Analysis and Data-Based Reconstruction of Complex Nonlinear Dynamical Systems

Using the Methods of Stochastic Processes

  • M. Reza Rahimi Tabar

Part of the Understanding Complex Systems book series (UCS)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. M. Reza Rahimi Tabar
    Pages 1-8
  3. M. Reza Rahimi Tabar
    Pages 9-18
  4. M. Reza Rahimi Tabar
    Pages 31-37
  5. M. Reza Rahimi Tabar
    Pages 39-48
  6. M. Reza Rahimi Tabar
    Pages 69-78
  7. M. Reza Rahimi Tabar
    Pages 79-86
  8. M. Reza Rahimi Tabar
    Pages 111-121
  9. M. Reza Rahimi Tabar
    Pages 123-128
  10. M. Reza Rahimi Tabar
    Pages 243-260
  11. M. Reza Rahimi Tabar
    Pages 261-271
  12. Back Matter
    Pages 273-280

About this book


This book focuses on a central question in the field of complex systems: Given a fluctuating (in time or space), uni- or multi-variant sequentially measured set of experimental data (even noisy data), how should one analyse non-parametrically the data, assess underlying trends, uncover characteristics of the fluctuations (including diffusion and jump contributions), and construct a stochastic evolution equation?

Here, the term "non-parametrically" exemplifies that all the functions and parameters of the constructed stochastic evolution equation can be determined directly from the measured data.

The book provides an overview of methods that have been developed for the analysis of fluctuating time series and of spatially disordered structures.  Thanks to its feasibility and simplicity, it has been successfully applied to fluctuating time series and spatially disordered structures of complex systems studied in scientific fields such as physics, astrophysics, meteorology, earth science, engineering, finance, medicine and the neurosciences, and has led to a number of important results.

The book also includes the numerical and analytical approaches to the analyses of complex time series that are most common in the physical and natural sciences. Further, it is self-contained and readily accessible to students, scientists, and researchers who are familiar with traditional methods of mathematics, such as ordinary, and partial differential equations.

The codes for analysing continuous time series are available in an R package developed by the research group Turbulence, Wind energy and Stochastic (TWiSt) at the Carl von Ossietzky University of Oldenburg under the supervision of Prof. Dr. Joachim Peinke. This package makes it possible to extract the (stochastic) evolution equation underlying a set of data or measurements.


Time Series Analysis Langevin Dynamics Jump-Diffusion Dynamics From Time Series to Dynamical Equation Modeling epileptic Brain Dynamics Dynamics of Optically Trapped Particles Jumpy Stochastic Behavior Diffusive Stochastic Behavior Jump-Diffusion Processes Discontinuous Stochastic Processes Modeling complex dynamical systems Physics of stochastic processes

Authors and affiliations

  • M. Reza Rahimi Tabar
    • 1
  1. 1.Department of PhysicsSharif University of TechnologyTehranIran

Bibliographic information

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