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Clean numerical simulation for some chaotic systems using the parallel multiple-precision Taylor scheme

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  • Computational Physics
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Chinese Science Bulletin

Abstract

An improved parallel multiple-precision Taylor (PMT) scheme is developed to obtain clean numerical simulation (CNS) solutions of chaotic ordinary differential equations (ODEs). The new version program is about 500 times faster than the reported solvers developed in the MATHEMATICA, and also 2–3 times faster than the older version (PMT-1.0) of the scheme. This solver has the ability to yield longer solutions of Lorenz equations [up to 5000 TU (time unit)]. The PMT-1.1 scheme is applied to a selection of chaotic systems including the Chen, Rossler, coupled Lorenz and Lü systems. The T c -M and T c -K diagrams for these chaotic systems are presented and used to analyze the computation parameters for long-term solutions. The reliable computation times of these chaotic equations are obtained for single- and double-precision computation.

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References

  1. Hammel SM, Yorke JA, Grebogi C (1987) Do numerical orbits of chaotic dynamical processes represent true orbits? J Complex 3:136–145

    Article  Google Scholar 

  2. Nusse HE, Yorke JA (1988) Is every approximate trajectory of some process near an exact trajectory of a nearby process? Commun Math Phys 114:363–379

    Article  Google Scholar 

  3. Sauer T, Yorke JA (1991) Rigorous verification of trajectories for the computer simulation of dynamical systems. Nonlinearity 4:961

    Article  Google Scholar 

  4. Ansov D (1969) Geodesic flows on closed riemannian manifolds with negative curvature. In: Proceedings of the Steklov Institute of Mathematics

  5. Sauer T, Grebogi C, Yorke JA (1997) How long do numerical chaotic solutions remain valid? Phys Rev Lett 79:59–62

    Article  Google Scholar 

  6. Li JP, Zeng QC, Chou JF (2000) Computational uncertainty principle in nonlinear ordinary differential equations-I. Numerical results. Sci China Ser E Tech Sci 43:449–461

    Article  Google Scholar 

  7. Li JP, Zeng QC, Chou JF (2001) Computational uncertainty principle in nonlinear ordinary differential equations-II. Theoretical analysis. Sci China Ser E Tech Sci 44:55–74

    Article  Google Scholar 

  8. Lorenz EN (2006) Computational periodicity as observed in a simple system. Tellus A 58:549–557

    Article  Google Scholar 

  9. Teixeira J, Reynolds CA, Judd K (2007) Time step sensitivity of nonlinear atmospheric models: numerical convergence, truncation error growth, and ensemble design. J Atmos Sci 64:175–189

    Article  Google Scholar 

  10. Liao SJ (2009) On the reliability of computed chaotic solutions of non-linear differential equations. Tellus A 61:550–564

    Article  Google Scholar 

  11. Li J, Wang S (2008) Some mathematical and numerical issues in geophysical fluid dynamics and climate dynamics. Commun Comput Phys 3:759–793

    Google Scholar 

  12. Shi P (2008) A relation on round-off error, attractor size and its dynamics in driven or coupled logistic map system. Chaos 18:013122

    Article  Google Scholar 

  13. Brent RP (1978) A Fortran multiple-precision arithmetic package. ACM Trans Math Softw (TOMS) 4:57–70

    Article  Google Scholar 

  14. Oyanarte P (1990) MP-a multiple precision package. Comput Phys Commun 59:345–358

    Article  Google Scholar 

  15. Wang PF, Huang G, Wang ZZ (2006) Analysis and application of multiple-precision computation and round-off error for nonlinear dynamical systems. Adv Atmos Sci 23:758–766

    Article  Google Scholar 

  16. Moore RE (1966) Interval analysis. Prentice-Hall, Englewood Cliffs New Jersey

    Google Scholar 

  17. Moore RE (1979) Methods and applications of interval analysis. SIAM, Philadelphia

    Book  Google Scholar 

  18. Barrio R (2005) Performance of the Taylor series method for ODEs/DAEs. Appl Math Comput 163:525–545

    Article  Google Scholar 

  19. Liao SJ (2013) On the numerical simulation of propagation of micro-level inherent uncertainty for chaotic dynamic systems. Chaos Soliton Fract 47:1–12

    Article  Google Scholar 

  20. Liao S (2014) Physical limit of prediction for chaotic motion of three-body problem. Commun Nonlinear Sci Numer Simul 19:601–616

    Article  Google Scholar 

  21. Barrio R, Rodríguez M, Abad A et al (2011) Breaking the limits: the Taylor series method. Appl Math Comput 217:7940–7954

    Article  Google Scholar 

  22. Hénon M, Heiles C (1964) The applicability of the third integral of motion: some numerical experiments. Astron J 69:73–79

    Article  Google Scholar 

  23. Kehlet B, Logg A (2013) Quantifying the computability of the Lorenz system. arXiv:1306.2782

  24. Wang PF, Li JP, Li Q (2012) Computational uncertainty and the application of a high-performance multiple precision scheme to obtaining the correct reference solution of Lorenz equations. Numer Algorithms 59:147–159

    Article  Google Scholar 

  25. Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20:130–141

    Article  Google Scholar 

  26. Chen G, Ueta T (1999) Yet another chaotic attractor. Int J Bifurc Chaos 9:1465–1466

    Article  Google Scholar 

  27. Rossler OE (1976) An equation for continuous chaos. Phys Lett A 57:397–398

    Article  Google Scholar 

  28. Boffetta G, Giuliani P, Paladin G et al (1998) An extension of the lyapunov analysis for the predictability problem. J Atmos Sci 55:3409–3416

    Article  Google Scholar 

  29. Lü J, Chen G (2002) A new chaotic attractor coined. Int J Bifurc Chaos 12:659–661

    Article  Google Scholar 

Download references

Acknowledgement

The authors thank Prof. Shijun Liao for suggestions made during helpful discussions, especially in terms of improving the computation of the Taylor coefficients. This work was jointly supported by the National Basic Research Program of China (2011CB309704) and the National Natural Science Foundation of China (41375112).

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Correspondence to Pengfei Wang.

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Wang, P., Liu, Y. & Li, J. Clean numerical simulation for some chaotic systems using the parallel multiple-precision Taylor scheme. Chin. Sci. Bull. 59, 4465–4472 (2014). https://doi.org/10.1007/s11434-014-0412-5

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  • DOI: https://doi.org/10.1007/s11434-014-0412-5

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