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Chaos: An Interdisciplinary Journal of Nonlinear Science is a peer-reviewed journal published by AIP Publishing, covering Nonlinear Dynamics and Pattern Formation, Chaos control and synchronization, stochastic dynamics and bifurcation. Over its lifetime it has published 9,056 papers, which have received 211,973 citations.
| Publisher | AIP Publishing |
|---|---|
| Reference style | Numbered (AIP) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, Chaos: An Interdisciplinary Journal of Nonlinear Science 12, 45 (2023).
Formats any DOI in Chaos: An Interdisciplinary Journal of Nonlinear Science style. No sign-up. |
| Publishes research in | Nonlinear Dynamics and Pattern Formation Chaos control and synchronization stochastic dynamics and bifurcation Quantum chaos and dynamical systems Neural dynamics and brain function |
| ISSN | 1054-1500 |
| Citation impact (2-yr) | 3.52 |
| h-index | 154 |
| i10-index | 4,606 |
| Top institutions publishing here | Potsdam Institute for Climate Impact Research |
| Journal website | chaos.aip.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
The healthy heartbeat is traditionally thought to be regulated according to the classical principle of homeostasis whereby physiologic systems operate to reduce variability and achieve an equilibrium-like state [Physiol. Rev. 9, 399-431 (1929)]. However, recent studies [Phys. Rev. Lett. 70, 1343-1346 (1993); Fractals in Biology and Medicine (Birkhauser-Verlag, Basel, 1994), pp. 55-65] reveal that under…
We describe the implementation of methods of nonlinear time series analysis which are based on the paradigm of deterministic chaos. A variety of algorithms for data representation, prediction, noise reduction, dimension and Lyapunov estimation, and nonlinearity testing are discussed with particular emphasis on issues of implementation and choice of parameters. Computer programs that implement the…
Approximate entropy (ApEn) is a recently developed statistic quantifying regularity and complexity, which appears to have potential application to a wide variety of relatively short (greater than 100 points) and noisy time-series data. The development of ApEn was motivated by data length constraints commonly encountered, e.g., in heart rate, EEG, and endocrine hormone secretion data…
It is shown that, in the infinite size limit, certain systems of globally coupled phase oscillators display low dimensional dynamics. In particular, we derive an explicit finite set of nonlinear ordinary differential equations for the macroscopic evolution of the systems considered. For example, an exact, closed form solution for the nonlinear time evolution of the…
