The formalism recently introduced in [BHZ ] allows one to assign a regularity structure, as well as a corresponding "renormalisation group", to any subcritical system of semilinear stochastic PDEs. Under very mild additional assumptions, it was then shown in [CH ] that large classes of driving noises exhibiting the relevant small-scale behaviour can be lifted to such a regularity structure in a robust way, following a renormalisation procedure reminiscent of the BPHZ procedure arising in perturbative QFT.The present work completes this programme by constructing an action of the renormalisation group onto a suitable class of stochastic PDEs which is intertwined with its action on the corresponding space of models. This shows in particular that solutions constructed from the BPHZ lift of a smooth driving noise coincide with the classical solutions of a modified PDE. This yields a very general black box type local existence and stability theorem for a wide class of singular nonlinear SPDEs.

Consider the problem of estimating the small probability that the maximum of a random walk exceeds a large threshold, when the process has a negative drift and the underlying random variables may have heavy tailed distributions. We consider one class of such problems that has applications in estimating the ruin probability associated with insurance claim processes with subexponentially distributed claim sizes, and in estimating the probability of large delays in single server M/G/1 queues with subexponentially distributed service times. Significant work has been done on analogous problems for the light tailed case (when the moment generating function exists in a neighborhood around zero, so that the tail decreases at an exponential rate or faster) involving importance sampling methods that use exponential twisting. However, for the subexponential case, moment generating functions do not exist in the pertinent regions making exponential twisting infeasible. In this paper we introduce importance sampling techniques where the new probability measure is obtained by twisting the hazard rate of the original distribution. For subexponential distributions this amounts to twisting at a subexponential rate. We also introduce the technique of "delaying" the change of measure and show that the combination of the two techniques produces asymptotically optimal estimates of the small probabilities mentioned above for a large class of subexponential distributions.

These lecture notes grew out of a series of lectures given by the second named author in short courses in Toulouse, Matsumoto, and Darmstadt. The main aim is to explain some aspects of the theory of "Regularity structures" developed recently by Hairer in [27]. This theory gives a way to study wellposedness for a class of stochastic PDEs that could not be treated previously. Prominent examples include the KPZ equation as well as the dynamic Φ 4 3 model.Such equations can be expanded into formal perturbative expansions. Roughly speaking the theory of regularity structures provides a way to truncate this expansion after finitely many terms and to solve a fixed point problem for the "remainder". The key ingredient is a new notion of "regularity" which is based on the terms of this expansion.Résumé. Ces notes sont basées sur trois cours que le deuxième auteur a donnés à Toulouse, Matsumoto et Darmstadt. L'objectif principal est d'expliquer certains aspects de la théorie des "structures de régularité" développée récemment par Hairer [27]. Cette théorie permet de montrer que certaines EDP stochastiques, qui ne pouvaient pas être traitées auparavant, sont bien posées. Parmi les exemples se trouvent l'équation KPZ et le modèle Φ 4 3 dynamique. Telles équations peuvent être développées en séries perturbatives formelles. La théorie des structures de régularité permet de tronquer ce développement aprés un nombre fini de termes, et de résoudre un problème de point fixe pour le reste. L'idée principale est une nouvelle notion de régularité des distributions, qui dépend des termes de ce développement. Random Distributions and Scaling Behaviour.1 There are also some physical phenomena appearing in the scale regimes that regularity structures can access, such as near -critical systems at large volume.

We define a natural state space and Markov process associated to the stochastic Yang–Mills heat flow in two dimensions.To accomplish this we first introduce a space of distributional connections for which holonomies along sufficiently regular curves (Wilson loop observables) and the action of an associated group of gauge transformations are both well-defined and satisfy good continuity properties. The desired state space is obtained as the corresponding space of orbits under this group action and is shown to be a Polish space when equipped with a natural Hausdorff metric.To construct the Markov process we show that the stochastic Yang–Mills heat flow takes values in our space of connections and use the “DeTurck trick” of introducing a time dependent gauge transformation to show invariance, in law, of the solution under gauge transformations.Our main tool for solving for the Yang–Mills heat flow is the theory of regularity structures and along the way we also develop a “basis-free” framework for applying the theory of regularity structures in the context of vector-valued noise – this provides a conceptual framework for interpreting several previous constructions and we expect this framework to be of independent interest.

Upon its inception the theory of regularity structures [Hai14] allowed for the treatment for many semilinear perturbations of the stochastic heat equation driven by space-time white noise. When the driving noise is non-Gaussian the machinery of the theory can still be used but must be combined with an infinite number of stochastic estimates in order to compensate for the loss of hypercontractivity, as was done in [HS15]. In this paper we obtain a more streamlined and automatic set of criteria implying these estimates which facilitates the treatment of some other problems including non-Gaussian noise such as some general phase coexistence models [HX16], [SX16] -as an example we prove here a generalization of the Wong-Zakai Theorem found in [HP15].