Loughborough University
Browse
PLA_2016.pdf (1.47 MB)

On the origin of heavy-tail statistics in equations of the Nonlinear Schrodinger type

Download (1.47 MB)
journal contribution
posted on 2016-08-04, 10:29 authored by Miguel Onorato, Davide Proment, Gennady El, Stephane Randoux, Pierre Suret
We study the formation of extreme events in incoherent systems described by the Nonlinear Schrödinger type of equations. We consider an exact identity that relates the evolution of the normalized fourth-order moment of the probability density function of the wave envelope to the rate of change of the width of the Fourier spectrum of the wave field. We show that, given an initial condition characterized by some distribution of the wave envelope, an increase of the spectral bandwidth in the focusing/defocusing regime leads to an increase/decrease of the probability of formation of rogue waves. Extensive numerical simulations in 1D+1 and 2D+1 are also performed to confirm the results.

History

School

  • Science

Department

  • Mathematical Sciences

Published in

Physics Letters Section A: General, Atomic and Solid State Physics

Citation

ONORATO, M. ...et al., 2016. On the origin of heavy-tail statistics in equations of the Nonlinear Schrodinger type. Physics Letters A, 380 (39), pp.3173-3177.

Publisher

© Elsevier

Version

  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

2016-07-20

Publication date

2016-07-26

Notes

This paper was accepted for publication in the journal Physics Letters A and the definitive published version is available at http://dx.doi.org/10.1016/j.physleta.2016.07.048.

ISSN

1873-2429

Language

  • en

Usage metrics

    Loughborough Publications

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC