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For several classes of second order dispersionless PDEs, we show that the symbols of their formal linearizations define conformal structures which must be Einstein-Weyl in 3D (or self-dual in 4D) if and only if the PDE is integrable by the…

Mathematical Physics · Physics 2015-03-11 Eugene Ferapontov , Boris Kruglikov

This paper contains two contributions in the study of optimal transport on metric graphs. Firstly, we prove a Benamou-Brenier formula for the Wasserstein distance, which establishes the equivalence of static and dynamical optimal transport.…

Analysis of PDEs · Mathematics 2022-05-02 Matthias Erbar , Dominik Forkert , Jan Maas , Delio Mugnolo

We consider discrete porous medium equations of the form \partial_t \rho_t = \Delta \phi(\rho_t), where \Delta is the generator of a reversible continuous time Markov chain on a finite set X, and \phi is an increasing function. We show that…

Functional Analysis · Mathematics 2012-12-06 Matthias Erbar , Jan Maas

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 propose a spatial discretization of the fourth-order nonlinear DLSS equation on the circle. Our choice of discretization is motivated by a novel gradient flow formulation with respect to a metric that generalizes martingale transport.…

Analysis of PDEs · Mathematics 2025-02-14 Daniel Matthes , Eva-Maria Rott , Giuseppe Savaré , André Schlichting

We propose a mathematically principled PDE gradient flow framework for distributionally robust optimization (DRO). Exploiting the recent advances in the intersection of Markov Chain Monte Carlo sampling and gradient flow theory, we show…

Optimization and Control · Mathematics 2026-05-27 Zusen Xu , Jia-Jie Zhu

Many equations that model fluid behaviour are derived from systems that encompass multiple physical forces. When the equations are written in non dimensional form appropriate to the physics of the situation, the resulting partial…

Analysis of PDEs · Mathematics 2020-11-18 Susan Friedlander , Anthony Suen

We establish a comparison principle for viscosity solutions of a class of nonlinear partial differential equations posed on the space of nonnegative finite measures, thereby extending recent results for PDEs defined on the Wasserstein space…

Probability · Mathematics 2026-05-05 Ibrahim Ekren , Xihao He , Tianxu Lan , Xiaolu Tan

The sliced Wasserstein flow (SWF), a nonparametric and implicit generative gradient flow, is transformed into a Liouville partial differential equation (PDE)-based formalism. First, the stochastic diffusive term from the Fokker-Planck…

Machine Learning · Statistics 2026-05-12 Jayshawn Cooper , Pilhwa Lee

Our purpose is to obtain gradient estimates for certain nonlinear partial differential equations by coupling methods. First we derive uniform gradient estimates for a certain semi-linear PDEs based on the coupling method introduced in Wang…

Probability · Mathematics 2014-07-22 Yongsheng Song

We introduce a new theory of generalised solutions which applies to fully nonlinear PDE systems of any order and allows for merely measurable maps as solutions. This approach bypasses the standard problems arising by the application of…

Analysis of PDEs · Mathematics 2017-02-21 Nikos Katzourakis

Using probabilistic methods, we establish a-priori estimates for two classes of quasilinear parabolic systems of partial differential equations (PDEs). We treat in particular the case of a nonlinearity which has quadratic growth in the…

Probability · Mathematics 2023-04-05 Joe Jackson

We analyze a diffuse interface model for multi-phase flows of $N$ incompressible, viscous Newtonian fluids with different densities. In the case of a bounded and sufficiently smooth domain existence of weak solutions in two and three space…

Analysis of PDEs · Mathematics 2024-01-15 Helmut Abels , Harald Garcke , Andrea Poiatti

Learning conditional densities and identifying factors that influence the entire distribution are vital tasks in data-driven applications. Conventional approaches work mostly with summary statistics, and are hence inadequate for a…

Methodology · Statistics 2022-09-13 Chengliang Tang , Nathan Lenssen , Ying Wei , Tian Zheng

In this work, we develop a method for learning interpretable, thermodynamically stable and Galilean invariant partial differential equations (PDEs) based on the Conservation-dissipation Formalism of irreversible thermodynamics. As governing…

Computational Physics · Physics 2021-10-13 Juntao Huang , Zhiting Ma , Yizhou Zhou , Wen-An Yong

We develop a gradient-flow framework based on the Wasserstein metric for a parabolic moving-boundary problem that models crystal dissolution and precipitation. In doing so we derive a new weak formulation for this moving-boundary problem…

Mathematical Physics · Physics 2010-03-12 Jacobus W. Portegies , Mark A. Peletier

We study a non-local version of the Cahn-Hilliard dynamics for phase separation in a two-component incompressible and immiscible mixture with linear mobilities. In difference to the celebrated local model with nonlinear mobility, it is only…

Analysis of PDEs · Mathematics 2019-03-07 Clément Cancès , Daniel Matthes , Flore Nabet

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

Over the centuries mathematicians have been challenged by the partial differential equations (PDEs) that describe the motion of fluids in many physical contexts. Important and beautiful results were obtained in the past one hundred years,…

Analysis of PDEs · Mathematics 2023-07-05 Alexey Cheskidov , Mimi Dai , Susan Friedlander

We consider a class of time-homogeneous diffusion processes on $\mathbb{R}^{n}$ with common invariant measure but varying volatility matrices. In Euclidean space, we show via stochastic control of the diffusion coefficient that the…

Probability · Mathematics 2023-10-31 Bertram Tschiderer