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Self-Organization in Randomly Forced Diffusion Systems: A Stochastic Sensitivity Technique. / Kolinichenko, Alexander; Bashkirtseva, Irina; Ryashko, Lev.
In: Mathematics, Vol. 11, No. 2, 451, 2023.

Research output: Contribution to journal › Article › peer-review

Harvard

Kolinichenko, A, Bashkirtseva, I & Ryashko, L 2023, 'Self-Organization in Randomly Forced Diffusion Systems: A Stochastic Sensitivity Technique', Mathematics, vol. 11, no. 2, 451. https://doi.org/10.3390/math11020451

APA

Kolinichenko, A., Bashkirtseva, I., & Ryashko, L. (2023). Self-Organization in Randomly Forced Diffusion Systems: A Stochastic Sensitivity Technique. Mathematics, 11(2), [451]. https://doi.org/10.3390/math11020451

Vancouver

Kolinichenko A, Bashkirtseva I, Ryashko L. Self-Organization in Randomly Forced Diffusion Systems: A Stochastic Sensitivity Technique. Mathematics. 2023;11(2):451. doi: 10.3390/math11020451

Author

Kolinichenko, Alexander ; Bashkirtseva, Irina ; Ryashko, Lev. / Self-Organization in Randomly Forced Diffusion Systems: A Stochastic Sensitivity Technique. In: Mathematics. 2023 ; Vol. 11, No. 2.

BibTeX

@article{2597dba6ac734eb896480f5fb94fdbd9,
title = "Self-Organization in Randomly Forced Diffusion Systems: A Stochastic Sensitivity Technique",
abstract = "The problem with the analysis of noise-induced transitions between patterns in distributed stochastic systems is considered. As a key model, we use the spatially extended dynamical {"}phytoplankton-herbivore{"} system with diffusion. We perform the parametric bifurcation analysis of this model and determine the Turing instability zone, where non-homogeneous patterns are generated by diffusion. The multistability of this deterministic model with the coexistence of several waveform pattern-attractors is found. We study how noise affects these non-homogeneous patterns and estimate the dispersion of random states using a new technique based on stochastic sensitivity function (SSF) analysis and the confidence domain method. To investigate the preferences in noise-induced transitions between patterns, we analyze and compare the results of this theoretical approach with the statistics extracted from the direct numerical simulation.",
author = "Alexander Kolinichenko and Irina Bashkirtseva and Lev Ryashko",
note = "The work of A.K. on the bifurcation analysis of the deterministic diffusion population model is supported by the Ministry of Science and Higher Education of the Russian Federation (Ural Mathematical Center project No. 075-02-2022-877). The work of A.K., I.B., and L.R. on the research and development of the stochastic sensitivity theory of pattern–attractors and their application to the study of noise-induced effects was supported by the Russian Science Foundation (N 21-11-00062).",
year = "2023",
doi = "10.3390/math11020451",
language = "English",
volume = "11",
journal = "Mathematics",
issn = "2227-7390",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "2",

}

RIS

TY - JOUR

T1 - Self-Organization in Randomly Forced Diffusion Systems: A Stochastic Sensitivity Technique

AU - Kolinichenko, Alexander

AU - Bashkirtseva, Irina

AU - Ryashko, Lev

N1 - The work of A.K. on the bifurcation analysis of the deterministic diffusion population model is supported by the Ministry of Science and Higher Education of the Russian Federation (Ural Mathematical Center project No. 075-02-2022-877). The work of A.K., I.B., and L.R. on the research and development of the stochastic sensitivity theory of pattern–attractors and their application to the study of noise-induced effects was supported by the Russian Science Foundation (N 21-11-00062).

PY - 2023

Y1 - 2023

N2 - The problem with the analysis of noise-induced transitions between patterns in distributed stochastic systems is considered. As a key model, we use the spatially extended dynamical "phytoplankton-herbivore" system with diffusion. We perform the parametric bifurcation analysis of this model and determine the Turing instability zone, where non-homogeneous patterns are generated by diffusion. The multistability of this deterministic model with the coexistence of several waveform pattern-attractors is found. We study how noise affects these non-homogeneous patterns and estimate the dispersion of random states using a new technique based on stochastic sensitivity function (SSF) analysis and the confidence domain method. To investigate the preferences in noise-induced transitions between patterns, we analyze and compare the results of this theoretical approach with the statistics extracted from the direct numerical simulation.

AB - The problem with the analysis of noise-induced transitions between patterns in distributed stochastic systems is considered. As a key model, we use the spatially extended dynamical "phytoplankton-herbivore" system with diffusion. We perform the parametric bifurcation analysis of this model and determine the Turing instability zone, where non-homogeneous patterns are generated by diffusion. The multistability of this deterministic model with the coexistence of several waveform pattern-attractors is found. We study how noise affects these non-homogeneous patterns and estimate the dispersion of random states using a new technique based on stochastic sensitivity function (SSF) analysis and the confidence domain method. To investigate the preferences in noise-induced transitions between patterns, we analyze and compare the results of this theoretical approach with the statistics extracted from the direct numerical simulation.

UR - https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=tsmetrics&SrcApp=tsm_test&DestApp=WOS_CPL&DestLinkType=FullRecord&KeyUT=000918737200001

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85146741941

U2 - 10.3390/math11020451

DO - 10.3390/math11020451

M3 - Article

VL - 11

JO - Mathematics

JF - Mathematics

SN - 2227-7390

IS - 2

M1 - 451

ER -

ID: 33968995