Computability and Computational Complexity of the Evolution of Nonlinear Dynamical Systems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7921)


Nonlinear dynamical systems abound as models of natural phenomena. They are often characterized by highly unpredictable behaviour which is hard to analyze as it occurs, for example, in chaotic systems. A basic problem is to understand what kind of information we can realistically expect to extract from those systems, especially information concerning their long-term evolution. Here we review a few recent results which look at this problem from a computational perspective.


Computational Complexity Periodic Orbit Turing Machine Nonlinear Dynamical System Lipschitz Constant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.LIXEcole PolytechniquePalaiseau CedexFrance
  2. 2.CEDMES/FCTUniversidade do AlgarveFaroPortugal
  3. 3.SQIG/Instituto de TelecomunicaçõesLisbonPortugal
  4. 4.DMSUniversity of CincinnatiCincinnatiU.S.A.

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