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We will revisit the classical questions of understanding the statistics of various deterministic dynamics of $N$ hard spheres of diameter $\varepsilon$ with random initial data in the Boltzmann-Grad scaling as $\varepsilon$ tends to zero…

Analysis of PDEs · Mathematics 2024-05-08 Karsten Matthies , Theodora Syntaka

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

We prove the convergence of a particle method for the approximation of diffusive gradient flows in one dimension. This method relies on the discretisation of the energy via non-overlapping balls centred at the particles and preserves the…

Analysis of PDEs · Mathematics 2017-06-09 J. A. Carrillo , F. S. Patacchini , P. Sternberg , G. Wolansky

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

These are lecture notes for various Summer and Winter schools that I have given. The notes describe the methodology called Variational Modelling, and focus on the application to the modelling of gradient-flow systems. I describe the…

Mathematical Physics · Physics 2014-02-11 Mark A. Peletier

We discuss geometric formulations of hydrodynamic limits in diffusive systems. Specifically, we describe a geometrical construction in the space of density profiles --- the Wasserstein geometry --- which allows the deterministic…

Statistical Mechanics · Physics 2015-06-22 Robert L. Jack , Johannes Zimmer

The gradient-flow dynamics of an arbitrary geometric quantity is derived using a generalization of Darcy's Law. We consider flows in both Lagrangian and Eulerian formulations. The Lagrangian formulation includes a dissipative modification…

Adaptation and Self-Organizing Systems · Physics 2008-04-28 Darryl D. Holm , Vakhtang Putkaradze , Cesare Tronci

We study the quantitative convergence of drift-diffusion PDEs that arise as Wasserstein gradient flows of linearly convex functions over the space of probability measures on ${\mathbb R}^d$. In this setting, the objective is in general not…

Optimization and Control · Mathematics 2025-07-17 Lénaïc Chizat , Maria Colombo , Xavier Fernández-Real

The energy gradient theory was proposed in our previous studies. The mechanism of flow instability is very different in shear driven flows from pressure driven flows. In present paper, the relationship for the energy variation, work done,…

Fluid Dynamics · Physics 2020-09-22 Hua-Shu Dou

We derive the incompressible Euler equations with heat convection with the no-penetration boundary condition from the Boltzmann equation with the diffuse boundary in the hydrodynamic limit for the scale of large Reynold number. Inspired by…

Analysis of PDEs · Mathematics 2021-04-07 Yunbai Cao , Juhi Jang , Chanwoo Kim

The two-dimensional ideal (Euler) fluids can be described by the classical fields of streamfunction, velocity and vorticity and, in an equivalent manner, by a model of discrete point-like vortices interacting in plane by a self-generated…

Fluid Dynamics · Physics 2010-01-05 Florin Spineanu , Madalina Vlad

The gradient expansion is the fundamental organising principle underlying relativistic hydrodynamics, yet understanding its convergence properties for general nonlinear flows has posed a major challenge. We introduce a simple method to…

High Energy Physics - Theory · Physics 2022-04-06 Michal P. Heller , Alexandre Serantes , Michał Spaliński , Viktor Svensson , Benjamin Withers

Arnold showed that the Euler equations of an ideal fluid describe geodesics on the Lie algebra of incompressible vector fields. We generalize this to fluids with dissipation and Gaussian random forcing. The dynamics is determined by the…

Mathematical Physics · Physics 2015-05-18 S. G. Rajeev

Wasserstein gradient flows have become a central tool for optimization problems over probability measures. A natural numerical approach is forward-Euler time discretization. We show, however, that even in the simple case where the energy…

Numerical Analysis · Mathematics 2025-10-16 Yewei Xu , Qin Li

The Euler-Maxwell system as a hydrodynamic model for plasma physics to describe the dynamics of the compressible electrons in a constant charged non-moving ion background is studied. The global smooth flow with small amplitude is…

Analysis of PDEs · Mathematics 2011-07-12 Renjun Duan

Variational inference is a technique that approximates a target distribution by optimizing within the parameter space of variational families. On the other hand, Wasserstein gradient flows describe optimization within the space of…

Machine Learning · Statistics 2023-11-01 Mingxuan Yi , Song Liu

Numerical solution of Euler-Euler model using different in-house, open source and commercial software can generate significantly different results, even when the governing equations and the initial and boundary conditions are exactly same.…

Fluid Dynamics · Physics 2024-06-03 Yige Liu , Mingming He , Jianhua Chen , Wen Li , Bidan Zhao , Ji Xu , Junwu Wang

We provide an estimation of the dissipation of the Wasserstein 2 distance between the law of some interacting $N$-particle system, and the $N$ times tensorized product of solution to the corresponding limit nonlinear conservation law. It…

Analysis of PDEs · Mathematics 2018-10-23 Samir Salem

The fate of small particles in turbulent flows depends strongly on the surrounding fluid's velocity gradient properties such as rotation and strain-rates. For non-inertial (fluid) particles, the Restricted Euler model provides a simple,…

Fluid Dynamics · Physics 2017-04-05 Perry L. Johnson , Charles Meneveau

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