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Edward Ott

Researcher at University of Maryland, College Park

Publications -  676
Citations -  48167

Edward Ott is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Attractor & Chaotic. The author has an hindex of 101, co-authored 669 publications receiving 44649 citations. Previous affiliations of Edward Ott include Johns Hopkins University Applied Physics Laboratory & Eötvös Loránd University.

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Super persistent chaotic transients

TL;DR: The unstable-unstable pair bifurcation is an example of the crisis route to chaos as discussed by the authors, in which two unstable fixed points or periodic orbits of the same period coalesce and disappear as a system paremeter is raised.
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Lower hybrid wave scattering by density fluctuations

Edward Ott
- 01 Sep 1979 - 
TL;DR: In this article, a wave kinetic equation is formulated for lower hybrid scattering by low frequency density fluctuations (such as those recently observed in tokamaks) and its implications for heating of to kamak plasmas are discussed.
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Enhancing synchronism of chaotic systems

TL;DR: In this paper, the authors point out that exact synchronism may also occur for a large class of systems that are not replicas of part of the original system, and discuss the possibility of using this freedom to choose synchronizer systems with improved performance.
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Effect of short ray trajectories on the scattering statistics of wave chaotic systems

TL;DR: It is shown that the average impedance matrix, which also characterizes the system-specific properties, can be expressed in terms of classical trajectories that travel between ports and thus can be calculated semiclassically.
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Emergence of synchronization in complex networks of interacting dynamical systems

TL;DR: In this paper, the authors study the emergence of coherence in large complex networks of interacting heterogeneous dynamical systems and show that the critical coupling strength at which the systems undergo a transition from incoherent to coherent behavior is k c = (Z λ ) − 1, where Z depends only on the uncoupled dynamics of the individual systems on each node, while λ is the largest eigenvalue of the network adjacency matrix.