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Predictability of chaotic dynamics : a finite-time Lyapunov exponents approach

Author: Juan Camilo Vallejo; Miguel A F Sanjuán
Publisher: Cham : Springer, [2019] ©2019
Series: Springer complexity.; Springer series in synergetics (Unnumbered)
Edition/Format:   eBook : Document : English : Second editionView all editions and formats
Summary:
This book is primarily concerned with the computational aspects of predictability of dynamical systems - in particular those where observations, modeling and computation are strongly interdependent. Unlike with physical systems under control in laboratories, in astronomy it is uncommon to have the possibility of altering the key parameters of the studied objects. Therefore, the numerical simulations offer an  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Vallejo, Juan C.
Predictability of Chaotic Dynamics : A Finite-Time Lyapunov Exponents Approach
Cham : Springer,c2019
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Juan Camilo Vallejo; Miguel A F Sanjuán
ISBN: 9783030286309 3030286304
OCLC Number: 1125944616
Description: 1 online resource : illustrations (some color).
Contents: Preface --
Forecasting and chaos --
Lyapunov exponents --
Dynamical regimes and timescales --
Predictability --
Chaos, predictability and astronomy --
A detailed example: galactic dynamics --
Appendix.
Series Title: Springer complexity.; Springer series in synergetics (Unnumbered)
Responsibility: Juan C. Vallejo, Miguel A. F. Sanjuan.

Abstract:

This book is primarily concerned with the computational aspects of predictability of dynamical systems - in particular those where observations, modeling and computation are strongly interdependent. Unlike with physical systems under control in laboratories, in astronomy it is uncommon to have the possibility of altering the key parameters of the studied objects. Therefore, the numerical simulations offer an essential tool for analysing these systems, and their reliability is of ever-increasing interest and importance. In this interdisciplinary scenario, the underlying physics provide the simulated models, nonlinear dynamics provides their chaoticity and instability properties, and the computer sciences provide the actual numerical implementation. This book introduces and explores precisely this link between the models and their predictability characterization based on concepts derived from the field of nonlinear dynamics, with a focus on the strong sensitivity to initial conditions and the use of Lyapunov exponents to characterize this sensitivity. This method is illustrated using several well-known continuous dynamical systems, such as the Contopoulos, Hénon-Heiles and Rössler systems. This second edition revises and significantly enlarges the material of the first edition by providing new entry points for discussing new predictability issues on a variety of areas such as machine decision-making, partial differential equations or the analysis of attractors and basins. Finally, the parts of the book devoted to the application of these ideas to astronomy have been greatly enlarged, by first presenting some basics aspects of predictability in astronomy and then by expanding these ideas to a detailed analysis of a galactic potential.

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