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We present variational approximations of boundary value problems for curvature flow (curve shortening flow) and elastic flow (curve straightening flow) in two-dimensional Riemannian manifolds that are conformally flat. For the evolving open…

Numerical Analysis · Mathematics 2021-11-03 Harald Garcke , Robert Nürnberg

The elastic flow, which is the $L^2$-gradient flow of the elastic energy, has several applications in geometry and elasticity theory. We present stable discretizations for the elastic flow in two-dimensional Riemannian manifolds that are…

Numerical Analysis · Mathematics 2019-11-01 John W. Barrett , Harald Garcke , Robert Nürnberg

The paper addresses the numerical approximation of two variants of hyperbolic mean curvature flow of surfaces in $\mathbb R^3$. For each evolution law we propose both a finite element method, as well as a finite difference scheme in the…

Numerical Analysis · Mathematics 2025-02-11 Klaus Deckelnick , Robert Nürnberg

We propose a generalization of two classes of Lie-Hamilton systems on the Euclidean plane to two-dimensional curved spaces, leading to novel Lie-Hamilton systems on Riemannian spaces (flat $2$-torus, product of hyperbolic lines, sphere and…

Mathematical Physics · Physics 2024-11-26 Rutwig Campoamor-Stursberg , Oscar Carballal , Francisco J. Herranz

In this paper we introduce a new geometric flow --- the hyperbolic gradient flow for graphs in the $(n+1)$-dimensional Euclidean space $\mathbb{R}^{n+1}$. This kind of flow is new and very natural to understand the geometry of manifolds. We…

Differential Geometry · Mathematics 2016-09-09 De-Xing Kong , Kefeng Liu

A recent paper [CGT] studies the evolution of star-shaped mean convex hypersurfaces of the Euclidean space by a class of nonhomogeneous expanding curvature flows. In the present paper we consider the same problem in the real, complex and…

Differential Geometry · Mathematics 2020-10-08 Giuseppe Pipoli

We formulate hydrodynamic equations and spectrally accurate numerical methods for investigating the role of geometry in flows within two-dimensional fluid interfaces. To achieve numerical approximations having high precision and level of…

Soft Condensed Matter · Physics 2023-02-28 Ben J. Gross , Paul J. Atzberger

We investigate the motion of a family of closed curves evolving according to the geometric evolution law on a given two dimensional manifold which is embedded or immersed in the three-dimensional Euclidean space. We derive a system of…

Analysis of PDEs · Mathematics 2025-12-23 Miroslav Kolar , Daniel Sevcovic

Normalizing flows have shown great promise for modelling flexible probability distributions in a computationally tractable way. However, whilst data is often naturally described on Riemannian manifolds such as spheres, torii, and hyperbolic…

Machine Learning · Statistics 2020-12-10 Emile Mathieu , Maximilian Nickel

Physically meaningful periodic solutions to certain integrable partial differential equations are given in terms of multi-dimensional theta functions associated to real Riemann surfaces. Typical analytical problems in the numerical…

Mathematical Physics · Physics 2015-05-28 C. Kalla , C. Klein

We present natural axisymmetric variants of schemes for curvature flows introduced earlier by the present authors and analyze them in detail. Although numerical methods for geometric flows have been used frequently in axisymmetric settings,…

Numerical Analysis · Mathematics 2019-11-01 John W. Barrett , Harald Garcke , Robert Nürnberg

An algebraic formulation of Riemannian geometry on quantum spaces is presented, where Riemannian metric, distance, Laplacian, connection, and curvature have their counterparts. This description is also extended to complex manifolds.…

q-alg · Mathematics 2009-10-28 Pei-Ming Ho

In machine learning, data is usually represented in a (flat) Euclidean space where distances between points are along straight lines. Researchers have recently considered more exotic (non-Euclidean) Riemannian manifolds such as hyperbolic…

Machine Learning · Computer Science 2021-01-12 Marc T. Law , Jos Stam

We study one-parameter curves on the universal Teichm\"uller space $T$ and on the homogeneous space $M=\Diff S^1/\Rot S^1$ embedded into $T$. As a result, we deduce evolution equations for conformal maps that admit quasiconformal extensions…

Analysis of PDEs · Mathematics 2007-05-23 Alexander Vasil'ev

This paper gives some examples of hypersurfaces $\phi_t(M^n)$ evolving in time with speed determined by functions of the normal curvatures in an $(n+1)$-dimensional hyperbolic manifold; we emphasize the case of flow by harmonic mean…

Differential Geometry · Mathematics 2013-09-25 Robert Gulliver , Guoyi Xu

A fundamental question in Riemannian geometry is to find canonical metrics on a given smooth manifold. In the 1980s, R. Hamilton proposed an approach to this question based on parabolic partial differential equations. The goal is to start…

Differential Geometry · Mathematics 2011-08-24 S. Brendle

We establish area bounds for two-dimensional immersions in R^3 and R^n. Namely, for \mu-stable immersions in R^3 (R^n), for graphs in $\mathbb R^3$ which solve quasilinear equations in divergence form, and for graphs which are critical for…

Differential Geometry · Mathematics 2007-05-23 Steffen Froehlich

The consideration of the so-called rotation minimizing frames allows for a simple and elegant characterization of plane and spherical curves in Euclidean space via a linear equation relating the coefficients that dictate the frame motion.…

Differential Geometry · Mathematics 2018-03-28 Luiz C. B. da Silva , José Deibsom da Silva

Families of conformal field theories are naturally endowed with a Riemannian geometry which is locally encoded by correlation functions of exactly marginal operators. We show that the curvature of such conformal manifolds can be computed…

High Energy Physics - Theory · Physics 2023-08-09 Bruno Balthazar , Clay Cordova

Representing graphs as sets of node embeddings in certain curved Riemannian manifolds has recently gained momentum in machine learning due to their desirable geometric inductive biases, e.g., hierarchical structures benefit from hyperbolic…

Machine Learning · Computer Science 2020-06-09 Calin Cruceru , Gary Bécigneul , Octavian-Eugen Ganea
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