The Annals of Probability

Spectral gaps in Wasserstein distances and the 2D stochastic Navier–Stokes equations

Martin Hairer and Jonathan C. Mattingly

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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 $\L^{p}$-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 as 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 degenerate. 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.

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Ann. Probab. Volume 36, Number 6 (2008), 2050-2091.

First available in Project Euclid: 19 December 2008

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Primary: 37A30: Ergodic theorems, spectral theory, Markov operators {For operator ergodic theory, see mainly 47A35} 37A25: Ergodicity, mixing, rates of mixing 60H15: Stochastic partial differential equations [See also 35R60]

Stochastic PDEs Wasserstein distance ergodicity mixing spectral gap


Hairer, Martin; Mattingly, Jonathan C. Spectral gaps in Wasserstein distances and the 2D stochastic Navier–Stokes equations. Ann. Probab. 36 (2008), no. 6, 2050--2091. doi:10.1214/08-AOP392.

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