15:45
Applications of rough integrals: from PDEs to mathematical physics
Abstract
I will describe some applications of the main techniques of rough paths
theory to problems not related to SDE
Forthcoming events in this series
I will describe some applications of the main techniques of rough paths
theory to problems not related to SDE
Gradient bounds are a very powerful tool to study heat kernel measures and
regularisation properties for the heat kernel. In the elliptic case, it is easy
to derive them from bounds on the Ricci tensor of the generator. In recent
years, many efforts have been made to extend these bounds to some simple
examples in the hypoelliptic situation. The simplest case is the Heisenberg
group. In this talk, we shall discuss some recent developments (due to H.Q. Li)
on this question, and give some elementary proofs of these bounds.
Relatively little is known about the ability of numerical methods for stochastic differential equations (SDE
Diffusion limited aggregation (DLA) is a random growth model which was
originally introduced in 1981 by Witten and Sander. This model is prevalent in
nature and has many applications in the physical sciences as well as industrial
processes. Unfortunately it is notoriously difficult to understand, and only one
rigorous result has been proved in the last 25 years. We consider a simplified
version of DLA known as the Eden model which can be used to describe the growth
of cancer cells, and show that under certain scaling conditions this model gives
rise to a limit object known as the Brownian web.
We study the parabolic Anderson problem, i.e., the heat equation on the d-dimentional
integer lattice with independent identically distributed random potential and
localised initial condition. Our interest is in the long-term behaviour of the
random total mass of the unique non-negative solution, and we prove the complete
localisation of mass for potentials with polynomial tails.
First I will introduce Poisson random measures and their connection to Levy processes. Then SPDE
We will see how Dynkin's isomorphism emerges from the "loop soup" introduced by
Lawler and Werner.
Sequential Monte Carlo Samplers are a class of stochastic algorithms for
Monte Carlo integral estimation w.r.t. probability distributions, which combine
elements of Markov chain Monte Carlo methods and importance sampling/resampling
schemes. We develop a stability analysis by functional inequalities for a
nonlinear flow of probability measures describing the limit behaviour of the
methods as the number of particles tends to infinity. Stability results are
derived both under global and local assumptions on the generator of the
underlying Metropolis dynamics. This allows us to prove that the combined
methods sometimes have good asymptotic stability properties in multimodal setups
where traditional MCMC methods mix extremely slowly. For example, this holds for
the mean field Ising model at all temperatures.
We report on two joint works with Jeremy Quastel and Alejandro Ramirez, on an
interacting particle system which can be viewed as a combustion mechanism or a
chemical reaction.
We consider a model of the reaction $X+Y\to 2X$ on the integer lattice in
which $Y$ particles do not move while $X$ particles move as independent
continuous time, simple symmetric random walks. $Y$ particles are transformed
instantaneously to $X$ particles upon contact.
We start with a fixed number $a\ge 1$ of $Y$ particles at each site to the
right of the origin, and define a class of configurations of the $X$ particles
to the left of the origin having a finite $l^1$ norm with a specified
exponential weight. Starting from any configuration of $X$ particles to the left
of the origin within such a class, we prove a central limit theorem for the
position of the rightmost visited site of the $X$ particles.
We consider Burgers type nonlinear SPDEs with L