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Flow matching has recently emerged as a flexible and efficient framework for generative modelling by learning deterministic transport dynamics between probability measures. In this work, we extend flow matching to the space of probability…

Machine Learning · Computer Science 2026-05-12 Moritz Piening , Richard Duong , Gabriele Steidl

In this paper, we establish a result for existence and uniqueness of stochastic differential equations on Riemannian manifolds, for regular inhomogeneous tensor coefficients with stochastic drift, under geometrical hypothesis on the…

Probability · Mathematics 2025-05-07 Matthias Rakotomalala

The default Gaussian latent in flow-based generative models poses challenges when learning certain distributions such as heavy-tailed ones. We introduce a general framework for learning data-adaptive latent distributions using…

Machine Learning · Statistics 2026-02-11 Jannis Chemseddine , Gregor Kornhardt , Richard Duong , Gabriele Steidl

We introduce a notion of stability for non-autonomous Hamiltonian flows on two-dimensional annular surfaces. This notion of stability is designed to capture the sustained twisting of particle trajectories. The main Theorem is applied to…

Analysis of PDEs · Mathematics 2024-08-30 Theodore D. Drivas , Tarek M. Elgindi , In-Jee Jeong

We obtain $q$-Wasserstein convergence rates in the invariance principle for nonuniformly hyperbolic flows, where $q\ge1$ depends on the degree of nonuniformity. Utilizing a martingale-coboundary decomposition for nonuniformly expanding…

Dynamical Systems · Mathematics 2025-11-07 Ian Melbourne , Zhe Wang

Wasserstein barycenters provide a principled approach for aggregating probability measures, while preserving the geometry of their ambient space. Existing discrete methods are not scalable as they assume access to the complete set of…

Machine Learning · Statistics 2026-03-10 Eduardo Fernandes Montesuma , Yassir Bendou , Mike Gartrell

Homogenisation theory has seen recent applications in deriving stochastic transport models for fluid dynamics. In this work, we first derive the stochastic Lagrange-to-Euler map that underpins stochastic transport noise in fluid dynamics as…

Mathematical Physics · Physics 2025-11-06 Theo Diamantakis , Ruiao Hu , James-Michael Leahy

We propose a novel method for comparing non-aligned graphs of different sizes, based on the Wasserstein distance between graph signal distributions induced by the respective graph Laplacian matrices. Specifically, we cast a new formulation…

Machine Learning · Computer Science 2020-03-16 Hermina Petric Maretic , Mireille El Gheche , Matthias Minder , Giovanni Chierchia , Pascal Frossard

Effective Hamiltonians arise in several problems, including homogenization of Hamilton--Jacobi equations, nonlinear control systems, Hamiltonian dynamics, and Aubry--Mather theory. In Aubry--Mather theory, related objects, Mather measures,…

Numerical Analysis · Mathematics 2020-04-20 Diogo A. Gomes , Xianjin Yang

A framework for statistical-mechanical analysis of quantum Hamiltonians is introduced. The approach is based upon a gradient flow equation in the space of Hamiltonians such that the eigenvectors of the initial Hamiltonian evolve toward…

Quantum Physics · Physics 2013-09-13 Dorje C. Brody , David C. P. Ellis , Darryl D. Holm

In the first part of this paper we prove that the flow associated to the Burgers equation with a non local term of the form $\partial_x |D|^{\alpha-1} u$ fails to be uniformly continuous from bounded sets of $H^s({\mathbb D})$ to…

Analysis of PDEs · Mathematics 2025-10-13 Ayman Rimah Said

Continuing the research initiated in \cite{Fr-Ki2}, we study the existence of solutions and their regularity for the cohomological equations $X u=f$ for locally Hamiltonian flows (determined by the vector field $X$) on a compact surface $M$…

Dynamical Systems · Mathematics 2025-09-25 Krzysztof Frączek , Minsung Kim

In 1966, Edward Nelson presented an interesting derivation of the Schrodinger equation using Brownian motion. Recently, this derivation is linked to the theory of optimal transport, which shows that the Schrodinger equation is a Hamiltonian…

Dynamical Systems · Mathematics 2021-07-07 Shui-Nee Chow , Wuchen Li , Haomin Zhou

We revisit the existence problem of heteroclinic connections in $\mathbb{R}^N$ associated with Hamiltonian systems involving potentials $W:\mathbb{R}^N\to \mathbb{R}$ having several global minima. Under very mild assumptions on $W$ we…

Analysis of PDEs · Mathematics 2019-01-23 Andres Zuniga , Peter Sternberg

We give an application of a Huisken monotonicity-type formula for the mean curvature flow in a compact smooth manifold with a Riemannian metric that evolves by a shrinking self-similar solution of the extended Ricci flow. Our investigation…

Differential Geometry · Mathematics 2025-08-25 José N. V. Gomes , Matheus Hudson , Hikaru Yamamoto

Modeling real-world distributions can often be challenging due to sample data that are subjected to perturbations, e.g., instrumentation errors, or added random noise. Since flow models are typically nonlinear algorithms, they amplify these…

Machine Learning · Computer Science 2022-10-11 Sameera Ramasinghe , Kasun Fernando , Salman Khan , Nick Barnes

We study the modeling of a compressible two-phase flow in a porous medium. The governing free boundary problem is known as the Verigin problem with phase transition. We introduce a novel variational framework to construct weak solutions.…

Analysis of PDEs · Mathematics 2026-01-29 Anna Kubin , Tim Laux , Alice Marveggio

We numerically investigate the flow structure of periodic steady water waves of fixed relative mass flux propagating on rotational flows with piece-wise constant vorticity. We show that for wave solutions along the global bifurcation…

Fluid Dynamics · Physics 2020-11-25 Lin Chen , Biswajit Basu , Calin-I Martin

We prove well-posedness and regularity for the stochastic pure Cahn-Hilliard equation under homogeneous Neumann boundary conditions, with both additive and multiplicative Wiener noise. In contrast with great part of the literature, the…

Analysis of PDEs · Mathematics 2018-10-03 Luca Scarpa

Several deterministic and stochastic multi-variable global optimization algorithms (Conjugate Gradient, Nelder-Mead, Quasi-Newton, and Global) are investigated in conjunction with energy minimization principle to resolve the pressure and…

Fluid Dynamics · Physics 2015-09-08 Taha Sochi