Stochastic sensitivity of Turing patterns: methods and applications to the analysis of noise-induced transitions

Irina Bashkirtseva, Alexander Kolinichenko and Lev Ryashko

Chaos, Solitons & Fractals, 2021, vol. 153, issue P2

Abstract: In this paper, a problem of the analysis of the randomly forced patterns in spatially distributed systems with diffusion is considered. For the approximation of mean-square deviations of random solutions from the unforced deterministic pattern-attractors, we suggest a constructive method based on the stochastic sensitivity technique. To demonstrate an efficiency of this method, we consider the Levin-Segel model with formation of non-homogeneous structures of the phytoplankton and herbivore populations. The spatial peculiarities of probabilistic distributions near patterns are investigated. The dependence of the stochastic sensitivity on the variation of system parameters is studied. An application of the stochastic sensitivity technique to the study of noise-induced transitions between coexisting spatial structures is demonstrated.

Keywords: Spatially distributed systems; Diffusion; Random disturbances; Patterns; Stochastic sensitivity; Noise-induced transitions (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077921008456
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:153:y:2021:i:p2:s0960077921008456

DOI: 10.1016/j.chaos.2021.111491

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().