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As a counterpoint to classical stochastic particle methods for diffusion, we develop a deterministic particle method for linear and nonlinear diffusion. At first glance, deterministic particle methods are incompatible with diffusive partial…

Analysis of PDEs · Mathematics 2019-03-05 José Antonio Carrillo , Katy Craig , Francesco S. Patacchini

In the present work, we develop a novel particle method for a general class of mean field control problems, with source and terminal constraints. Specific examples of the problems we consider include the dynamic formulation of the…

Optimization and Control · Mathematics 2025-08-27 Katy Craig , Karthik Elamvazhuthi , Harlin Lee

Motivated by recent work on approximation of diffusion equations by deterministic interacting particle systems, we develop a nonlocal approximation for a range of linear and nonlinear diffusion equations and prove convergence of the method…

Analysis of PDEs · Mathematics 2024-04-05 Katy Craig , Matt Jacobs , Olga Turanova

This paper deals with the deterministic particle method for the equation of porous media (with p = 2). We establish a convergence rate in the Wasserstein-2 distance between the approximate solution of the associated nonlinear transport…

Analysis of PDEs · Mathematics 2025-02-03 Amina Amassad , Datong Zhou

The stochastic motions of a diffusing particle contain information concerning the particle's interactions with binding partners and with its local environment. However, accurate determination of the underlying diffusive properties, beyond…

Biological Physics · Physics 2016-12-21 Peter K. Koo , Simon G. J. Mochrie

Mathematical models based on probability density functions (PDF) have been extensively used in hydrology and subsurface flow problems, to describe the uncertainty in porous media properties (e.g., permeability modelled as random field).…

Fluid Dynamics · Physics 2020-06-01 Matteo Icardi , Marco Dentz

Devising optimal interventions for diffusive systems often requires the solution of the Hamilton-Jacobi-Bellman (HJB) equation, a nonlinear backward partial differential equation (PDE), that is, in general, nontrivial to solve. Existing…

Statistical Mechanics · Physics 2022-10-18 Dimitra Maoutsa , Manfred Opper

In this article, we introduce a new method for discretizing micro-macro models of dilute polymeric fluids by integrating a finite element discretization for the macroscopic fluid dynamic equation with a deterministic variational particle…

Numerical Analysis · Mathematics 2025-07-24 Xuelian Bao , Chun Liu , Yiwei Wang

We present a class of diffusion-based algorithms to draw samples from high-dimensional probability distributions given their unnormalized densities. Ideally, our methods can transport samples from a Gaussian distribution to a specified…

Machine Learning · Computer Science 2025-02-04 Anand Jerry George , Nicolas Macris

We develop an inertial coupling method for modeling the dynamics of point-like 'blob' particles immersed in an incompressible fluid, generalizing previous work for compressible fluids. The coupling consistently includes excess (positive or…

Soft Condensed Matter · Physics 2015-08-06 F. Balboa Usabiaga , R. Delgado-Buscalioni , B. E. Griffith , A. Donev

We present the Fokker-Planck equation (FPE) for an inhomogeneous medium with a position-dependent mass particle by making use of the Langevin equation, in the context of a generalized deformed derivative for an arbitrary deformation space…

Statistical Mechanics · Physics 2020-12-17 Bruno G. da Costa , Ignacio S. Gomez , Ernesto P. Borges

In this paper we consider a new probability sampling methods based on Langevin diffusion dynamics to resolve the problem of existing Monte Carlo algorithms when draw samples from high dimensional target densities. We extent…

Machine Learning · Computer Science 2025-03-31 Z. Zarezadeh , N. Zarezadeh

We consider the problem of making nonparametric inference in a class of multi-dimensional diffusions in divergence form, from low-frequency data. Statistical analysis in this setting is notoriously challenging due to the intractability of…

Methodology · Statistics 2025-01-23 Matteo Giordano , Sven Wang

Score-based diffusion models have proven effective in image generation and have gained widespread usage; however, the underlying factors contributing to the performance disparity between stochastic and deterministic (i.e., the probability…

Machine Learning · Computer Science 2024-03-19 Yuji Hirono , Akinori Tanaka , Kenji Fukushima

The method of choice for integrating the time-dependent Fokker-Planck equation in high-dimension is to generate samples from the solution via integration of the associated stochastic differential equation. Here, we study an alternative…

Machine Learning · Computer Science 2023-02-17 Nicholas M. Boffi , Eric Vanden-Eijnden

We introduce a guided stochastic sampling method that augments sampling from diffusion models with physics-based guidance derived from partial differential equation (PDE) residuals and observational constraints, ensuring generated samples…

Machine Learning · Computer Science 2026-05-28 Andrew Millard , Fredrik Lindsten , Zheng Zhao

We present an explicit method for simulating stochastic differential equations (SDEs) that have variable diffusion coefficients and satisfy the detailed balance condition with respect to a known equilibrium density. In Tupper and Yang…

Numerical Analysis · Mathematics 2014-06-27 Paul Tupper , Xin Yang

We present a model of diffusion in heterogeneous environment, which qualitatively reflects the transport properties of a polymeric membrane with carbon nanotubes. We derived Fokker-Planck equation from system of stochastic equations,…

Materials Science · Physics 2020-08-19 Ilia Kalashnikov , Polina Likhomanova

The well-posedness of a non-local advection-selection-mutation problem deriving from adaptive dynamics models is shown for a wide family of initial data. A particle method is then developed, in order to approximate the solution of such…

Numerical Analysis · Mathematics 2023-04-28 Frank Ernesto Alvarez , Jules Guilberteau

Diffusion processes with boundaries are models of transport phenomena with wide applicability across many fields. These processes are described by their probability density functions (PDFs), which often obey Fokker-Planck equations (FPEs).…

Probability · Mathematics 2019-09-25 Haozhe Shan , Rubén Moreno-Bote , Jan Drugowitsch
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