Izvestiya Instituta Matematiki I Informatiki Udmurtskogo Gosudarstvennogo Universiteta 2020
DOI: 10.35634/2226-3594-2020-55-10
|View full text |Cite
|
Sign up to set email alerts
|

Analysis of stochastic sensitivity of Turing patterns in distributed reaction-diffusion systems

Abstract: In this paper, a distributed stochastic Brusselator model with diffusion is studied. We show that a variety of stable spatially heterogeneous patterns is generated in the Turing instability zone. The effect of random noise on the stochastic dynamics near these patterns is analysed by direct numerical simulation. Noise-induced transitions between coexisting patterns are studied. A stochastic sensitivity of the pattern is quantified as the mean-square deviation from the initial unforced pattern. We show … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2020
2020
2020
2020

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 19 publications
0
1
0
Order By: Relevance
“…[19][20][21][22][23][24][25] Novelty of the present paper is in the analytical approach to estimation of mean-square deviation of random states from the deterministic patterns-attractors. 26 Section 2 introduces the stochastic sensitivity function method for stationary states of spatially distributed systems. In Section 3, a spatially extended stochastic Brusselator 27,28 model with diffusion is considered.…”
Section: Introductionmentioning
confidence: 99%
“…[19][20][21][22][23][24][25] Novelty of the present paper is in the analytical approach to estimation of mean-square deviation of random states from the deterministic patterns-attractors. 26 Section 2 introduces the stochastic sensitivity function method for stationary states of spatially distributed systems. In Section 3, a spatially extended stochastic Brusselator 27,28 model with diffusion is considered.…”
Section: Introductionmentioning
confidence: 99%