Georgia Institute of Technology College of Sciences

We consider the small noise perturbation (in the Ito sense) of a one dimensional ODE. We study the case in which the ODE has not unique solution, but the SDE does. A particular setting of this sort is studied and the properties of the solution are obtained when the noise level vanishes. We relate this to give an example of a 1-dimensional transport equation without uniqueness of weak solution. We show how by a suitable random noise perturbation, the stochastic equation is well posed and study what the limit is when the noise level tends to zero.

In this talk I will present an elementary short proof of the existence of global in time H¨older continuous solutions for the Stochastic Navier-Stokes equation with small initial data ( in both, 3 and 2 dimensions). The proof is based on a Stochastic Lagrangian formulation of the Navier-Strokes equations. This talk summarizes several papers by Iyer, Mattingly and Constantin.

In my talk, I will present the main results of a recent article by Martin Hairer and Jonathan Mattingly on an ergodic theorem for Markov chains. I will consider Markov chains evolving in discrete time on an abstract, possibly uncountable, state space. Under certain regularity assumptions on the chain's transition kernel, such as the existence of a Foster-Lyapunov function with small level sets (what exactly is meant by that will be thoroughly explained in the talk), one can establish the existence and uniqueness of a stationary distribution. I will focus on a new proof technique for that theorem which relies on a family of metrics on the set of probability measures living on the state space. The main result of my talk will be a strict contraction estimate involving these metrics.

The talk is based on the paper by B. Klartag. It will be shown that there exists a sequence \eps_n\to 0 for which the following holds: let K be a compact convex subset in R^n with nonempty interior and X a random vector uniformly distributed in K. Then there exists a unit vector v, a real number \theta and \sigma^2>0 such that d_TV(, Z)\leq \eps_n where Z has Normal(\theta,\sigma^2) distribution and d_TV - the total variation distance. Under some additional assumptions on X, the statement is true for most vectors v \in R^n.