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This paper investigates the connections between rectified flows, flow matching, and optimal transport. Flow matching is a recent approach to learning generative models by estimating velocity fields that guide transformations from a source…

Machine Learning · Computer Science 2026-02-17 Johannes Hertrich , Antonin Chambolle , Julie Delon

In this work a result of existence and uniqueness for a plane cavity driven steady flow is deduced using an analytical method for the resolution of a linear partial differential problem on a triangular domain. The solution admits a symbolic…

Analysis of PDEs · Mathematics 2009-12-23 Gianluca Argentini

Within the Functional Renormalisation Group (FRG) approach, we present a fluid-dynamical approach to solving flow equations for models living in a multi-dimensional field space. To this end, the underlying exact flow equation of the…

Statistical Mechanics · Physics 2024-12-23 Niklas Zorbach , Adrian Koenigstein , Jens Braun

This paper proposes a method to translate multilevel flow modeling (MFM) into a first-order language (FOL), which enables the utilisation of logical techniques, such as inference engines and abductive reasoners. An example of this is a…

Artificial Intelligence · Computer Science 2018-06-20 Shota Motoura , Kazeto Yamamoto , Shumpei Kubosawa , Takashi Onishi

A novel pressure-free two-fluid model formulation is proposed for the simulation of one-dimensional incompressible multiphase flow in pipelines and channels. The model is obtained by simultaneously eliminating the volume constraint and the…

Fluid Dynamics · Physics 2020-10-27 B. Sanderse , J. F. H. Buist , R. A. W. M. Henkes

Many generative models originally developed in finite-dimensional Euclidean space have functional generalizations in infinite-dimensional settings. However, the extension of rectified flow to infinite-dimensional spaces remains unexplored.…

Machine Learning · Computer Science 2025-09-15 Jianxin Zhang , Clayton Scott

We provide a new universal real flow of the Hilbert-cubical type. We prove that any real flow can be equivariantly embedded in the translation on $L(\mathbb{R})^\mathbb{N}$, where $L(\mathbb{R})$ denotes the space of $1$-Lipschitz functions…

Dynamical Systems · Mathematics 2018-09-07 Lei Jin , Siming Tu

We prove that if a translating soliton can be expressed as the sum of two curves and one of these curves is planar, then the other curve is also planar and consequently the surface must be a plane or a grim reaper. We also investigate…

Differential Geometry · Mathematics 2021-09-14 Muhittin Evren Aydin , Rafael Lopez

The well known formula $[X,Y]=\tfrac12\tfrac{\partial^2}{\partial t^2}|_0 (\Fl^Y_{-t}\o\Fl^X_{-t}\o\Fl^Y_t\o\Fl^X_t)$ for vector fields $X$, $Y$ is generalized to arbitrary bracket expressions and arbitrary curves of local diffeomorphisms.

Differential Geometry · Mathematics 2009-09-25 Markus Mauhart , Peter W. Michor

Normalizing Flows provide a principled framework for high-dimensional density estimation and generative modeling by constructing invertible transformations with tractable Jacobian determinants. We propose Fractal Flow, a novel normalizing…

Machine Learning · Statistics 2025-08-28 Binhui Zhang , Jianwei Ma

We investigate the topological structure of flows on the Girl's surfaces which is one of two possible immersions of the projective plane in three-dimensional space with one triple point of the selfintersection. First, we describe the…

Dynamical Systems · Mathematics 2022-08-23 Maria Loseva , Alexandr Prishlyak

Motivated by the study of billiards in polyhedra, we study linear flows in a family of singular flat $3$-manifolds which we call translation prisms. Using ideas of Furstenberg and Veech, we connect results about weak mixing properties of…

Dynamical Systems · Mathematics 2025-04-15 Jayadev S. Athreya , Nicolas Bédaride , W. Patrick Hooper , Pascal Hubert

In order to find reliable and efficient numerical approximation schemes, we suggest to identify the Functional Renormalization Group flow equations of one-particle irreducible two-point functions as Hamilton-Jacobi(-Bellman)-type partial…

High Energy Physics - Theory · Physics 2025-12-30 Adrian Koenigstein , Martin J. Steil , Stefan Floerchinger

We apply pseudo-spectral methods to integrate functional flow equations with high accuracy, extending earlier work on functional fixed point equations \cite{Borchardt:2015rxa}. The advantages of our method are illustrated with the help of…

High Energy Physics - Theory · Physics 2016-07-27 Julia Borchardt , Benjamin Knorr

We show that if a Laurent series $f\in\mathbb{C}((t))$ satisfies a particular kind of linear iterative equation, then $f$ is either a rational function or it is differentially transcendental over $\mathbb{C}(t)$. This condition is more…

Combinatorics · Mathematics 2023-12-04 Lucia Di Vizio , Gwladys Fernandes , Marni Mishna

We develop mean dimension theory for $\mathbb{R}$-flows. We obtain fundamental properties and examples and prove an embedding theorem: Any real flow $(X,\mathbb{R})$ of mean dimension strictly less than $r$ admits an extension…

Dynamical Systems · Mathematics 2020-04-03 Yonatan Gutman , Lei Jin

Rectified flow (Liu et al., 2022; Liu, 2022; Wu et al., 2023) is a method for defining a transport map between two distributions, and enjoys popularity in machine learning, although theoretical results supporting the validity of these…

Statistics Theory · Mathematics 2025-12-11 Gonzalo Mena , Arun Kumar Kuchibhotla , Larry Wasserman

This paper is concerned with qualitative properties of bounded steady flows of an ideal incompressible fluid with no stagnation point in the two-dimensional plane R^2. We show that any such flow is a shear flow, that is, it is parallel to…

Analysis of PDEs · Mathematics 2018-10-03 Francois Hamel , Nikolai Nadirashvili

A normalizing flow is an invertible mapping between an arbitrary probability distribution and a standard normal distribution; it can be used for density estimation and statistical inference. Computing the flow follows the change of…

Machine Learning · Computer Science 2021-12-10 Derek Onken , Samy Wu Fung , Xingjian Li , Lars Ruthotto

Learning probabilistic surrogates for partial differential equations remains challenging in data-scarce regimes: neural operators require large amounts of high-fidelity data, while generative approaches typically sacrifice resolution…

Computation · Statistics 2025-12-18 Sahil Bhola , Karthik Duraisamy