Stationary solutions of stochastic differential equations with memory and stochastic partial differential equations

dc.contributor.author

Bakhtin, Y

dc.contributor.author

Mattingly, JC

dc.date.accessioned

2022-04-01T14:02:07Z

dc.date.available

2022-04-01T14:02:07Z

dc.date.issued

2005-10-01

dc.date.updated

2022-04-01T14:02:06Z

dc.description.abstract

We explore Itô stochastic differential equations where the drift term possibly depends on the infinite past. Assuming the existence of a Lyapunov function, we prove the existence of a stationary solution assuming only minimal continuity of the coefficients. Uniqueness of the stationary solution is proven if the dependence on the past decays sufficiently fast. The results of this paper are then applied to stochastically forced dissipative partial differential equations such as the stochastic Navier-Stokes equation and stochastic Ginsburg-Landau equation. © World Scientific Publishing Company.

dc.identifier.issn

0219-1997

dc.identifier.issn

1793-6683

dc.identifier.uri

https://hdl.handle.net/10161/24757

dc.language

en

dc.publisher

World Scientific Pub Co Pte Lt

dc.relation.ispartof

Communications in Contemporary Mathematics

dc.relation.isversionof

10.1142/S0219199705001878

dc.subject

Science & Technology

dc.subject

Physical Sciences

dc.subject

Mathematics, Applied

dc.subject

Mathematics

dc.subject

stochastic differential equations

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memory

dc.subject

Lyapunov functions

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ergodicity

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stationary solutions

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stochastic Navier-Stokes equation

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stochastic Ginsburg-Landau equation

dc.subject

NAVIER-STOKES EQUATIONS

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FORCED NONLINEAR PDES

dc.subject

COUPLING APPROACH

dc.subject

ERGODICITY

dc.subject

DYNAMICS

dc.title

Stationary solutions of stochastic differential equations with memory and stochastic partial differential equations

dc.type

Journal article

duke.contributor.orcid

Mattingly, JC|0000-0002-1819-729X

pubs.begin-page

553

pubs.end-page

582

pubs.issue

5

pubs.organisational-group

Duke

pubs.organisational-group

Trinity College of Arts & Sciences

pubs.organisational-group

Mathematics

pubs.organisational-group

Statistical Science

pubs.publication-status

Published

pubs.volume

7

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