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We prove that a system of locally interacting diffusions carrying discrete masses, subject to an environmental noise and undergoing mass coagulation, converges to a system of Stochastic Partial Differential Equations (SPDEs) with…

Probability · Mathematics 2022-03-15 Franco Flandoli , Ruojun Huang

Driven by diverse applications, several recent models impose randomly switching boundary conditions on either a PDE or SDE. The purpose of this paper is to provide tools for calculating statistics of these models and to establish a…

Probability · Mathematics 2020-03-13 Sean D. Lawley

Diffusion models are loosely modelled based on non-equilibrium thermodynamics, where \textit{diffusion} refers to particles flowing from high-concentration regions towards low-concentration regions. In statistics, the meaning is quite…

Machine Learning · Computer Science 2023-12-19 Inga Strümke , Helge Langseth

We introduce an inferential framework for a wide class of semi-linear stochastic differential equations (SDEs). Recent work has shown that numerical splitting schemes can preserve critical properties of such types of SDEs, give rise to…

Computation · Statistics 2025-07-22 Shu Huang , Richard G. Everitt , Massimiliano Tamborrino , Adam M. Johansen

This paper studies homogenization of stochastic differential systems. The standard example of this phenomenon is the small mass limit of Hamiltonian systems. We consider this case first from the heuristic point of view, stressing the role…

Mathematical Physics · Physics 2018-08-16 Jeremiah Birrell , Jan Wehr

We present a new approach to the dynamics of interacting particles with reaction and diffusion. Starting from the underlying discrete stochastic jump process we derive a general field theory describing the dynamics of the density field,…

Statistical Mechanics · Physics 2009-11-11 Alexei Andreanov , Giulio Biroli , Jean-Philippe Bouchaud , Alexandre Lefevre

Parameter inference for stochastic differential equations is challenging due to the presence of a latent diffusion process. Working with an Euler-Maruyama discretisation for the diffusion, we use variational inference to jointly learn the…

Computation · Statistics 2018-05-15 Thomas Ryder , Andrew Golightly , A. Stephen McGough , Dennis Prangle

Simulation of stochastic spatially-extended systems is a challenging problem. The fundamental quantities in these models are individual entities such as molecules, cells, or animals, which move and react in a random manner. In big systems,…

Quantitative Methods · Quantitative Biology 2024-09-24 Tomás Alarcón , Natalia Briñas-Pascual , Juan Calvo , Pilar Guerrero , Daria Stepanova

We introduce a schematic non-linear diffusion model where density fluctuations induce a rich out of equilibrium dynamics. The properties of the model are studied by numerical simulations and analytically in a mean field approximation. At…

Statistical Mechanics · Physics 2008-07-03 Federico Corberi , Mario Nicodemi , Marina Piccioni , Antonio Coniglio

In this work I show how a diffusion-advection equation in three space-dimensions may have its advection term weakly limited to a velocity field localized to a moving curve. This is rigorously accomplished through the technique of…

Analysis of PDEs · Mathematics 2021-06-24 Colin Klaus

Stochastic simulation methods can be applied successfully to model exact spatio-temporally resolved reaction-diffusion systems. However, in many cases, these methods can quickly become extremely computationally intensive with increasing…

Quantitative Methods · Quantitative Biology 2016-04-29 Jonathan U. Harrison , Christian A. Yates

In this paper, an online multiscale model reduction method is presented for stochastic partial differential equations (SPDEs) with multiplicative noise, where the diffusion coefficient is spatially multiscale and the noise perturbation…

Numerical Analysis · Mathematics 2022-04-26 Lijian Jiang , Mengnan Li , Meng Zhao

We present a first-principles formalism for studying dynamical heterogeneities in glass forming liquids. Based on the Non-Equilibrium Self-Consistent Generalized Langevin Equation theory, we were able to describe the time-dependent local…

Soft Condensed Matter · Physics 2022-04-06 J. Lira-Escobedo , J. R. Velez-Cordero , Pedro E. Ramírez-González

Given an unconditional diffusion model targeting a joint model $\pi(x, y)$, using it to perform conditional simulation $\pi(x \mid y)$ is still largely an open question and is typically achieved by learning conditional drifts to the…

Machine Learning · Statistics 2025-02-21 Adrien Corenflos , Zheng Zhao , Simo Särkkä , Jens Sjölund , Thomas B. Schön

Langevin Dynamics is a Stochastic Differential Equation (SDE) central to sampling and generative modeling and is implemented via time discretization. Langevin Monte Carlo (LMC), based on the Euler-Maruyama discretization, is the simplest…

Machine Learning · Computer Science 2025-10-10 Saravanan Kandasamy , Dheeraj Nagaraj

While diffusion models have shown great success in image generation, their noise-inverting generative process does not explicitly consider the structure of images, such as their inherent multi-scale nature. Inspired by diffusion models and…

Computer Vision and Pattern Recognition · Computer Science 2023-04-14 Severi Rissanen , Markus Heinonen , Arno Solin

We expand on a recent study of a lattice model of interacting particles [Phys. Rev. Lett. 111, 110601 (2013)]. The adsorption isotherm and equilibrium fluctuations in particle number are discussed as a function of the interaction. Their…

Statistical Mechanics · Physics 2014-11-20 T. Becker , K. Nelissen , B. Cleuren , B. Partoens , C. Van den Broeck

Macroscopic theories of nucleation such as classical nucleation theory envision that clusters of the bulk stable phase form inside the bulk metastable phase. Molecular dynamics simulations are often used to elucidate nucleation mechanisms,…

Materials Science · Physics 2017-09-06 Bingqing Cheng , Michele Ceriotti

Score-based generative models based on stochastic differential equations (SDEs) achieve impressive performance in sampling from unknown distributions, but often fail to satisfy underlying constraints. We propose a constrained generative…

Machine Learning · Statistics 2025-10-29 Adam Nordenhög , Akash Sharma

We reconsider the problem of diffusion of particles at constant speed and present a generalization of the Telegrapher process to higher dimensional stochastic media ($d>1$), where the particle can move along $2^d$ directions. We derive the…

Disordered Systems and Neural Networks · Physics 2009-10-31 S. Anantha Ramakrishna , N. Kumar