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In finite dimension, the long-time and metastable behavior of a gradient flow perturbated by a small Brownian noise is well understood. A similar situation arises when a Wasserstein gradient flow over a space of probability measure is…

Probability · Mathematics 2025-10-21 Pierre Monmarché

We study the gradient flow for a relaxed approximation to the Kullback-Leibler (KL) divergence between a moving source and a fixed target distribution. This approximation, termed the KALE (KL approximate lower-bound estimator), solves a…

Machine Learning · Statistics 2021-11-01 Pierre Glaser , Michael Arbel , Arthur Gretton

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

We establish the global-in-time existence of solutions of the Cauchy problem for the full Navier-Stokes equations for compressible heat-conducting flow in multidimensions with initial data that are large, discontinuous, spherically…

Analysis of PDEs · Mathematics 2022-08-11 Gui-Qiang G. Chen , Yucong Huang , Shengguo Zhu

Modeling observations as random distributions embedded within Wasserstein spaces is becoming increasingly popular across scientific fields, as it captures the variability and geometric structure of the data more effectively. However, the…

Statistics Theory · Mathematics 2026-04-08 François Bachoc , Alberto González-Sanz , Jean-Michel Loubes , Yisha Yao

Flow Matching, a promising approach in generative modeling, has recently gained popularity. Relying on ordinary differential equations, it offers a simple and flexible alternative to diffusion models, which are currently the…

Machine Learning · Statistics 2026-03-13 Lea Kunkel

What is the optimal way to approximate a high-dimensional diffusion process by one in which the coordinates are independent? This paper presents a construction, called the \emph{independent projection}, which is optimal for two natural…

Probability · Mathematics 2024-10-10 Daniel Lacker

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

We study the long time behavior of the Wasserstein gradient flow for an energy functional consisting of two components: particles are attracted to a fixed profile $\omega$ by means of an interaction kernel $\psi_a(z)=|z|^{q_a}$,and they…

Analysis of PDEs · Mathematics 2014-01-13 Marco Di Francesco , Massimo Fornasier , Jan-Christian Hütter , Daniel Matthes

This article is concerned with the existence of nonnegative weak solutions to a particular fourth-order partial differential equation: it is a formal gradient flow with respect to a generalized Wasserstein transportation distance with…

Analysis of PDEs · Mathematics 2016-03-07 Jonathan Zinsl

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

We present a computationally efficient framework, called $\texttt{FlowDRO}$, for solving flow-based distributionally robust optimization (DRO) problems with Wasserstein uncertainty sets while aiming to find continuous worst-case…

Machine Learning · Computer Science 2024-02-27 Chen Xu , Jonghyeok Lee , Xiuyuan Cheng , Yao Xie

Sampling a probability distribution with an unknown normalization constant is a fundamental problem in computational science and engineering. This task may be cast as an optimization problem over all probability measures, and an initial…

Machine Learning · Statistics 2024-09-12 Yifan Chen , Daniel Zhengyu Huang , Jiaoyang Huang , Sebastian Reich , Andrew M. Stuart

Score Distillation Sampling (SDS) and its variants have been widely used for text-to-3D generation by distilling 2D image diffusion priors. However, the standard SDS objective is prone to severe mode collapse, frequently yielding…

Computer Vision and Pattern Recognition · Computer Science 2026-05-12 Rohith Ramanan , A. N. Rajagopalan

Flow Matching has become a cornerstone of modern generative models like Stable Diffusion 3, largely due to the efficiency of its Rectified Flow (RF) variant. The success of RF hinges on iteratively learning straight trajectories, pushing…

Machine Learning · Computer Science 2026-05-19 Vansh Bansal , Saptarshi Roy , Purnamrita Sarkar , Alessandro Rinaldo

For free energies of the form \[ F(\mu) = E(\mu) + \sigma\int_\Omega \mu\log\mu\,dx, \quad \sigma > 0, \] we study the Wasserstein gradient flow, a continuity equation also known as mean-field Langevin dynamics, around a stationary state…

Optimization and Control · Mathematics 2026-03-17 Dante Kalise , Lucas M. Moschen , Grigorios A. Pavliotis

Motivated by a recent method for approximate solution of Fredholm equations of the first kind, we develop a corresponding method for a class of Fredholm equations of the \emph{second kind}. In particular, we consider the class of equations…

Computation · Statistics 2026-02-19 Francesca R. Crucinio , Adam M. Johansen

We study the emergence of gradient flows in Wasserstein distance as high friction limits of an abstract Euler flow generated by an energy functional. We develop a relative energy calculation that connects the Euler flow to the gradient flow…

Analysis of PDEs · Mathematics 2021-03-22 Corrado Lattanzio , Athanasios E. Tzavaras

For the natural initial conditions $L^1$ in the density field (more generally a positive bounded Radon measure) and $L^\infty$ in the velocity field we obtain global approximate solutions to the Cauchy problem for the 3-D systems of…

Analysis of PDEs · Mathematics 2014-06-03 Mathilde Colombeau

Purely dissipative evolution equations are often cast as gradient flow structures, $\dot{\mathbf{z}}=K(\mathbf{z})DS(\mathbf{z})$, where the variable $\mathbf{z}$ of interest evolves towards the maximum of a functional $S$ according to a…

Mathematical Physics · Physics 2015-11-18 Celia Reina , Johannes Zimmer
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