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Chaos in dynamical systems
- E. Ott
Preface 1. Introduction and overview 2. One-dimensional maps 3. Strange attractors and fractal dimensions 4. Dynamical properties of chaotic systems 5. Nonattracting chaotic sets 6. Quasiperiodicity…
- E. Ott
When reading the PDF, the author is very reliable in using the words to create sentences and the ways how the author creates the diction to influence many people will be seen.
Low dimensional behavior of large systems of globally coupled oscillators.
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…
A Local Ensemble Kalman Filter for Atmospheric Data Assimilation
A new, local formulation of the ensemble Kalman Filter approach for atmospheric data assimilation based on the hypothesis that, when the Earth's surface is divided up into local regions of moderate size, vectors of the forecast uncertainties in such regions tend to lie in a subspace of much lower dimension than that of the full atmospheric state vector of such a region.
Crises, sudden changes in chaotic attractors, and transient chaos
Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach.
- Jaideep Pathak, B. Hunt, M. Girvan, Zhixin Lu, E. Ott
- Computer SciencePhysical review letters
- 12 January 2018
We demonstrate the effectiveness of using machine learning for model-free prediction of spatiotemporally chaotic systems of arbitrarily large spatial extent and attractor dimension purely from…
Strange attractors that are not chaotic
Stretch, Twist, Fold: The Fast Dynamo
Introduction: the fast dynamo problem fast dynamo action in flows fast dynamo in maps. Methods and their applications: dynamos and non-dynamos magnetic structure in steady integrable flows upper…