Work

Predicting Catastrophes in Nonlinear Dynamical Systems by Compressive Sensing

Year: 2011

Type: article

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.

Source: Physical Review Letters

DOI: 10.1103/physrevlett.106.154101

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

Institutions: Arizona State University, University of Aberdeen, Wright-Patterson Air Force Base

Language: English

Topic: Chaos control and synchronization

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: Biotechnology and Biological Sciences Research Council, Air Force Office of Scientific Research

Awards: G459135822, Ribosome traffic flow on the mRNA as a regulator of cellular protein production: an integrated modelling and experimental analysis, Combinatorial responses of fungal pathogens to their human hosts: an Integrative Systems Biology approach, G6020801980