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We show the existence of a properly immersed translating solution to curve diffusion flow in the plane. Curve diffusion flow is a higher order version of curve shortening flow, namely \[ \left( \frac{dX}{dt}\right) ^{\perp}=-\kappa_{ss}N.…

Differential Geometry · Mathematics 2024-12-23 W. Jacob Ogden , Micah Warren

We present a new formulation of some basic differential geometric notions on a smooth manifold M, in the setting of nonstandard analysis. In place of classical vector fields, for which one needs to construct the tangent bundle of M, we…

Differential Geometry · Mathematics 2016-09-27 Tahl Nowik , Mikhail G. Katz

We show that any strictly mean convex translator of dimension $n\geq 3$ which admits a cylindrical estimate and a corresponding gradient estimate is rotationally symmetric. As a consequence, we deduce that any translating solution of the…

Differential Geometry · Mathematics 2016-06-01 Theodora Bourni , Mat Langford

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

Assignment flows denote a class of dynamical models for contextual data labeling (classification) on graphs. We derive a novel parametrization of assignment flows that reveals how the underlying information geometry induces two processes…

Dynamical Systems · Mathematics 2019-10-17 Fabrizio Savarino , Christoph Schnörr

Knowledge graph embedding refers to projecting entities and relations in knowledge graph into continuous vector spaces. State-of-the-art methods, such as TransE, TransH, and TransR build embeddings by treating relation as translation from…

Computation and Language · Computer Science 2015-09-11 Jun Feng , Mantong Zhou , Yu Hao , Minlie Huang , Xiaoyan Zhu

Partial differential equation (PDE) simulation holds extensive significance in scientific research. Currently, the integration of deep neural networks to learn solution operators of PDEs has introduced great potential. In this paper, we…

Machine Learning · Computer Science 2026-03-25 Haosen Li , Qi Meng , Jiahao Li , Rui Zhang , Ruihua Song , Liang Ma , Zhi-Ming Ma

The unsupervised task of aligning two or more distributions in a shared latent space has many applications including fair representations, batch effect mitigation, and unsupervised domain adaptation. Existing flow-based approaches estimate…

Machine Learning · Computer Science 2022-03-17 Zeyu Zhou , Ziyu Gong , Pradeep Ravikumar , David I. Inouye

A prominent goal of representation learning research is to achieve representations which are factorized in a useful manner with respect to the ground truth factors of variation. The fields of disentangled and equivariant representation…

Machine Learning · Computer Science 2023-09-26 Yue Song , T. Anderson Keller , Nicu Sebe , Max Welling

In this article we present a novel staggered semi-implicit hybrid finite-volume/finite-element (FV/FE) method for the resolution of weakly compressible flows in two and three space dimensions. The pressure-based methodology introduced in…

Numerical Analysis · Mathematics 2020-06-16 A. Bermúdez , S. Busto , M. Dumbser , J. L. Ferrín , L. Saavedra , M. E. Vázquez-Cendón

The aim of this paper is to obtain an asymptotic expansion for ergodic integrals of translation flows on flat surfaces of higher genus (Theorem 1) and to give a limit theorem for these flows (Theorem 2).

Dynamical Systems · Mathematics 2014-07-28 Alexander I. Bufetov

We examine the blow-up claims of the incompressible Euler equations for several specific flow-fields, (1) the columnar eddies in the vicinity of stagnation; (2) a quasi-three-dimensional structure for illustrating oscillations and…

Fluid Dynamics · Physics 2023-06-16 F. Lam

We investigate the onset and evolution of zonal flows in a growing convective layer when a stably-stratified fluid with a composition gradient is cooled from above. This configuration allows the study of zonal flows for a wide range of…

Fluid Dynamics · Physics 2023-11-08 J. R. Fuentes , A. Cumming

The notion of the flow introduced by Kitaev is a manifestly topological formulation of the winding number on a real lattice. First, we show in this paper that the flow is quite useful for practical numerical computations for systems without…

Chaotic Dynamics · Physics 2024-08-01 F. Hamano , T. Fukui

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

We investigate topological propeties of flows with one singular point and without closed orbits on the 2-dimensional disk. To classify such flows, destingueshed graph is used, which is a two-colored rooted tree imbedded in the plane. We…

Dynamical Systems · Mathematics 2023-04-04 Alexandr Prishlyak , Serhii Stas

A class of harmonic solutions to the steady Euler equations for incompressible fluids is presented in two dimensions in circular, elliptic and bipolar coordinates. Since the velocity field is solenoidal in this case, it can be written as…

Fluid Dynamics · Physics 2014-08-06 Pablo Luis Rendón , Eugenio Ley-Koo

In this paper we provide a full classification of complete translating graphs in $\mathbf{R}^3$. We also construct two $(n-1)$-parameter families of new examples of translating graphs in $\mathbf{R}^{n+1}$.

Differential Geometry · Mathematics 2025-11-20 David Hoffman , Tom Ilmanen , Francisco Martin , Brian White

An elliptic Newton flow is a dynamical system that can be interpreted as a continuous version of Newton's iteration method for finding the zeros of an elliptic function f. Previous work focusses on structurally stable flows (i.e., the phase…

Dynamical Systems · Mathematics 2017-02-21 G. F. Helminck , F. Twilt

We formulate well-posed continuous-time generative flows for learning distributions that are supported on low-dimensional manifolds through Wasserstein proximal regularizations of $f$-divergences. Wasserstein-1 proximal operators regularize…

Machine Learning · Statistics 2024-07-17 Hyemin Gu , Markos A. Katsoulakis , Luc Rey-Bellet , Benjamin J. Zhang