Predicting catastrophes in nonlinear dynamical systems by compressive sensing

Wen Xu Wang, Rui Yang, Ying-Cheng Lai, Vassilios Kovanis, Celso Grebogi

Research output: Contribution to journalArticle

151 Scopus citations

Abstract

An extremely challenging problem of significant interest is to predict catastrophes in advance of their occurrences. We present a general approach to predicting catastrophes in nonlinear dynamical systems under the assumption that the system equations are completely unknown and only time series reflecting the evolution of the dynamical variables of the system are available. Our idea is to expand the vector field or map of the underlying system into a suitable function series and then to use the compressive-sensing technique to accurately estimate the various terms in the expansion. Examples using paradigmatic chaotic systems are provided to demonstrate our idea.

Original languageEnglish (US)
Article number154101
JournalPhysical Review Letters
Volume106
Issue number15
DOIs
StatePublished - Apr 15 2011

    Fingerprint

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this