Article in Journal ART-2006-03

BibliographyAvrutin, V.; Levi, P.; Schanz, M.; Fundinger, D.; Osipenko, G.: Investigation of Dynamical Systems Using Symbolic Images: Efficient Implementation and Applications.
In: International Journal of Bifurcation and Chaos. Vol. 16(12).
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology.
pp. 3451-3496, english.
New Jersey, London, Singapore, Hong Kong: World Scientific Publishing Company, December 2006.
DOI: 10.1142/S0218127406016938.
Article in Journal.
CR-SchemaG.1.0 (Numerical Analysis General)
G.1.5 (Roots of Nonlinear Equations)
Keywordssymbolic image, invariant set, basin of attraction, stable manifold, unstable manifold
Abstract

Symbolic images represent a unified framework to apply several methods for the investigation of dynamical systems both discrete and continuous in time. By transforming the system flow into a graph, they allow it to formulate investigation methods as graph algorithms. Several kinds of stable and unstable return trajectories can be localized on this graph as well as attractors, their basins and connecting orbits. Extensions of the framework allow, e.g. the calculation of the Morse spectrum and verification of hyperbolicity. In this work, efficient algorithms and adequate data structures will be presented for the construction of symbolic images and some basic operations on them, like the localization of the chain recurrent set and periodic orbits. The performance of these algorithms will be analyzed and we show their application in practice. The focus is not only put on several standard systems, like Lorenz and Ikeda, but also on some less well-known ones. Additionally, some tuning techniques are presented for an efficient usage of the method.

ContactMichael.Schanz@informatik.uni-stuttgart.de Viktor.Avrutin@informatik.uni-stuttgart.de
Department(s)University of Stuttgart, Institute of Parallel and Distributed Systems, Image Understanding
Project(s)AnT
Entry dateMarch 14, 2006
   Publ. Institute   Publ. Computer Science