Published · Stochastics and Partial Differential Equations: Analysis and Computations 13(2) (2025), 956–1033. Using the method of paracontrolled distributions, we show the local well-posedness of an additive noise approximation to the fluctuating hydrodynamics of the Keller-Segel model on the two-dimensional torus. Our approximation is a…
Published · Proceedings of the 42nd International Conference on Machine Learning, PMLR 267 (2025). Reinforcement Learning (RL) agents can solve diverse tasks but often exhibit unsafe behavior. Constrained Markov Decision Processes (CMDPs) address this by enforcing safety constraints, yet existing methods either sacrifice reward maximization or…
Published · Probability Theory and Related Fields (2026). In the last few years it was proved that scalar passive quantities subject to suitable stochastic transport noise, and more recently that also vector passive quantities subject to suitable stochastic transport and…
The task of sampling from a probability density can be approached as transporting a tractable density function to the target, known as dynamical measure transport. In this work, we tackle it through a principled unified framework using deterministic or stochastic…
Published · Nonlinearity 38, 075023 (2025). In this paper, we prove the global well-posedness and interior regularity for the 2D Navier-Stokes equations driven by a fractional noise acting as an inhomogeneous Dirichlet-type boundary condition. The model describes a…
Published · SIAM Journal on Mathematical Analysis 57(3), 3071–3109 (2025). We derive quantitative estimates proving the conditional propagation of chaos for large stochastic systems of interacting particles subject to both idiosyncratic and common noise. We obtain explicit bounds on the relative entropy…
We study the asymptotic properties of a stochastic model for the induction equations of the magnetic field in a three dimensional periodic domain. The turbulent velocity field driving the electromotive force on the magnetic field is modeled by a noise…
Published · Information and Inference 15(1), iaaf034 (2026). We study the error introduced by entropy regularization in infinite-horizon discrete discounted Markov decision processes. We show that this error decreases exponentially in the inverse regularization strength, both in a weighted KL-divergence…
Published · Advances in Neural Information Processing Systems (NeurIPS 2024). Physics-informed neural networks (PINNs) are infamous for being hard to train. Recently, second-order methods based on natural gradient and Gauss-Newton methods have shown promising performance, improving the accuracy achieved by first-order methods…
Published · Annals of Applied Probability 36(2), 1690–1736 (2026). We derive weak and strong well-posedness for one-dimensional stochastic differential equations with measure-valued drift driven by a symmetric stable process…
Published · SIAM Journal on Optimization 35(2) (2025). Kakade’s natural policy gradient method has been studied extensively in recent years, showing linear convergence with and without regularization. We study another natural gradient method based on the Fisher information matrix of…
Published · Communications in Mathematical Physics 407, 158 (2026). We consider strictly positive solutions to a class of fourth-order conservative quasilinear SPDEs on the $d$-dimensional torus modeled after the stochastic thin-film equation. We prove local Lipschitz estimates in Bessel potential spaces…
