Attractor Dimension Estimates for Dynamical Systems: Theory and Computation: Dedicated to Gennady Leonov...

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Nikolay Kuznetsov Volker Reitmann

Attractor Dimension Estimates for Dynamical Systems

Theory and Computation: Dedicated to Gennady Leonov

Nikolay Kuznetsov, Volker Reitmann

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English

Springer Nature Switzerland AG
03 July 2021

Non-linear science; Dynamics & statics; Statistical physics; Mathematical theory of computation

Series: Emergence, Complexity and Computation

This book provides analytical and numerical methods for the estimation of dimension characteristics (Hausdorff, Fractal, Carathéodory dimensions) for attractors and invariant sets of dynamical systems and cocycles generated by smooth differential equations or maps in finite-dimensional Euclidean spaces or on manifolds. It also discusses stability investigations using estimates based on Lyapunov functions and adapted metrics. Moreover, it introduces various types of Lyapunov dimensions of dynamical systems with respect to an invariant set, based on local, global and uniform Lyapunov exponents, and derives analytical formulas for the Lyapunov dimension of the attractors of the Hénon and Lorenz systems. Lastly, the book presents estimates of the topological entropy for general dynamical systems in metric spaces and estimates of the topological dimension for orbit closures of almost periodic solutions to differential equations.

By: Nikolay Kuznetsov, Volker Reitmann
Imprint: Springer Nature Switzerland AG
Country of Publication: Switzerland
Edition: 2021 ed.
Volume: 38
Dimensions: Height: 235mm, Width: 155mm,
Weight: 860g
ISBN: 9783030509897
ISBN 10: 3030509893
Series: Emergence, Complexity and Computation
Pages: 545
Publication Date: 03 July 2021
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Reviews for Attractor Dimension Estimates for Dynamical Systems: Theory and Computation: Dedicated to Gennady Leonov

It is interesting and very well written. Mostly, chapters are self-contained and rich of detailed explanations. Many powerful computational tools and algorithms provide a solid numerical background for the study of attractor dimensions. ... this book contains advanced material on attractor dimension estimates for dynamical systems. This is definitely suitable for researchers in applied mathematics and computational theory of dynamical systems. (Mohammad Sajid, zbMATH 1483.37001, 2022)