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Related papers: Wasserstein Gradient Flows of semi-discret energie…

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Over the past fifteen years, the theory of Wasserstein gradient flows of convex (or, more generally, semiconvex) energies has led to advances in several areas of partial differential equations and analysis. In this work, we extend the…

Analysis of PDEs · Mathematics 2017-05-04 Katy Craig

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

In this paper we devote our attention to a class of weighted ultrafast diffusion equations arising from the problem of quantisation for probability measures. These equations have a natural gradient flow structure in the space of probability…

Analysis of PDEs · Mathematics 2019-01-30 Mikaela Iacobelli , Francesco Patacchini , Filippo Santambrogio

Diffusion-based models on continuous spaces have seen substantial recent progress through the mathematical framework of gradient flows, leveraging the Wasserstein-2 (${W}_2$) metric via the Jordan-Kinderlehrer-Otto (JKO) scheme. Despite the…

Machine Learning · Computer Science 2026-04-14 Dario Rancati , Jan Maas , Francesco Locatello

We analyze the gradient flow of a potential energy in the space of probability measures when we substitute the optimal transport geometry with a geometry based on Sinkhorn divergences, a debiased version of entropic optimal transport. This…

Analysis of PDEs · Mathematics 2025-11-19 Mathis Hardion , Hugo Lavenant

The Wasserstein gradient flow structure of the PDE system governing multiphase flows in porous media was recently highlighted in [C. Canc\`es, T. O. Gallou\"et, and L. Monsaingeon, {\it Anal. PDE} 10(8):1845--1876, 2017]. The model can thus…

Numerical Analysis · Mathematics 2018-07-23 Clément Cancès , Thomas O. Gallouët , Maxime Laborde , Léonard Monsaingeon

Minimizing functionals in the space of probability distributions can be done with Wasserstein gradient flows. To solve them numerically, a possible approach is to rely on the Jordan-Kinderlehrer-Otto (JKO) scheme which is analogous to the…

Machine Learning · Computer Science 2022-11-16 Clément Bonet , Nicolas Courty , François Septier , Lucas Drumetz

We study a system of drift-diffusion PDEs for a potentially infinite number of incompressible phases, subject to a joint pointwise volume constraint. Our analysis is based on the interpretation as a collection of coupled Wasserstein…

Analysis of PDEs · Mathematics 2024-11-22 Clément Cancès , Daniel Matthes , Ismael Medina , Bernhard Schmitzer

In this paper, we study higher-order-accurate-in-time minimizing movements schemes for Wasserstein gradient flows. We introduce a novel accelerated second-order scheme, leveraging the differential structure of the Wasserstein space in both…

Analysis of PDEs · Mathematics 2025-12-23 Raymond Chu , Matt Jacobs

Wasserstein Gradient Flow (WGF) describes the gradient dynamics of probability density within the Wasserstein space. WGF provides a promising approach for conducting optimization over the probability distributions. Numerically approximating…

Machine Learning · Computer Science 2024-06-04 Jaemoo Choi , Jaewoong Choi , Myungjoo Kang

The JKO scheme provides the discrete-in-time approximation for the solutions of evolutionary equations with Wasserstein gradient structure. We study a natural space-discretization of this scheme by restricting the minimization to the…

Analysis of PDEs · Mathematics 2025-04-21 Anastasiia Hraivoronska , Filippo Santambrogio

Wasserstein gradient flow has emerged as a promising approach to solve optimization problems over the space of probability distributions. A recent trend is to use the well-known JKO scheme in combination with input convex neural networks to…

Machine Learning · Computer Science 2022-07-26 Jiaojiao Fan , Qinsheng Zhang , Amirhossein Taghvaei , Yongxin Chen

We present a simple approach to study the one-dimensional pressureless Euler system via adhesion dynamics in the Wasserstein space of probability measures with finite quadratic moments. Starting from a discrete system of a finite number of…

Analysis of PDEs · Mathematics 2014-09-16 Luca Natile , Giuseppe Savaré

We reveal a precise mathematical framework about a new family of generative models which we call Gradient Flow Drifting. With this framework, we prove an equivalence between the recently proposed Drifting Model and the Wasserstein gradient…

Machine Learning · Computer Science 2026-03-12 Jiarui Cao , Zixuan Wei , Yuxin Liu

Flow-based generative models enjoy certain advantages in computing the data generation and the likelihood, and have recently shown competitive empirical performance. Compared to the accumulating theoretical studies on related score-based…

Machine Learning · Statistics 2025-06-30 Xiuyuan Cheng , Jianfeng Lu , Yixin Tan , Yao Xie

Gradient flows in the Wasserstein space have become a powerful tool in the analysis of diffusion equations, following the seminal work of Jordan, Kinderlehrer and Otto (JKO). The numerical applications of this formulation have been limited…

Numerical Analysis · Mathematics 2014-08-21 Jean-David Benamou , Guillaume Carlier , Quentin Mérigot , Edouard Oudet

This paper is devoted to existence and uniqueness results for classes of nonlinear diffusion equations (or systems) which may be viewed as regular perturbations of Wasserstein gradient flows. First, in the case. where the drift is a…

Analysis of PDEs · Mathematics 2015-05-07 Guillaume Carlier , Maxime Laborde

We study a singular-limit problem arising in the modelling of chemical reactions. At finite {\epsilon} > 0, the system is described by a Fokker-Planck convection-diffusion equation with a double-well convection potential. This potential is…

Analysis of PDEs · Mathematics 2014-09-16 Steffen Arnrich , Alexander Mielke , Mark A. Peletier , Giuseppe Savaré , Marco Veneroni

We study the particle method to approximate the gradient flow on the $L^p$-Wasserstein space. This method relies on the discretization of the energy introduced by [3] via nonoverlapping balls centered at the particles and preserves the…

Numerical Analysis · Mathematics 2025-01-08 Rong Lei

We analyze some parabolic PDEs with different drift terms which are gradient flows in the Wasserstein space and consider the corresponding discrete-in-time JKO scheme. We prove with optimal transport techniques how to control the L p and L…

Analysis of PDEs · Mathematics 2019-11-26 Simone Di Marino , Filippo Santambrogio
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