Martin Hairer

A theory of regularity structures

We introduce a new notion of “regularity structure” that provides an algebraic framework allowing to describe functions and/or distributions via a kind of “jet” or local Taylor expansion around each point. The main novel idea is to replace the classical polynomial model which is suitable for describing smooth functions by arbitrary models that are purpose-built for the problem at hand. In particular, this allows to describe the local behaviour not only of functions but also of large classes of distributions. We then build a calculus allowing to perform the various operations (multiplication, composition with smooth functions, integration against singular kernels) necessary to formulate fixed point equations for a very large class of semilinear PDEs driven by some very singular (typically random) input. This allows, for the first time, to give a mathematically rigorous meaning to many interesting stochastic PDEs arising in physics. The theory comes with convergence results that allow to interpret the solutions obtained in this way as limits of classical solutions to regularised problems, possibly modified by the addition of diverging counterterms. These counterterms arise naturally through the action of a “renormalisation group” which is defined canonically in terms of the regularity structure associated to the given class of PDEs. Our theory also allows to easily recover many existing results on singular stochastic PDEs (KPZ equation, stochastic quantisation equations, Burgers-type equations) and to understand them as particular instances of a unified framework. One surprising insight is that in all of these instances local solutions are actually “smooth” in the sense that they can be approximated locally to arbitrarily high degree as linear combinations of a fixed family of random functions/distributions that play the role of “polynomials” in the theory. As an example of a novel application, we solve the long-standing problem of building a natural Markov process that is symmetric with respect to the (finite volume) measure describing the

A Course on Rough Paths

\Phi ^4_3

ANALYSIS OF SPDES ARISING IN PATH SAMPLING PART II: THE NONLINEAR CASE

Φ34 Euclidean quantum field theory. It is natural to conjecture that the Markov process built in this way describes the Glauber dynamic of

Spectral Properties of Hypoelliptic Operators

3

Ergodicity of stochastic differential equations driven by fractional Brownian motion

3-dimensional ferromagnets near their critical temperature.

A simple framework to justify linear response theory

The aim of this note is to present an elementary proof of a variation of Harris’ ergodic theorem of Markov chains.