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We propose a variational finite volume scheme to approximate the solutions to Wasserstein gradient flows. The time discretization is based on an implicit linearization of the Wasserstein distance expressed thanks to Benamou-Brenier formula,…

Numerical Analysis · Mathematics 2019-07-22 Clément Cancès , Thomas O. Gallouët , Gabriele Todeschi

In this work, we investigate a variational formulation for a time-fractional Fokker-Planck equation which arises in the study of complex physical systems involving anomalously slow diffusion. The model involves a fractional-order Caputo…

Numerical Analysis · Mathematics 2020-06-05 Manh Hong Duong , Bangti Jin

A nonlinear diffusion equation, interpreted as a Wasserstein gradient flow, is numerically solved in one space dimension using a higher-order minimizing movement scheme based on the BDF (backward differentiation formula) discretization. In…

Numerical Analysis · Mathematics 2015-09-02 Bertram Düring , Philipp Fuchs , Ansgar Jüngel

We study the Fokker-Planck equation as the hydrodynamic limit of a stochastic particle system on one hand and as a Wasserstein gradient flow on the other. We write the rate functional, that characterizes the large deviations from the…

Analysis of PDEs · Mathematics 2012-03-29 Manh Hong Duong , Vaios Laschos , Michiel Renger

Wasserstein gradient flows are continuous time dynamics that define curves of steepest descent to minimize an objective function over the space of probability measures (i.e., the Wasserstein space). This objective is typically a divergence…

Optimization and Control · Mathematics 2021-02-23 Adil Salim , Anna Korba , Giulia Luise

Numerous infinite dimensional dynamical systems arising in different fields have been shown to exhibit a gradient flow structure in the Wasserstein space. We construct Two Point Flux Approximation Finite Volume schemes discretizing such…

Numerical Analysis · Mathematics 2020-06-29 Andrea Natale , Gabriele Todeschi

We introduce in this paper two time discretization schemes tailored for a range of Wasserstein gradient flows. These schemes are designed to preserve mass, positivity and to be uniquely solvable. In addition, they also ensure energy…

Numerical Analysis · Mathematics 2024-07-15 Shiheng Zhang , Jie Shen

We introduce a novel spatio-temporal discretization for nonlinear Fokker-Planck equations on the multi-dimensional unit cube. This discretization is based on two structural properties of these equations: the first is the representation as a…

Numerical Analysis · Mathematics 2016-01-11 Oliver Junge , Daniel Matthes , Horst Osberger

Wasserstein gradient flows provide a powerful means of understanding and solving many diffusion equations. Specifically, Fokker-Planck equations, which model the diffusion of probability measures, can be understood as gradient descent over…

Machine Learning · Computer Science 2021-10-26 Petr Mokrov , Alexander Korotin , Lingxiao Li , Aude Genevay , Justin Solomon , Evgeny Burnaev

We present a discretization-free scalable framework for solving a large class of mass-conserving partial differential equations (PDEs), including the time-dependent Fokker-Planck equation and the Wasserstein gradient flow. The main…

Machine Learning · Computer Science 2023-11-15 Lingxiao Li , Samuel Hurault , Justin Solomon

We propose a fully discrete finite volume scheme for the standard Fokker-Planck equation. The space discretization relies on the well-known square-root approximation, which falls into the framework of two-point flux approximations. Our time…

Analysis of PDEs · Mathematics 2024-10-07 Clément Cancès , Léonard Monsaingeon , Andrea Natale

The theory of Wasserstein gradient flows in the space of probability measures has made an enormous progress over the last twenty years. It constitutes a unified and powerful framework in the study of dissipative partial differential…

Analysis of PDEs · Mathematics 2022-01-17 Daniel Adams , Manh Hong Duong , Goncalo dos Reis

We propose a variational form of the BDF2 method as an alternative to the commonly used minimizing movement scheme for the time-discrete approximation of gradient flows in abstract metric spaces. Assuming uniform semi-convexity --- but no…

Analysis of PDEs · Mathematics 2017-12-25 Daniel Matthes , Simon Plazotta

Wasserstein gradient flows have become a central tool for optimization problems over probability measures. A natural numerical approach is forward-Euler time discretization. We show, however, that even in the simple case where the energy…

Numerical Analysis · Mathematics 2025-10-16 Yewei Xu , Qin Li

We propose a fully discrete variational scheme for nonlinear evolution equations with gradient flow structure on the space of finite Radon measures on an interval with respect to a generalized version of the Wasserstein distance with…

Numerical Analysis · Mathematics 2016-09-29 Jonathan Zinsl , Daniel Matthes

We propose a novel class of temporal high-order parametric finite element methods for solving a wide range of geometric flows of curves and surfaces. By incorporating the backward differentiation formulae (BDF) for time discretization into…

Numerical Analysis · Mathematics 2024-08-21 Wei Jiang , Chunmei Su , Ganghui Zhang

We propose a variational scheme for computing Wasserstein gradient flows. The scheme builds upon the Jordan--Kinderlehrer--Otto framework with the Benamou-Brenier's dynamic formulation of the quadratic Wasserstein metric and adds a…

Numerical Analysis · Mathematics 2020-07-15 Wuchen Li , Jianfeng Lu , Li Wang

We modify the JKO scheme, which is a time discretization of Wasserstein gradient flows, by replacing the Wasserstein distance with more general transport costs on manifolds. We show when the cost function has a mixed Hessian which defines a…

Analysis of PDEs · Mathematics 2024-02-28 Cale Rankin , Ting-Kam Leonard Wong

In this article we study a variational problem providing a way to extend for all times minimizing geodesics connecting two given probability measures, in the Wasserstein space. This is simply obtained by allowing for negative coefficients…

Optimization and Control · Mathematics 2025-05-06 Thomas O. Gallouët , Andrea Natale , Gabriele Todeschi

In this note, we examine the forward-Euler discretization for simulating Wasserstein gradient flows. We provide two counter-examples showcasing the failure of this discretization even for a simple case where the energy functional is defined…

Machine Learning · Statistics 2024-06-13 Yewei Xu , Qin Li
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