E
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.
Papers
More filters
Journal ArticleDOI
Predicting Chaos Most of the Time from Embeddings with Self-Intersections
TL;DR: In this paper, the authors consider embeddings with self-intersection and show that reliable prediction is still possible from most orbit points when the dimension M of the measurement space exceeds the information dimension D{sub 1} of the attractor.
Journal ArticleDOI
Observing chaos: deducing and tracking the state of a chaotic system from limited observation
TL;DR: A method is proposed whereby the full state vector of a chaotic system can be reconstructed and tracked using only the time series of a single observed scalar function of the system state.
Journal ArticleDOI
Statistics of wave-function scars.
Thomas M. Antonsen,Thomas M. Antonsen,Edward Ott,Edward Ott,Q. Chen,Q. Chen,Robert N. Oerter,Robert N. Oerter +7 more
TL;DR: In this article, the authors studied the properties of scars on eigenfunctions of a two-dimensional, classically chaotic billiard and showed that the tendency for a scar to form is controlled by both the stability of the periodic orbit and the statistical fluctuations in the time for wave density to return to the unstable orbit once having left.
Journal ArticleDOI
Nonlinear landau damping and beat wave trapping.
Edward Ott,Christian T. Dum +1 more
TL;DR: In this article, the nonlinear interaction of two coherent waves with particles of velocity near (ω 1−ω 2) and (k 1−k2) was investigated. But the results were only applicable to the case of (1 − ε)-coherence.
Journal ArticleDOI
Using synchronism of chaos for adaptive learning of time-evolving network topology.
TL;DR: In this paper, an adaptive strategy that, based on a potential that the network systems seek to minimize in order to maintain synchronization, can be successfully applied to identify the time evolution of the network from limited information, taking advantage of the properties of synchronism of chaos and the presence of different communication delays over the network links.