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We consider a nonlinear Fokker-Planck equation driven by a deterministic rough path which describes the conditional probability of a McKean-Vlasov diffusion with "common" noise. To study the equation we build a self-contained framework of…

Probability · Mathematics 2021-07-27 Michele Coghi , Torstein Nilssen

Langevin Dynamics has been extensively employed in global non-convex optimization due to the concentration of its stationary distribution around the global minimum of the potential function at low temperatures. In this paper, we propose to…

Optimization and Control · Mathematics 2023-05-22 Ryo Fujino

An approximation to the solution of a stochastic parabolic equation is constructed using the Galerkin approximation followed by the Wiener Chaos decomposition. The result is applied to the nonlinear filtering problem for the time…

Probability · Mathematics 2007-06-13 Sergey V. Lototsky

We study a nonlinear branching diffusion process in the sense of McKean, i.e., where particles are subjected to a mean-field interaction. We consider first a strong formulation of the problem and we provide an existence and uniqueness…

Probability · Mathematics 2024-09-12 Julien Claisse , Jiazhi Kang , Xiaolu Tan

A particle subject to a white noise external forcing moves like a Langevin process. Consider now that the particle is reflected at a boundary which restores a portion c of the incoming speed at each bounce. For c strictly smaller than the…

Probability · Mathematics 2011-03-16 Emmanuel Jacob

We consider the diffusion process and its approximation by Markov chain with nonlinear increasing trends. The usual parametrix method is not appliable because these models have unbounded trends. We describe a procedure that allows to…

Probability · Mathematics 2016-11-02 V. Konakov , A. Markova

We derive the spatially homogeneous Landau equation for Maxwellian molecules from a natural stochastic interacting particle system. More precisely, we control the relative entropy between the joint law of the particle system and the…

Analysis of PDEs · Mathematics 2024-10-01 José Antonio Carrillo , Xuanrui Feng , Shuchen Guo , Pierre-Emmanuel Jabin , Zhenfu Wang

We propose a method for inference on moderately high-dimensional, nonlinear, non-Gaussian, partially observed Markov process models for which the transition density is not analytically tractable. Markov processes with intractable transition…

Methodology · Statistics 2020-04-02 Joonha Park , Edward L. Ionides

In this paper, we derive a stability result for $L_1$ and $L_{\infty}$ perturbations of diffusions under weak regularity conditions on the coefficients. In particular, the drift terms we consider can be unbounded with at most linear growth,…

Probability · Mathematics 2021-04-02 Ilya Bitter , Valentin Konakov

A new method is proposed to numerically extract the diffusivity of a (typically nonlinear) diffusion equation from underlying stochastic particle systems. The proposed strategy requires the system to be in local equilibrium and have…

Statistical Mechanics · Physics 2018-05-09 Peter Embacher , Nicolas Dirr , Johannes Zimmer , Celia Reina

The present paper discusses the diffusion approximation of the linear Boltzmann equation in cases where the collision frequency is not uniformly large in the spatial domain. Our results apply for instance to the case of radiative transfer…

Analysis of PDEs · Mathematics 2015-03-23 Claude Bardos , Etienne Bernard , François Golse , Rémi Sentis

The solution to nonlinear Fokker-Planck equation is constructed in terms of the minimal Markov semigroup generated by the equation. The semigroup is obtained by a purely functional analytical method via Hille-Yosida theorem. The existence…

Mathematical Physics · Physics 2007-05-23 Hong Qian , Min Qian , Xiang Tang

Inspired by the modeling of grain growth in polycrystalline materials, we consider a nonlinear Fokker-Plank model, with inhomogeneous diffusion and with variable mobility parameters. We develop large time asymptotic analysis of such…

Analysis of PDEs · Mathematics 2022-06-24 Yekaterina Epshteyn , Chang Liu , Chun Liu , Masashi Mizuno

In this paper, we consider a nonlinear filtering model with observations driven by correlated Wiener processes and point processes. We first derive a Zakai equation whose solution is a unnormalized probability density function of the filter…

Numerical Analysis · Mathematics 2022-11-29 Fengshan Zhang , Yongkui Zou , Shimin Chai , Yanzhao Cao

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

We obtain non asymptotic bounds for the Monte Carlo algorithm associated to the Euler discretization of some diffusion processes. The key tool is the Gaussian concentration satisfied by the density of the discretization scheme. This…

Probability · Mathematics 2018-02-20 Vincent Lemaire , Stephane Menozzi

Discretization of continuous-time diffusion processes is a widely recognized method for sampling. However, it seems to be a considerable restriction when the potentials are often required to be smooth (gradient Lipschitz). This paper…

Computation · Statistics 2022-02-23 Dao Nguyen

We consider a class of nonlinear partial-differential equations, including the spatially homogeneous Fokker-Planck-Landau equation for Maxwell (or pseudo-Maxwell) molecules. Continuing the work of Fontbona-Gu\'erin-M\'el\'eard, we propose a…

Mathematical Physics · Physics 2008-11-18 Nicolas Fournier

We argue that the spatial discretization of the strongly nonlinear Lefever-Lejeune partial differential equation defines a nonlinear lattice that is physically relevant in the context of the nonlinear physics of ecosystems, modelling the…

Pattern Formation and Solitons · Physics 2024-06-26 Nikos I. Karachalios , Antonis Krypotos , Paris Kyriazopoulos

Diffusion models for continuous state spaces based on Gaussian noising processes are now relatively well understood from both practical and theoretical perspectives. In contrast, results for diffusion models on discrete state spaces remain…

Machine Learning · Computer Science 2026-04-02 Giovanni Conforti , Alain Durmus , Le-Tuyet-Nhi Pham , Gael Raoul
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