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We propose a new scheme for the long time approximation of a diffusion when the drift vector field is not globally Lipschitz. Under this assumption, regular explicit Euler scheme --with constant or decreasing step-- may explode and implicit…

Probability · Mathematics 2018-02-20 Vincent Lemaire

We establish weak convergence rates for noise discretizations of a wide class of stochastic evolution equations with non-regularizing semigroups and additive or multiplicative noise. This class covers the nonlinear stochastic wave, HJMM,…

Probability · Mathematics 2019-04-10 Philipp Harms , Marvin S. Müller

This article deals with the weak and strong unique continuation principle for fractional Schr\"odinger equations with scaling-critical and rough potentials via Carleman estimates. Our methods allow to apply the results to variable…

Analysis of PDEs · Mathematics 2016-06-29 Angkana Rüland

We consider the empirical measures of multi-type voter models with mutation on large finite sets, and prove their weak atomic convergence in the sense of Ethier and Kurtz (1994) toward a Fleming-Viot process. Convergence in the weak atomic…

Probability · Mathematics 2016-08-23 Yu-Ting Chen , J. Theodore Cox

We propose a discrete time discrete space Markov chain approximation with a Brownian bridge correction for computing curvilinear boundary crossing probabilities of a general diffusion process on a finite time interval. For broad classes of…

Probability · Mathematics 2021-12-13 Vincent Liang , Konstantin Borovkov

We revisit the work of Mitter and Newton on an information-theoretic interpretation of Bayes' formula through the Gibbs variational principle. This formulation allowed them to pose nonlinear estimation for diffusion processes as a problem…

Optimization and Control · Mathematics 2024-05-02 Maxim Raginsky

Discrete flow models (DFMs) have been proposed to learn the data distribution on finite state space, offering a flexible framework as an alternative to discrete diffusion models. A line of recent work has studied samplers for discrete…

Machine Learning · Statistics 2026-05-28 Zhengyan Wan , Yidong Ouyang , Liyan Xie , Hongyuan Zha , Fang Fang , Guang Cheng

This paper is concerned with the weak solution for the fast diffusion equation with absorption and singularity in the form of $u_t=\triangle u^m -u^p$. We first prove the existence and decay estimate of weak solution when the fast diffusion…

Analysis of PDEs · Mathematics 2024-05-14 Changping Xie , Shaomei Fang , Ming Mei , Yuming Qin

We address the long time behaviour of weakly interacting diffusive particle systems on the d-dimensional torus. Our main result is to show that, under certain regularity conditions, the weak error between the empirical distribution of the…

Analysis of PDEs · Mathematics 2025-05-13 François Delarue , Alvin Tse

In the present paper we consider the semiclassical magnetic Schr\"odinger equation, which describes the dynamics of particles under the influence of a magnetic field. The solution of the time-dependent Schr\"odinger equation is approximated…

Numerical Analysis · Mathematics 2023-10-26 Selina Burkhard , Benjamin Dörich , Marlis Hochbruck , Caroline Lasser

Diffuse domain methods (DDMs) have gained significant attention for solving partial differential equations (PDEs) on complex geometries. These methods approximate the domain by replacing sharp boundaries with a diffuse layer of thickness…

Analysis of PDEs · Mathematics 2025-04-25 Toai Luong , Tadele Mengesha , Steven M. Wise , Ming Hei Wong

We consider a finite element approximation of a general semi-linear stochastic partial differential equation (SPDE) driven by space-time multiplicative and additive noise. We examine the full weak convergence rate of the exponential Euler…

Numerical Analysis · Mathematics 2015-07-28 Antoine Tambue , Jean Medard T. Ngnotchouye

We consider the weak convergence of numerical methods for stochastic differential equations (SDEs). Weak convergence is usually expressed in terms of the convergence of expected values of test functions of the trajectories. Here we present…

Numerical Analysis · Mathematics 2009-11-28 Benoit Charbonneau , Yuriy Svyrydov , P. F. Tupper

We consider the Schr\"odinger bridge problem which, given ensemble measurements of the initial and final configurations of a stochastic dynamical system and some prior knowledge on the dynamics, aims to reconstruct the "most likely"…

Machine Learning · Statistics 2026-02-04 Stephen Y. Zhang , Michael P H Stumpf

The characterization of particle diffusion is a classical problem in physics and probability theory. The field of microrheology is based on experiments in which microscopic tracer beads are placed into a non-Newtonian fluid and tracked…

Methodology · Statistics 2012-07-03 Gustavo Didier , John Fricks

We consider random Schr\"odinger equations on $\bR^d$ for $d\ge 3$ with a homogeneous Anderson-Poisson type random potential. Denote by $\lambda$ the coupling constant and $\psi_t$ the solution with initial data $\psi_0$. The space and time…

Mathematical Physics · Physics 2007-05-23 Laszlo Erdos , Manfred Salmhofer , Horng-Tzer Yau

This paper is devoted to the numerical analysis of a piecewise constant discontinuous Galerkin method for time fractional subdiffusion problems. The regularity of weak solution is firstly established by using variational approach and…

Numerical Analysis · Mathematics 2022-02-22 Binjie Li , Hao Luo , Xiaoping Xie

We prove a weak rate of convergence of a fully discrete scheme for stochastic Cahn--Hilliard equation with additive noise, where the spectral Galerkin method is used in space and the backward Euler method is used in time. Compared with the…

Numerical Analysis · Mathematics 2023-03-21 Meng Cai , Siqing Gan , Yaozhong Hu

Schr\"odinger bridges (SBs) provide an elegant framework for modeling the temporal evolution of populations in physical, chemical, or biological systems. Such natural processes are commonly subject to changes in population size over time…

Machine Learning · Computer Science 2023-06-16 Matteo Pariset , Ya-Ping Hsieh , Charlotte Bunne , Andreas Krause , Valentin De Bortoli

Practical diffusion sampling is a numerical approximation problem: under a fixed inference budget, one must simulate a reverse-time ODE or SDE using only a limited number of denoising steps, so discretization error is often the dominant…

Machine Learning · Computer Science 2026-05-12 Samuel Hurault , Thomas Moreau , Gabriel Peyré