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

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Date
2005-10-01
Authors
Bakhtin, Y
Mattingly, JC
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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.
Type
Journal article
Subject
Science & Technology
Physical Sciences
Mathematics, Applied
Mathematics
stochastic differential equations
memory
Lyapunov functions
ergodicity
stationary solutions
stochastic Navier-Stokes equation
stochastic Ginsburg-Landau equation
NAVIER-STOKES EQUATIONS
FORCED NONLINEAR PDES
COUPLING APPROACH
ERGODICITY
DYNAMICS
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https://hdl.handle.net/10161/24757
Published Version (Please cite this version)
10.1142/S0219199705001878
Publication Info
Bakhtin, Y; & Mattingly, JC (2005). Stationary solutions of stochastic differential equations with memory and stochastic partial differential equations. Communications in Contemporary Mathematics, 7(5). pp. 553-582. 10.1142/S0219199705001878. Retrieved from https://hdl.handle.net/10161/24757.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

Mattingly

Jonathan Christopher Mattingly

James B. Duke Distinguished Professor
Jonathan Christopher  Mattingly grew up in Charlotte, NC where he attended Irwin Ave elementary and Charlotte Country Day.  He graduated from the NC School of Science and Mathematics and received a BS is Applied Mathematics with a concentration in physics from Yale University. After two years abroad with a year spent at ENS Lyon studying nonlinear and statistical physics on a Rotary Fellowship, he returned to the US to attend Princeton University where he obtained a PhD in Applied and
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