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  4. SPECTRAL GAPS IN WASSERSTEIN DISTANCES AND THE 2D STOCHASTIC NAVIER-STOKES EQUATIONS
 
journal article

SPECTRAL GAPS IN WASSERSTEIN DISTANCES AND THE 2D STOCHASTIC NAVIER-STOKES EQUATIONS

Hairer, Martin  
•
Mattingly, Jonathan C.
November 1, 2008
ANNALS OF PROBABILITY

We develop a general method to prove the existence of spectral gaps for Markov semigroups on Banach spaces. Unlike most previous work, the type of norm we consider for this analysis is neither a weighted supremum norm nor an LP-type norm, but involves the derivative of the observable as well and hence can be seen as a type of 1-Wasserstein distance. This turns Out to be a suitable approach for infinite-dimensional spaces where the usual Harris or Doeblin conditions, which are geared toward total variation convergence, often fail to hold. In the first part of this paper, we consider semigroups that have uniform behavior which one can view its the analog of Doeblin's condition. We then proceed to Study Situations where the behavior is not SO Uniform, but the system has a suitable Lyapunov structure, leading to a type of Harris condition. We finally show that the latter condition is satisfied by the two-dimensional stochastic Navier-Stokes equations. even in situations where the forcing is extremely de generate. Using the convergence result, we show that the stochastic Navier-Stokes equations' invariant measures depend continuously on the viscosity and the structure of the forcing.

  • Details
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Type
journal article
DOI
10.1214/08-AOP392
Web of Science ID

WOS:000262228800002

Author(s)
Hairer, Martin  
Mattingly, Jonathan C.
Date Issued

2008-11-01

Publisher

INST MATHEMATICAL STATISTICS

Published in
ANNALS OF PROBABILITY
Volume

36

Issue

6

Start page

2050

End page

2091

Subjects

INFINITE-DIMENSIONAL SYSTEMS

•

MALLIAVIN CALCULUS

•

COUPLING APPROACH

•

DEGENERATE NOISE

•

ERGODICITY

•

DYNAMICS

•

PDES

•

APPROXIMATION

•

BURGERS

•

Stochastic PDEs

•

Wasserstein distance

•

ergodicity

•

mixing

•

spectral gap

•

Science & Technology

•

Physical Sciences

Editorial or Peer reviewed

REVIEWED

Written at

OTHER

EPFL units
PROPDE  
FunderFunding(s)Grant NumberGrant URL

Alfred P. Sloan Foundation fellowship

EPSRC

EP/D071593/1

NSF PECASE Award

DMS-04-49910

Available on Infoscience
September 17, 2024
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/241170
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