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This article introduces a new approach to discrete curvature based on the concept of effective resistances. We propose a curvature on the nodes and links of a graph and present the evidence for their interpretation as a curvature. Notably,…

Differential Geometry · Mathematics 2022-09-26 Karel Devriendt , Renaud Lambiotte

How does one generalize differential geometric constructs such as curvature of a manifold to the discrete world of graphs and other combinatorial structures? This problem carries significant importance for analyzing models of discrete…

Combinatorics · Mathematics 2023-06-27 J. F. Du Plessis , Xerxes D. Arsiwalla

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

In this paper, we introduce a new discretization of the Gaussian curvature on surfaces, which is defined as the quotient of the angle defect and the area of some dual cell of a weighted triangulation at the conic singularity. A discrete…

Differential Geometry · Mathematics 2023-09-12 Xu Xu , Chao Zheng

This paper presents an analysis of properties of two hybrid discretization methods for Gaussian derivatives, based on convolutions with either the normalized sampled Gaussian kernel or the integrated Gaussian kernel followed by central…

Numerical Analysis · Mathematics 2025-01-31 Tony Lindeberg

For matrix analogues of embedded surfaces we define discrete curvatures and Euler characteristics, and a non-commutative Gauss--Bonnet theorem is shown to follow. We derive simple expressions for the discrete Gauss curvature in terms of…

Mathematical Physics · Physics 2010-01-20 Joakim Arnlind , Jens Hoppe , Gerhard Huisken

We give a relationship that yields an effective geometric way of evaluating mean curvature of surfaces. The approach is reminiscent of the Gauss's contour based evaluation of intrinsic curvature. The presented formula may have a number of…

Numerical Analysis · Mathematics 2011-08-10 Pavel Grinfeld

In this paper we use the angular deficit scheme [V. Borrelli, F. Cazals, and J.-M. Morvan, {\sl Computer Aided Geometric Design} {\bf 20}, 319 (2003)] to determine the distribution of Gaussian curvature in developable cones (d-cones) [E.…

Soft Condensed Matter · Physics 2015-05-13 Jin W. Wang , Thomas A. Witten

A discretisation scheme that preserves topological features of a physical problem is extended so that differential geometric structures can be approximated in a consistent way thus giving access to the study of physical systems which are…

High Energy Physics - Theory · Physics 2007-05-23 Vivien de Beauce , Siddhartha Sen

We develop efficient numerical integration methods for computing an integral whose integrand is a product of a smooth function and the Gaussian function with a small standard deviation. Traditional numerical integration methods applied to…

Numerical Analysis · Mathematics 2018-04-12 Yunyun Ma , Yuesheng Xu

A new semi-discrete finite element scheme for the evolution of three parametrized curves by curvature flow that are connected by a triple junction is presented and analyzed. In this triple junction, conditions are imposed on the angles at…

Numerical Analysis · Mathematics 2019-11-22 Paola Pozzi , Björn Stinner

We consider the Gaussian curvature conjecture of a minimal graph $S$ over the unit disk. First of all we reduce the general conjecture to the estimating the Gaussian curvature of some Scherk's type minimal surfaces over a quadrilateral…

Differential Geometry · Mathematics 2021-11-23 David Kalaj

This paper is concerned with the problem of prescribing Gaussian curvature and geodesic curvature in a compact surface with boundary with conical singularities and corners. Solutions are obtained using a new variational formulation,…

Analysis of PDEs · Mathematics 2025-08-18 Luca Battaglia , Francisco Javier Reyes-Sanchez

Interesting data often concentrate on low dimensional smooth manifolds inside a high dimensional ambient space. Random projections are a simple, powerful tool for dimensionality reduction of such data. Previous works have studied bounds on…

Machine Learning · Statistics 2016-09-13 Subhaneil Lahiri , Peiran Gao , Surya Ganguli

In this paper, we study the estimation of Gauss curvature for $K$-quasiconformal harmonic surface in ${\mathbb R}^3$ and present an accurate improvement of the previous result in [6, Theorem 5.2]. Let $X:M\rightarrow{\mathbb R}^3$ denote a…

Complex Variables · Mathematics 2025-03-14 Zhixue Liu , Yezhou Li

A widely used approximation of the Gaussian curvature on a triangulated surface is the angle defect, which measures the deviation between $2\pi$ and the sum of the angles between neighboring edges emanating from a common vertex. We show…

Numerical Analysis · Mathematics 2019-05-20 Evan S. Gawlik

Gaussian Mixture Models (GMM) do not adapt well to curved and strongly nonlinear data. However, we can use Gaussians in the curvilinear coordinate systems to solve this problem. Moreover, such a solution allows for the adaptation of…

Computer Vision and Pattern Recognition · Computer Science 2023-04-05 Krzysztof Byrski , Przemysław Spurek , Jacek Tabor

A variant of the Gauss curvature flow for closed and convex hypersurfaces is considered. We reveal that if the initial hypersurface is pinched enough, then this property is preserved. Furthermore, based on some structure assumptions on the…

Analysis of PDEs · Mathematics 2023-12-01 Jinrong Hu , Ping Zhang

In this paper, we solve the longstanding Gaussian curvature conjecture of a minimal graph $S$ over the unit disk. The conjecture asserts that for any minimal graph above the unit disk, the Gaussian curvature at the point directly above the…

Differential Geometry · Mathematics 2025-06-10 David Kalaj , Petar Melentijevic

Generalisation across image scales remains a fundamental challenge for deep networks, which often fail to handle images at scales not seen during training (the out-of-distribution problem). In this paper, we present provably scale-invariant…

Computer Vision and Pattern Recognition · Computer Science 2026-03-04 Andrzej Perzanowski , Tony Lindeberg