English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
 PreviousNext  
  Traveling phase waves in asymmetric networks of noisy chaotic attractors

Peron, T. K. D., Kurths, J., Rodrigues, F. A., Schimansky-Geier, L., Sonnenschein, B. (2016): Traveling phase waves in asymmetric networks of noisy chaotic attractors. - Physical Review E, 94, 042210.
https://doi.org/10.1103/PhysRevE.94.042210

Item is

Files

show Files
hide Files
:
7350.pdf (Any fulltext), 3MB
 
File Permalink:
-
Name:
7350.pdf
Description:
-
Visibility:
Private
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Peron, Thomas K. D.1, Author              
Kurths, Jürgen1, Author              
Rodrigues, F. A.2, Author
Schimansky-Geier, L.2, Author
Sonnenschein, B.2, Author
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: We explore identical Rössler systems organized into two equally sized groups, among which differing positive and negative in- and out-coupling strengths are allowed. With this asymmetric coupling, we analyze patterns in the phase dynamics that coexist with chaotic amplitudes. We specifically investigate traveling phase waves where the oscillators settle on a new rhythm different from their own. We show that these waves are possible even without coherence in the phase angles. It is further demonstrated that the emergence of these incoherent traveling waves depends on the type of coupling, not on the individual dynamics of the Rössler systems. Together with the study of noise effects, our results suggest a promising new avenue toward the interplay of chaotic, noisy, coherent, and incoherent collective dynamics.

Details

show
hide
Language(s):
 Dates: 2016
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1103/PhysRevE.94.042210
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 7350
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Physical Review E
Source Genre: Journal, SCI, Scopus, p3
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 94 Sequence Number: 042210 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/150218