Mon, 05 May 2025
15:30
L3

Weak Error of Dean-Kawasaki Equation with Smooth Mean-Field Interactions

Dr. Ana Djurdjevac
(Freie Universität Berlin)
Abstract

We consider the weak-error rate of the SPDE approximation by regularized Dean-Kawasaki equation with Itô noise, for particle systems exhibiting mean-field interactions both in the drift and the noise terms. Global existence and uniqueness of solutions to the corresponding SPDEs are established via the variational approach to SPDEs. To estimate the weak error, we employ the Kolmogorov equation technique on the space of probability measures. This work generalizes previous results for independent Brownian particles — where Laplace duality was used. In particular, we recover the same weak error rate as in that setting. This paper builds on joint work with X. Ji., H. Kremp and  N.  Perkowski.

Mon, 10 Mar 2025
15:30
L3

Recent progress on quantitative propagation of chaos

Dr Daniel Lacker
(Columbia University)
Abstract

When and how well can a high-dimensional system of stochastic differential equations (SDEs) be approximated by one with independent coordinates? This fundamental question is at the heart of the theory of mean field limits and the propagation of chaos phenomenon, which arise in the study of large (many-body) systems of interacting particles. This talk will present recent sharp quantitative answers to this question, both for classical mean field models and for more recently studied non-exchangeable models. Two high-level ideas underlie these answers. The first is a simple non-asymptotic construction, called the independent projection, which is a natural way to approximate a general SDE system by one with independent coordinates. The second is a "local" perspective, in which low-dimensional marginals are estimated iteratively by adding one coordinate at a time, leading to surprising improvements on prior results obtained by "global" arguments such as subadditivity inequalities. In the non-exchangeable setting, we exploit a surprising connection with first-passage percolation.

Mon, 03 Mar 2025
15:30
L3

Spin glasses with multiple types

Dr Jean-Christophe Mourrat
(ENS Lyon)
Abstract

Spin glasses are models of statistical mechanics in which a large number of elementary units interact with each other in a disordered manner. In the simplest case, there are direct interactions between any two units in the system, and I will start by reviewing some of the key mathematical results in this context. For modelling purposes, it is also desirable to consider models with more structure, such as when the units are split into two groups, and the interactions only go from one group to the other one. I will then discuss some of the technical challenges that arise in this case, as well as recent progress.

Mon, 24 Feb 2025
15:30
L3

Sharp bounds for parameter-dependent stochastic integrals

Dr Sonja Cox
(University of Amsterdam)
Abstract

We provide sharp bounds in the supremum- and Hölder norm for parameter-dependent stochastic integrals. As an application we obtain novel long-term bounds for stochastic partial differential equations as well as novel bounds on the space-time modulus of continuity of the stochastic heat equation. This concerns joint work with Joris van Winden (TU Delft).

Mon, 03 Feb 2025
15:30
L3

Analyzing the Error in Score-Based Generative Models: A Stochastic Control Approach

Dr Giovanni Conforti
(University of Padova)
Abstract

Score-based generative models (SGMs), which include diffusion models and flow matching, have had a transformative impact on the field of generative modeling. In a nutshell, the key idea is that by taking the time-reversal of a forward ergodic diffusion process initiated at the data distribution, one can "generate data from noise." In practice, SGMs learn an approximation of the score function of the forward process and employ it to construct an Euler scheme for its time reversal.

In this talk, I will present the main ideas of a general strategy that combines insights from stochastic control and entropic optimal transport to bound the error in SGMs. That is, to bound the distance between the algorithm's output and the target distribution. A nice feature of this approach is its robustness: indeed, it can be used to analyse SGMs built upon noising dynamics that are different from the Ornstein-Uhlenbeck process . As an example, I will illustrate how to obtain error bounds for SGMs on the hypercube.

Based on joint works with A.Durmus, M.Gentiloni-Silveri, Nhi Pham Le Tuyet and Dario Shariatian
Mon, 27 Jan 2025
15:30
L3

Adapted optimal transport for stochastic processes

Dr Daniel Bartl
(University of Vienna)
Abstract
In this talk, I will discuss adapted transport theory and the adapted Wasserstein distance, which extend classical transport theory from probability measures to stochastic processes by incorporating the temporal flow of information. This adaptation addresses key limitations of classical transport when dealing with time-dependent data. 
I will highlight how, unlike other topologies for stochastic processes, the adapted Wasserstein distance ensures continuity for fundamental probabilistic operations, including the Doob decomposition, optimal stopping, and stochastic control. Additionally, I will explore how adapted transport preserves many desirable properties of classical transport theory, making it a powerful tool for analyzing stochastic systems.
Mon, 20 Jan 2025
15:30
L3

Heat kernel for critical percolation clusters on the binary tree.

Prof Martin T Barlow
(University of British Columbia )
Abstract
Kesten defined the incipient infinite cluster (IIC) as the limit of large critical finite percolation clusters. We look at the (quenched) heat kernel on the IIC, and will see how it fluctuates due to the randomness of the cluster. 
 
This is a joint work with David Croydon and Takashi Kumagai. 
Mon, 03 Mar 2025
16:30
L4

The Stein-log-Sobolev inequality and the exponential rate of convergence for the continuous Stein variational gradient descent method

Jakub Jacek Skrzeczkowski
(Mathematical Institute)
Abstract

The Stein Variational Gradient Descent method is a variational inference method in statistics that has recently received a lot of attention. The method provides a deterministic approximation of the target distribution, by introducing a nonlocal interaction with a kernel. Despite the significant interest, the exponential rate of convergence for the continuous method has remained an open problem, due to the difficulty of establishing the related so-called Stein-log-Sobolev inequality. Here, we prove that the inequality is satisfied for each space dimension and every kernel whose Fourier transform has a quadratic decay at infinity and is locally bounded away from zero and infinity. Moreover, we construct weak solutions to the related PDE satisfying exponential rate of decay towards the equilibrium. The main novelty in our approach is to interpret the Stein-Fisher information as a duality pairing between $H^{-1}$ and $H^{1}$, which allows us to employ the Fourier transform. We also provide several examples of kernels for which the Stein-log-Sobolev inequality fails, partially showing the necessity of our assumptions. This is a joint work with J. A. Carrillo and J. Warnett. 

Mon, 03 Feb 2025
16:30
L4

Shock Reflection and other 2D Riemann Problems in Gas Dynamics

Alexander Cliffe
(Università degli Studi di Padova)
Abstract

The Riemann problem is an IVP having simple piecewise constant initial data that is invariant under scaling. In 1D, the problem was originally considered by Riemann during the 19th century in the context of gas dynamics, and the general theory was more or less completed by Lax and Glimm in the mid-20th century. In 2D and MD, the situation is much more complicated, and very few analytic results are available. We discuss a shock reflection problem for the Euler equations for potential flow, with initial data that generates four interacting shockwaves. After reformulating the problem as a free boundary problem for a nonlinear PDE of mixed hyperbolic-elliptic type, the problem is solved via a sophisticated iteration procedure. The talk is based on joint work with G-Q Chen (Oxford) et. al. arXiv:2305.15224, to appear in JEMS (2025).

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