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arxiv: 1603.02527 · v1 · pith:Q6ERG2VLnew · submitted 2016-03-08 · 🧮 math.PR

Large deviations for the two-dimensional stochastic Navier-Stokes equation with vanishing noise correlation

classification 🧮 math.PR
keywords largecorrelationdeviationequationnavier-stokesnoiseprincipletwo-dimensional
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We are dealing with the validity of a large deviation principle for the two-dimensional Navier-Stokes equation, with periodic boundary conditions, perturbed by a Gaussian random forcing. We are here interested in the regime where both the strength of the noise and its correlation are vanishing, on a length scale $\e$ and $\d(\e)$, respectively, with $0<\e,\ \d(\e)<<1$. Depending on the relationship between $\e$ and $\d(\e)$ we will prove the validity of the large deviation principle in different functional spaces.

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