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We introduce a technique to merge two biased Brownian motions into a single regular process. The outcome follows a stochastic differential equation with a constant diffusion coefficient and a non-linear drift. The emerging stochastic…

Probability · Mathematics 2023-04-03 Miquel Montero

We present a theoretical analysis of some popular adaptive Stochastic Gradient Descent (SGD) methods in the small learning rate regime. Using the stochastic modified equations framework introduced by Li et al., we derive effective…

Machine Learning · Statistics 2025-09-29 Luca Callisti , Marco Romito , Francesco Triggiano

Stochastic unravelings allow to efficiently simulate open system dynamics, yet their application has traditionally been restricted to master equations that preserve both Hermiticity and trace. In this work, we introduce a general framework…

We consider a one-dimensional gas of hard rods, one of the simplest examples of an interacting integrable model. It is well known that the hydrodynamics of such integrable models can be understood by viewing the system as a gas of…

Statistical Mechanics · Physics 2025-10-23 Seema Chahal , Indranil Mukherjee , Abhishek Dhar , Herbert Spohn , Anupam Kundu

We develop a framework for the stochastic thermodynamics of a probe coupled to a fluctuating medium with spatio-temporal correlations, described by a scalar field. For a Brownian particle dragged by a harmonic trap through a fluctuating…

Statistical Mechanics · Physics 2024-05-02 Davide Venturelli , Sarah A. M. Loos , Benjamin Walter , Édgar Roldán , Andrea Gambassi

Exploring the intersection of deterministic and stochastic dynamics, this paper delves into Lagrangian discovery for conservative and non-conservative systems under stochastic excitation. Traditional Lagrangian frameworks, adept at…

Dynamical Systems · Mathematics 2024-02-28 Tapas Tripura , Satyam Panda , Budhaditya Hazra , Souvik Chakraborty

We develop further ideas on how to construct low-dimensional models of stochastic dynamical systems. The aim is to derive a consistent and accurate model from the originally high-dimensional system. This is done with the support of centre…

chao-dyn · Physics 2008-02-03 Chao Xu , A. J. Roberts

The monograph is devoted to the study of stochastic area functionals of Brownian motions and of the associated heat kernels on Lie groups and Riemannian manifolds. It is essentially self-contained and as such can serve as a textbook on the…

Probability · Mathematics 2023-08-24 Fabrice Baudoin , Nizar Demni , Jing Wang

We present a numerical method for learning unknown nonautonomous stochastic dynamical system, i.e., stochastic system subject to time dependent excitation or control signals. Our basic assumption is that the governing equations for the…

Machine Learning · Computer Science 2025-03-04 Yuan Chen , Dongbin Xiu

We consider compressible pressureless fluid flows in Lagrangian coordinates in one space dimension. We assume that the fluid self-interacts through a force field generated by the fluid itself. We explain how this flow can be described by a…

Analysis of PDEs · Mathematics 2014-09-16 Yann Brenier , Wilfrid Gangbo , Giuseppe Savaré , Michael Westdickenberg

The classical method for deriving the macroscopic dynamics of a lattice Boltzmann system is to use a combination of different approximations and expansions. Usually a Chapman-Enskog analysis is performed, either on the continuous Boltzmann…

Statistical Mechanics · Physics 2010-12-30 David J. Packwood , Jeremy Levesley , Alexander N. Gorban

In these lecture notes, we explore the mathematical preliminaries and foundational concepts that connect stochastic processes with partial differential equations. We begin by investigating Brownian motion, which serves as a model for random…

Probability · Mathematics 2025-09-15 Helder Rojas

We consider stochastic flow on n-dimensional Euclidean space driven by fractional Brownian motion with Hurst parameter H greater than half, and study tangent flow and the growth of the Hausdorff measure of sub-manifolds of the ambient…

Probability · Mathematics 2008-08-05 Sreekar Vadlamani

Most physical systems are modelled by an ordinary or a partial differential equation, like the n-body problem in celestial mechanics. In some cases, for example when studying the long term behaviour of the solar system or for complex…

Probability · Mathematics 2016-08-16 Jacky Cresson , Sébastien Darses

We study stochastic thermodynamics of over-damped Brownian motion in a flowing fluid. Unlike some previous works, we treat the effects of the flow field as a non-conservational driving force acting on the Brownian particle. This allows us…

Statistical Mechanics · Physics 2024-04-23 Jun Wu , Mingnan Ding , Xiangjun Xing

Score-based diffusion models currently constitute the state of the art in continuous generative modeling. These methods are typically formulated via overdamped or underdamped Ornstein--Uhlenbeck-type stochastic differential equations, in…

Machine Learning · Computer Science 2025-12-22 Herlock Rahimi

The Schr\"odinger bridge problem is concerned with finding a stochastic dynamical system bridging two marginal distributions that minimises a certain transportation cost. This problem, which represents a generalisation of optimal transport…

Machine Learning · Computer Science 2026-03-03 Kirill Tamogashev , Nikolay Malkin

In this paper, we consider a stochastic version of the Cahn-Hilliard-Brinkman model in a smooth two- or three-dimensional domain with dynamical boundary conditions. The system describes creeping two-phase flows and is basically a coupling…

Probability · Mathematics 2026-01-13 Z. Brzeźniak , A. Ndongmo Ngana , T. Tachim Medjo

Fluctuating hydrodynamics based techniques have been developed in recent years for the simulation of Brownian motion of particles. These mesoscale simulation tools are viable approaches for problems where molecular dynamics simulations may…

Fluid Dynamics · Physics 2014-02-11 Yong Chen , Neelesh A. Patankar

This work is concerned with the dynamics of a class of slow-fast stochastic dynamical systems with non-Gaussian stable L\'evy noise with a scale parameter. Slow manifolds with exponentially tracking property are constructed, eliminating the…

Dynamical Systems · Mathematics 2017-07-18 Shenglan Yuan , Jianyu Hu , Xianming Liu , Jinqiao Duan