Riddled parameter space in spatiotemporal chaotic dynamical systems

Ying Cheng Lai; Raimond L. Winslow

doi:10.1103/PhysRevLett.72.1640

Riddled parameter space in spatiotemporal chaotic dynamical systems

Ying Cheng Lai, Raimond L. Winslow

School of Medicine

Research output: Contribution to journal › Article › peer-review

51 Scopus citations

Abstract

We demonstrate that some chaotic parameter subsets of a class of spatiotemporal chaotic systems modeled by globally coupled maps are riddled. That is, for every point in the chaotic parameter subset, there are parameter values arbitrarily nearby that lead to nonchaotic attractors. A consequence is an extremely sensitive parameter dependence characterized by a significant probability of error in numerical computation of asymptotic attractors, regardless of the precision with which parameters are specified.

Original language	English (US)
Pages (from-to)	1640-1643
Number of pages	4
Journal	Physical Review Letters
Volume	72
Issue number	11
DOIs	https://doi.org/10.1103/PhysRevLett.72.1640
State	Published - 1994

ASJC Scopus subject areas

Physics and Astronomy(all)

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10.1103/PhysRevLett.72.1640

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title = "Riddled parameter space in spatiotemporal chaotic dynamical systems",

abstract = "We demonstrate that some chaotic parameter subsets of a class of spatiotemporal chaotic systems modeled by globally coupled maps are riddled. That is, for every point in the chaotic parameter subset, there are parameter values arbitrarily nearby that lead to nonchaotic attractors. A consequence is an extremely sensitive parameter dependence characterized by a significant probability of error in numerical computation of asymptotic attractors, regardless of the precision with which parameters are specified.",

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AB - We demonstrate that some chaotic parameter subsets of a class of spatiotemporal chaotic systems modeled by globally coupled maps are riddled. That is, for every point in the chaotic parameter subset, there are parameter values arbitrarily nearby that lead to nonchaotic attractors. A consequence is an extremely sensitive parameter dependence characterized by a significant probability of error in numerical computation of asymptotic attractors, regardless of the precision with which parameters are specified.

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Riddled parameter space in spatiotemporal chaotic dynamical systems

Abstract

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