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We study ``nonlocal diffusion equations'' of the form \[ \partial_{t}\frac{d\rho_{t}}{d\pi}(x)+\int_{X}\left(\frac{d\rho_{t}}{d\pi}(x)-\frac{d\rho_{t}}{d\pi}(y)\right)\eta(x,y)d\pi(y)=0\qquad(\dagger) \] where $X$ is either $\mathbb{R}^{d}$…

Analysis of PDEs · Mathematics 2025-11-03 Andrew Warren

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

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

This paper studies the convergence properties of the inexact Jordan-Kinderlehrer-Otto (JKO) scheme and proximal-gradient algorithm in the context of Wasserstein spaces. The JKO scheme, a widely-used method for approximating solutions to…

Optimization and Control · Mathematics 2025-06-19 Simone Di Marino , Emanuele Naldi , Silvia Villa

The sliced-Wasserstein flow is an evolution equation where a probability density evolves in time, advected by a velocity field computed as the average among directions in the unit sphere of the optimal transport displacements from its 1D…

Optimization and Control · Mathematics 2024-05-13 Giacomo Cozzi , Filippo Santambogio

We prove existence of weak solutions of a fractional thin film type equation in any space dimension and for any order of the equation. The proof is based on a gradient flow technique in the space of Borel probability measures endowed with…

Analysis of PDEs · Mathematics 2020-07-02 Stefano Lisini

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

We study Fokker--Planck equations with symmetric, positive definite mobility matrices capturing diffusion in heterogeneous environments. A weighted Wasserstein metric is introduced for which these equations are gradient flows. This metric…

Optimization and Control · Mathematics 2025-05-19 Hailiang Liu , Athanasios E. Tzavaras

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

This work is the third part of a program initiated in arXiv:2111.13258, arXiv:2302.06571 aiming at the development of an intrinsic geometric well-posedness theory for Hamilton-Jacobi equations related to controlled gradient flow problems in…

Analysis of PDEs · Mathematics 2024-02-02 Giovanni Conforti , Richard C. Kraaij , Luca Tamanini , Daniela Tonon

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

In this paper, we study the stochastic Hamiltonian flow in Wasserstein manifold, the probability density space equipped with $L^2$-Wasserstein metric tensor, via the Wong--Zakai approximation. We begin our investigation by showing that the…

Probability · Mathematics 2021-12-01 Jianbo Cui , Shu Liu , Haomin Zhou

This article is concerned with the existence and the long time behavior of weak solutions to certain coupled systems of fourth-order degenerate parabolic equations of gradient flow type. The underlying metric is a Wasserstein-like…

Analysis of PDEs · Mathematics 2016-09-23 Daniel Matthes , Jonathan Zinsl

We consider a one-dimensional aggregation-diffusion equation, which is the gradient flow in the Wasserstein space of a functional with competing attractive-repulsive interactions. We prove that the fully deterministic particle…

Analysis of PDEs · Mathematics 2021-01-01 Sara Daneri , Emanuela Radici , Eris Runa

When propagating uncertainty in the data of differential equations, the probability laws describing the uncertainty are typically themselves subject to uncertainty. We present a sensitivity analysis of uncertainty propagation for…

Probability · Mathematics 2022-03-01 Oliver G. Ernst , Alois Pichler , Björn Sprungk

Wasserstein gradient flow provides a general framework for minimizing an energy functional $J$ over the space of probability measures on a Riemannian manifold $(M,g)$. Its canonical time-discretization, the Jordan-Kinderlehrer-Otto (JKO)…

Machine Learning · Statistics 2026-03-05 Peter Halmos , Boris Hanin

It is well known that nonlinear diffusion equations can be interpreted as a gradient flow in the space of probability measures equipped with the Euclidean Wasserstein distance. Under suitable convexity conditions on the nonlinearity, due to…

Analysis of PDEs · Mathematics 2014-02-13 François Bolley , José A. Carrillo

A relaxed notion of displacement convexity is defined and used to establish short time existence and uniqueness of Wasserstein gradient flows for higher order energy functionals. As an application, local and global well-posedness of…

Analysis of PDEs · Mathematics 2012-01-18 Ehsan Kamalinejad

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

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