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Preface to the Focus Issue: Chaos Detection Methods and Predictability
1. C. G. Antonopoulos, T. Bountis, C. Skokos, and L. Drossos, “ Complex statistics and diffusion in nonlinear disordered particle chains,” Chaos 24, 024405 (2014).
3. T. Bellsky, E. J. Kostelich, and A. Mahalov, “ Kalman filter data assimilation: Targeting observations and parameter estimation,” Chaos 24, 024406 (2014).
4. A. K. Charakopoulos, T. E. Karakasidis, P. N. Papanicolaou, and A. Liakopoulos, “ The application of complex network time series analysis in turbulent heated jets,” Chaos 24, 024408 (2014).
5. G. A. Gottwald and I. Melbourne, “ A test for a conjecture on the nature of attractors for smooth dynamical systems,” Chaos 24, 024403 (2014).
6. N. Kyriakopoulos, V. Koukouloyannis, C. Skokos, and P. Kevrekidis, “ Chaotic behavior of three interacting vortices in a confined Bose-Einstein condensate,” Chaos 24, 024410 (2014).
7. N. Lange, M. Richter, F. Onken, A. Bäcker, and R. Ketzmerick, “ Global structure of regular tori in a generic 4D symplectic map,” Chaos 24, 024409 (2014).
9. Y. Papaphilippou, “ Chaos detection methods in particle accelerators: From theory and simulations to experiments,” Chaos 24, 024412 (2014).
10. U. Parlitz, J. Schumann-Bischoff, and S. Luther, “ Local observability of state variables and parameters in nonlinear modeling quantified by delay embedding,” Chaos 24, 024411 (2014).
11. A. G. Ravelo-García, P. Saavedra-Santana, G. Juliá-Serdá, J. L. Navarro-Mesa, J. Navarro-Esteva, X. Álvarez López, A. Gapelyuk, T. Penzel, and N. Wessel, “ Symbolic dynamics marker of heart rate variability combined with clinical variables enhance obstructive sleep apnea screening,” Chaos 24, 024404 (2014).
12. X. Sun, M. Small, Y. Zhao, and X. Xue, “ Characterizing system dynamics with a weighted and directed network constructed from time series data,” Chaos 24, 024402 (2014).
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This Focus Issue presents a collection of papers originating from the workshop Methods of Chaos Detection and Predictability: Theory and Applications held at the Max Planck Institute for the Physics of Complex Systems in Dresden, June 17–21, 2013. The main aim of this interdisciplinary workshop was to review comprehensively the theory and numerical implementation of the existing methods of chaos detection and predictability, as well as to report recent applications of these techniques to different scientific fields. The collection of twelve papers in this Focus Issue represents the wide range of applications, spanning mathematics, physics, astronomy, particle accelerator physics, meteorology and medical research. This Preface surveys the papers of this Issue.
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