Talks and presentations

Sharp convergence rates for singular SPDEs - the case of KPZ

March 31, 2026

Conference, From discrete systems to SPDEs, Vienna, Austria

For subcritical singular SPDEs, the theory of regularity structures typically yields, as the mollification scale \(\varepsilon\to0\), a convergence rate of order \(\varepsilon^\gamma\) for some small \(\gamma>0\). The exponent \(\gamma\) is limited by the gap between the optimal regularity of the driving noise and the regularity in which the noise is measured; in this sense, one can trade regularity of the solution for convergence rate. For instance, under the subcritical and no variance blow-up conditions, in the KPZ equation driven by space-time white noise one obtains \(\gamma<\frac14\), while for \(\Phi^4_3\) one gets \(\gamma<\frac13\). In this work, we show that the optimal exponent for KPZ is \(\gamma=\frac12\). More precisely, we prove if \(u_\varepsilon\) denotes the KPZ solution driven by mollified white noise at scale \(\varepsilon\), then \(\varepsilon^{-1/2}(u_\varepsilon - u_{\varepsilon/2})\) is bounded in \(L^p(\Omega)\) for all \(p \ge 2\) and converges in law to a non-trivial limit \(w\), which solves another singular SPDE driven by an independent white noise. This CLT-type result identifies the optimal convergence rate for KPZ. We also conjecture that the same mechanism yields the optimal rates \(\gamma=1\) for \(\Phi^4_3\) and \(\gamma=2\) for \(\Phi^4_2\), which will be studied in forthcoming work.

Variance renormalisation of two dimensional gPAM with differentiated space white noise

September 25, 2025

Workshop, Young Researchers in Stochastic Analysis and Stochastic Geometric Analysis, Lausanne, Switzerland

In this work, we consider the gPAM equation with a differentiated space white noise in two dimensions. A particularity of this equation is that it just falls on the borderline of the subcriticality condition; moreover, even if one could have chosen a slightly more regular noise, the variance of certain non-linear functionals of the noise is expected to explode and the local solution theory would still fail. To tame down this variance blowup, a multiplicative renormalisation is introduced. This multiplicative renormalisation was first carried out by [Hairer ‘24] in the case of KPZ, and a general prediction was made there for a wider class of equations. The current work therefore has the objective to show gPAM falls into this picture. It is worth noting that, while a Da Prato-Debussche trick was used in the KPZ case, no such trick is available for gPAM. One thus has to work on the level of singular SPDE machineries such as regularity structure to obtain the desired result.