Chaotic Dynamics in Discrete Time Systems

A special issue of Dynamics (ISSN 2673-8716).

Deadline for manuscript submissions: 31 August 2025 | Viewed by 5105

Special Issue Editors


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Guest Editor
Laboratory of Nonlinear Systems, Circuits & Complexity, Physics Department, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: chaos; control; observer design; cryptography
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Guest Editor
Department of Mathematics Applications and Methods for Artificial Intelligence, Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
Interests: chaos; cryptography
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Guest Editor
Department of Physics, University of Aberdeen, Aberdeen AB24 3FX, UK
Interests: nonlinear dynamics; chaos; chaos-based communication
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Special Issue Information

Dear Colleagues,

The study of chaos theory emerged from continuous dynamical systems, but it is also comprehensively documented in discrete time. Here, chaotic behavior can be present even in one-dimensional systems, in contrast to continuous time, where a minimum of three dimensions is required. Due to their ease of implementation and low computational cost, discrete chaotic systems, or chaotic maps as they are also termed, are often opted in applications where speed and computational load are of importance.

Nonetheless, apart from their adequacy in applications, discrete-time systems are also studied for their rich dynamical properties, found often to be universal to many nonlinear dynamical systems. Discrete-time chaotic systems can exhibit a wide variety of chaos-related phenomena, with regards to their transition to (and from) chaos, the number of equilibria and their stability, the existence of symmetric behavior, coexisting behaviors, and more. They also allow for rigorous demonstrations of universal fundamental phenomena in nonlinear dynamics.

This Special Issue aims to explore chaotic phenomena in discrete-time systems. Authors are welcome to submit their original and review works on discrete-time systems of any dimension which showcase interesting chaotic phenomena. Examples include:

  • Symmetric attractors;
  • Coexisting attractors;
  • Hidden attractors;
  • Antimonotonicity;
  • Crisis;
  • Bifurcations;
  • Decay of correlations;
  • Transient dynamics;
  • Networks and multilayer networks of chaotic maps;
  • Robust chaos;
  • Infinite number of equilibria;
  • Controllable number of equilibria;
  • Controllable statistical measures;
  • Techniques for constructing new maps;
  • Novel tools and measures for studying chaotic maps;
  • Digital implementations of the above;
  • Applications of chaotic maps and their transformed versions in optimization, encryption, communications, and more.

Dr. Lazaros Moysis
Dr. Marcin Lawnik
Dr. Murilo da Silva Baptista
Guest Editors

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Keywords

  • chaos
  • discrete time
  • map
  • bifurcation
  • attractors
  • applications
  • equilibria
  • lyapunov exponent
  • chaotification
  • symmetry
  • coexisting attractor

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Published Papers (2 papers)

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Research

16 pages, 11013 KiB  
Article
Exploring Transition from Stability to Chaos through Random Matrices
by Roberto da Silva and Sandra Denise Prado
Dynamics 2023, 3(4), 777-792; https://doi.org/10.3390/dynamics3040042 - 13 Nov 2023
Cited by 1 | Viewed by 1117
Abstract
This study explores the application of random matrices to track chaotic dynamics within the Chirikov standard map. Our findings highlight the potential of matrices exhibiting Wishart-like characteristics, combined with statistical insights from their eigenvalue density, as a promising avenue for chaos monitoring. Inspired [...] Read more.
This study explores the application of random matrices to track chaotic dynamics within the Chirikov standard map. Our findings highlight the potential of matrices exhibiting Wishart-like characteristics, combined with statistical insights from their eigenvalue density, as a promising avenue for chaos monitoring. Inspired by a technique originally designed for detecting phase transitions in spin systems, we successfully adapted and applied it to identify analogous transformative patterns in the context of the Chirikov standard map. Leveraging the precision previously demonstrated in localizing critical points within magnetic systems in our prior research, our method accurately pinpoints the Chirikov resonance overlap criterion for the chaos boundary at K2.43, reinforcing its effectiveness. Additionally, we verified our findings by employing a combined approach that incorporates Lyapunov exponents and bifurcation diagrams. Lastly, we demonstrate the adaptability of our technique to other maps, establishing its capability to capture the transition to chaos, as evidenced in the logistic map. Full article
(This article belongs to the Special Issue Chaotic Dynamics in Discrete Time Systems)
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14 pages, 11563 KiB  
Article
Unveiling Dynamical Symmetries in 2D Chaotic Iterative Maps with Ordinal-Patterns-Based Complexity Quantifiers
by Benjamin S. Novak and Andrés Aragoneses
Dynamics 2023, 3(4), 750-763; https://doi.org/10.3390/dynamics3040040 - 9 Nov 2023
Cited by 1 | Viewed by 2515
Abstract
Effectively identifying and characterizing the various dynamics present in complex and chaotic systems is fundamental for chaos control, chaos classification, and behavior-transition forecasting, among others. It is a complicated task that becomes increasingly difficult as systems involve more dimensions and parameters. Here, we [...] Read more.
Effectively identifying and characterizing the various dynamics present in complex and chaotic systems is fundamental for chaos control, chaos classification, and behavior-transition forecasting, among others. It is a complicated task that becomes increasingly difficult as systems involve more dimensions and parameters. Here, we extend methods inspired in ordinal patterns to analyze 2D iterative maps to unveil underlying approximate symmetries of their dynamics. We distinguish different families of chaos within the systems, find similarities among chaotic maps, identify approximate temporal and dynamical symmetries, and anticipate sharp transitions in dynamics. We show how this methodology displays the evolution of the spatial correlations in a dynamical system as the control parameter varies. We prove the power of these techniques, which involve simple quantifiers as well as combinations of them, in extracting relevant information from the complex dynamics of 2D systems, where other techniques are less informative or more computationally demanding. Full article
(This article belongs to the Special Issue Chaotic Dynamics in Discrete Time Systems)
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