supported by residual estimates. Int. J. Numer. Method Eng. 9, 844–868 (2015)

    Article  MathSciNet  Google Scholar 

  4. Le Clainche, S., Vega, J.M.: Higher order dynamic mode decomposition. SIAM J. Appl. Dyn. Syst. 16(2), 882–925 (2017)

    Article  MathSciNet  Google Scholar 

  5. Schmid, P.J.: Dynamic mode decomposition of numerical and experimental data. J. Fluid Mech. 656, 5–28 (2010)

    Article  MathSciNet  Google Scholar 

  6. Le Clainche, S., Vega, J.M.: Analyzing nonlinear dynamics via data-driven dynamic mode decomposition-like methods. Complexity 2018, 6920783 (2018)

    Article  Google Scholar 

  7. https://github.com/LeClaincheVega/HODMD

  8. Takens, F.: Detecting strange attractors in turbulence. In: Rand, D.A., Young, L.-S. (eds.) Lecture Notes in Mathematics, pp. 366–381. Springer (1981)

    Google Scholar 

  9. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C. Cambridge University Press, Cambridge (1988)

    MATH  Google Scholar 

  10. Le Clainche, S., Vega, J.M.: Higher order dynamic mode decomposition to identify and extrapolate flow patterns. Phys. Fluids 29, 084102 (2017)

    Article  Google Scholar 

  11. Aranson, I.S., Kramer, L.: The world of the complex Ginzburg-Landau equation. Rev. Mod. Phys. 74, 100–142 (2002)

    Article  MathSciNet  Google Scholar 

  12. Haragus, M., Iooss, G.: Local Bifurcations, Center Manifolds, and Normal Forms in Infinite Dimensional Dynamical Systems. Springer, London (2010)

    MATH  Google Scholar 

  13. Terragni, F., Vega, J.M.: Construction of bifurcation diagrams using POD on the fly. SIAM J. Appl. Dyn. Syst. 13, 339–365 (2014)

    Article  MathSciNet  Google Scholar 

  14. Sanchez Umbria, J., Net, M., Vega, J.M.: Analyzing multidimensional dynamics via spatio-temporal Koopman decomposition. Preprint (2019)

    Google Scholar 

  15. Le Clainche, S., Vega, J.M.: Spatio-temporal Koopman decomposition. J. Nonliner Sci. 28, 1793–1842 (2018)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This research has been supported by the Spanish Ministry of Economy and Competitiveness, under grant TRA2016-75075-R.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to José M. Vega .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Beltrán, V., Clainche, S.L., Vega, J.M. (2020). A Data-Driven ROM Based on HODMD. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham. https://doi.org/10.1007/978-3-030-20055-8_54

Download citation

Publish with us

Policies and ethics