Literature DB >> 27739820

Dynamical indicators for the prediction of bursting phenomena in high-dimensional systems.

Mohammad Farazmand1, Themistoklis P Sapsis1.   

Abstract

Drawing upon the bursting mechanism in slow-fast systems, we propose indicators for the prediction of such rare extreme events which do not require a priori known slow and fast coordinates. The indicators are associated with functionals defined in terms of optimally time-dependent (OTD) modes. One such functional has the form of the largest eigenvalue of the symmetric part of the linearized dynamics reduced to these modes. In contrast to other choices of subspaces, the proposed modes are flow invariant and therefore a projection onto them is dynamically meaningful. We illustrate the application of these indicators on three examples: a prototype low-dimensional model, a body-forced turbulent fluid flow, and a unidirectional model of nonlinear water waves. We use Bayesian statistics to quantify the predictive power of the proposed indicators.

Entities:  

Year:  2016        PMID: 27739820     DOI: 10.1103/PhysRevE.94.032212

Source DB:  PubMed          Journal:  Phys Rev E        ISSN: 2470-0045            Impact factor:   2.529


  5 in total

1.  Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.

Authors:  Pantelis R Vlachas; Wonmin Byeon; Zhong Y Wan; Themistoklis P Sapsis; Petros Koumoutsakos
Journal:  Proc Math Phys Eng Sci       Date:  2018-05-23       Impact factor: 2.704

Review 2.  New perspectives for the prediction and statistical quantification of extreme events in high-dimensional dynamical systems.

Authors:  Themistoklis P Sapsis
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2018-08-28       Impact factor: 4.226

3.  Data-assisted reduced-order modeling of extreme events in complex dynamical systems.

Authors:  Zhong Yi Wan; Pantelis Vlachas; Petros Koumoutsakos; Themistoklis Sapsis
Journal:  PLoS One       Date:  2018-05-24       Impact factor: 3.240

4.  Cluster-based network modeling-From snapshots to complex dynamical systems.

Authors:  Daniel Fernex; Bernd R Noack; Richard Semaan
Journal:  Sci Adv       Date:  2021-06-16       Impact factor: 14.136

5.  A variational approach to probing extreme events in turbulent dynamical systems.

Authors:  Mohammad Farazmand; Themistoklis P Sapsis
Journal:  Sci Adv       Date:  2017-09-22       Impact factor: 14.136

  5 in total

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