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Extended decorations on naturally decorated trees were introduced in the work of Bruned, Hairer and Zambotti on algebraic renormalization of regularity structures to provide a convenient framework for the renormalization of systems of…

Probability · Mathematics 2021-02-03 Ismael Bailleul , Yvain Bruned

In this article, we develop a comprehensive ODE-theory for structured continuity equations in fibred probability spaces, which represent a class of heterogeneous PDEs arising as the meanfield limit nonexchangeable particle systems. After…

Analysis of PDEs · Mathematics 2025-11-26 Benoît Bonnet-Weill , Nastassia Pouradier Duteil

We prove local existence of solutions for a class of suitably renormalised coupled SPDE-ODE systems driven by space-time white noise, where the space dimension is equal to 2 or 3. This class includes in particular the FitzHugh-Nagumo system…

Probability · Mathematics 2018-05-10 Nils Berglund , Christian Kuehn

Schauder theory is a basic tool in the study of elliptic and parabolic PDEs, asserting that solutions inherit the regularity of the coefficients. It plays a central role in establishing higher regularity for solutions to a broad class of…

Analysis of PDEs · Mathematics 2025-09-16 Cristiana De Filippis

In this paper, we show a series of abstract results on fixed point regularity with respect to a parameter. They are based on a Taylor development taking into account a loss of regularity phenomenon, typically occurring for composition…

Dynamical Systems · Mathematics 2018-04-04 Julien Sedro

We consider multiple stochastic integrals with respect to c\`adl\`ag martingales, which approximate a cylindrical Wiener process. We define a chaos expansion, analogous to the case of multiple Wiener stochastic integrals, for these…

Probability · Mathematics 2023-07-26 Paolo Grazieschi , Konstantin Matetski , Hendrik Weber

These notes are based on lectures held at the Lanzhou university (China) during a CIMPA summer school in july 2004 but benefit from recent devellopements. Our aim is to explain some perturbations technics that allow to study the long time…

Analysis of PDEs · Mathematics 2007-05-23 Benoit Grebert

This manuscript aims to provide a self-contained introduction to the regularity theory for elliptic PDE, focusing on the main ideas rather than proving all results in their greatest generality. It can be seen as a bridge between an…

Analysis of PDEs · Mathematics 2023-01-05 Xavier Fernández-Real , Xavier Ros-Oton

We introduce a general framework allowing to apply the theory of regularity structures to discretisations of stochastic PDEs. The approach pursued in this article is that we do not focus on any one specific discretisation procedure.…

Probability · Mathematics 2024-04-15 Dirk Erhard , Martin Hairer

A theory of structure is formulated for systems of many structureless classical particles with stable local interactions in Euclidean space. Such systems are shown to have their structure in thermodynamic equilibrium determined exactly by a…

Statistical Mechanics · Physics 2026-02-12 John Çamkıran , Fabian Parsch , Glenn D. Hibbard

A stochastic PDE, describing mesoscopic fluctuations in systems of weakly interacting inertial particles of finite volume, is proposed and analysed in any finite dimension $d\in\mathbb{N}$. It is a regularised and inertial version of the…

Analysis of PDEs · Mathematics 2021-02-10 Federico Cornalba , Tony Shardlow , Johannes Zimmer

We consider a class of weakly asymmetric continuous microscopic growth models with polynomial smoothing mechanisms, general nonlinearities and a Poisson type noise. We show that they converge to the KPZ equation after proper rescaling and…

Probability · Mathematics 2024-09-11 Fanhao Kong , Haiyi Wang , Weijun Xu

This article is focused on two related topics within the study of partial differential equations (PDEs) that illustrate a beautiful connection between dynamics, topology, and analysis: stability and spatial dynamics. The first is a property…

Dynamical Systems · Mathematics 2019-10-18 Margaret Beck

In this paper we revisit a fundamental technical issue within the theory of stochastic approximation (SA) in a Markovian framework, first proposed in the book by Djereveckii and Fradkov (1981), and further developed in much detail in the…

Pathwise uniqueness for stochastic PDEs with drift in differential form is a main open problem in the recent literature on regularisation by noise. This paper establishes a self-contained theory in the framework of stochastic evolution…

Probability · Mathematics 2025-12-22 Davide Addona , Davide Bignamini , Carlo Orrieri , Luca Scarpa

Stochastic differential equations (SDEs) are a ubiquitous modeling framework that finds applications in physics, biology, engineering, social science, and finance. Due to the availability of large-scale data sets, there is growing interest…

Machine Learning · Statistics 2025-03-04 Ziheng Guo , James Greene , Ming Zhong

We obtain (small-parameter) well-posedness for the (space-time periodic) $\Phi^4$ equation in the full subcritical regime in the context of regularity structures based on multi-indices. As opposed to Hairer's more extrinsic tree-based…

Analysis of PDEs · Mathematics 2025-03-04 Lucas Broux , Felix Otto , Rhys Steele

We study a class of nonlinear Burgers-type stochastic partial differential equations driven by additive space-time white noise in one spatial dimension. Building on the rough path framework initiated by Hairer, which provides a pathwise…

Probability · Mathematics 2026-01-26 Nannan Li , Xing Gao

We give a motivation and gentle introduction into the regularity structure and model introduced by Otto, Sauer, Smith and Weber, which fall into the framework of Hairer, but have a greedier index set than the one given by trees. We do this…

Analysis of PDEs · Mathematics 2022-07-22 Pablo Linares , Felix Otto

Deep neural networks trained in an end-to-end manner are proven to be efficient in a wide range of machine learning tasks. However, there is one drawback of end-to-end learning: The learned features and information are implicitly…

Machine Learning · Computer Science 2024-06-07 Canlin Zhang , Xiuwen Liu