@article{3cbfc9d582454fedb0b1e1572e12aaad,
title = "Recent achievements in nonlinear dynamics, synchronization, and networks",
abstract = "This Focus Issue covers recent developments in the broad areas of nonlinear dynamics, synchronization, and emergent behavior in dynamical networks. It targets current progress on issues such as time series analysis and data-driven modeling from real data such as climate, brain, and social dynamics. Predicting and detecting early warning signals of extreme climate conditions, epileptic seizures, or other catastrophic conditions are the primary tasks from real or experimental data. Exploring machine-based learning from real data for the purpose of modeling and prediction is an emerging area. Application of the evolutionary game theory in biological systems (eco-evolutionary game theory) is a developing direction for future research for the purpose of understanding the interactions between species. Recent progress of research on bifurcations, time series analysis, control, and time-delay systems is also discussed.",
author = "Dibakar Ghosh and Norbert Marwan and Michael Small and Changsong Zhou and Jobst Heitzig and Aneta Koseska and Peng Ji and Kiss, {Istvan Z.}",
note = "Funding Information: We extend our sincere appreciation to the contributions of all the authors who submitted their interesting works to this Special Issue on “Nonlinear dynamics, synchronization and networks: Dedicated to J{\"u}rgen Kurths{\textquoteright} 70 birthday for Chaos (NDSNJK23).” We are very much thankful to all the reviewers who helped us to ensure the quality of the selected papers. We acknowledge the NDA23 conference during March 15–17, 2023 and funding from the DFG for the conference [DFG Project No. MA 4759/19: International scientific conference: “Nonlinear Data Analysis and Modeling: Advances, Applications, Perspectives (NDA23),” Potsdam]. Finally, we express our gratitude to the editorial office for their timely guidance and consistent support throughout the publication process. Publisher Copyright: {\textcopyright} 2024 Author(s).",
year = "2024",
month = oct,
day = "1",
doi = "10.1063/5.0236801",
language = "English",
volume = "34",
journal = "Chaos",
issn = "1054-1500",
publisher = "American Institute of Physics",
number = "10",
}
