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Related papers: Non-local Wasserstein Geometry, Gradient Flows, an…

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We introduce a family of reversible fragmentating-coagulating processes of particles of varying size-scaled diffusivity with strictly local interaction on the real line as mathematically rigorous description of colloidal motion of fluids.…

Probability · Mathematics 2022-09-21 Vitalii Konarovskyi , Max von Renesse

Distance measures between graphs are important primitives for a variety of learning tasks. In this work, we describe an unsupervised, optimal transport based approach to define a distance between graphs. Our idea is to derive…

Computational Engineering, Finance, and Science · Computer Science 2024-04-11 Michael Scholkemper , Damin Kühn , Gerion Nabbefeld , Simon Musall , Björn Kampa , Michael T. Schaub

Recently, Deng et al. (2026) proposed Generative Modeling via Drifting (GMD), a novel framework for generative tasks. This note presents an analysis of GMD through the lens of Wasserstein Gradient Flows (WGF), i.e., the path of steepest…

Machine Learning · Computer Science 2026-05-22 Arthur Gretton , Li Kevin Wenliang , Alexandre Galashov , James Thornton , Valentin De Bortoli , Arnaud Doucet

We study a nonlinear, degenerate cross-diffusion model which involves two densities with two different drift velocities. A general framework is introduced based on its gradient flow structure in Wasserstein space to derive a notion of…

Analysis of PDEs · Mathematics 2018-03-20 Inwon Kim , Alpár R. Mészáros

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

We prove existence and uniqueness of solutions to a transport equation modelling vehicular traffic in which the velocity field depends non-locally on the downstream traffic density via a discontinuous anisotropic kernel. The result is…

Analysis of PDEs · Mathematics 2015-10-16 Paola Goatin , Francesco Rossi

We study transient nonequilibrium dynamics in Fisher-regularized Wasserstein gradient flows and identify a sign-changing cross-dissipation mechanism generated by the coupling between transport dissipation and Fisher-information geometry.…

Statistical Mechanics · Physics 2026-05-29 Michael Farmer , Abhinav Kochar , Yugyung Lee

This article establishes the cutoff phenomenon in the Wasserstein distance for systems of nonlinear ordinary differential equations with a unique coercive stable fixed point subject to general additive Markovian noise in the limit of small…

Probability · Mathematics 2023-05-05 Gerardo Barrera , Michael A. Högele , Juan Carlos Pardo

In this paper we establish for the first time local semiconvexity estimates for fully nonlinear equations and for obstacle problems driven by integro-differential operators with general kernels. Our proof is based on the Bernstein…

Analysis of PDEs · Mathematics 2024-11-05 Xavier Ros-Oton , Clara Torres-Latorre , Marvin Weidner

In this paper we present a numerical scheme for nonlinear continuity equations, which is based on the gradient flow formulation of an energy functional with respect to the quadratic transportation distance. It can be applied to a large…

Numerical Analysis · Mathematics 2016-11-23 José A. Carrillo , Helene Ranetbauer , Marie-Therese Wolfram

Thermodynamics serves as a universal means for studying physical systems from an energy perspective. In recent years, with the establishment of the field of stochastic and quantum thermodynamics, the ideas of thermodynamics have been…

Statistical Mechanics · Physics 2023-02-07 Tan Van Vu , Keiji Saito

This work considers the problem of computing distances between structured objects such as undirected graphs, seen as probability distributions in a specific metric space. We consider a new transportation distance (i.e. that minimizes a…

Machine Learning · Statistics 2019-05-14 Titouan Vayer , Laetitia Chapel , Rémi Flamary , Romain Tavenard , Nicolas Courty

We revisit the variational characterization of diffusion as entropic gradient flux and provide for it a probabilistic interpretation based on stochastic calculus. It was shown by Jordan, Kinderlehrer, and Otto that, for diffusions of…

Probability · Mathematics 2020-03-24 Ioannis Karatzas , Walter Schachermayer , Bertram Tschiderer

This paper presents existence and uniqueness results for a class of parabolic systems with non linear diffusion and nonlocal interaction. These systems can be viewed as regular perturbations of Wasserstein gradient flows. Here we extend…

Analysis of PDEs · Mathematics 2015-06-02 Maxime Laborde

The incompressible Navier-Stokes (NS) equation is known to govern the hydrodynamic limit of essentially any fluid and its rich non-linear structure has critical implications in both mathematics and physics. The employability of the methods…

High Energy Physics - Theory · Physics 2019-06-26 Shounak De , Sumit Dey , Bibhas Ranjan Majhi

The question of existence and properties of stationary solutions to Langevin equations driven by noise processes with stationary increments is discussed, with particular focus on noise processes of pseudo-moving-average type. On account of…

Probability · Mathematics 2011-07-15 Ole E. Barndorff-Nielsen , Andreas Basse-O'Connor

We propose a generalization of the Wasserstein distance of order 1 to the quantum states of $n$ qudits. The proposal recovers the Hamming distance for the vectors of the canonical basis, and more generally the classical Wasserstein distance…

Quantum Physics · Physics 2022-01-14 Giacomo De Palma , Milad Marvian , Dario Trevisan , Seth Lloyd

Problems of particle dynamics involving unsteady Stokes flows in confined geometries are typically harder to solve than their steady counterparts. Approximation techniques are often the only resort. Felderhof (see e.g. 2005, 2009b) has…

Fluid Dynamics · Physics 2018-04-04 Akarsh Simha , Jianyong Mo , Philip J. Morrison

A method for analyzing non-asymptotic guarantees of numerical discretizations of ergodic SDEs in Wasserstein-2 distance is presented by Sanz-Serna and Zygalakis in ``Wasserstein distance estimates for the distributions of numerical…

Machine Learning · Statistics 2024-12-03 Daniel Paulin , Peter A. Whalley

We investigate the Moreau-Yosida regularization and the associated proximal map in the context of discrete gradient flow for the 2-Wasserstein metric. Our main results are a stepwise contraction property for the proximal map and an "above…

Analysis of PDEs · Mathematics 2012-10-17 Eric A. Carlen , Katy Craig