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In this paper we provide an integral representation of the fractional Laplace-Beltrami operator for general riemannian manifolds which has several interesting applications. We give two different proofs, in two different scenarios, of…

Classical Analysis and ODEs · Mathematics 2017-04-21 Diego Alonso-Oran , Antonio Cordoba , Angel D. Martinez

The eigenvalues of the Laplace-Beltrami operator and the integrals of products of eigenfunctions must satisfy certain consistency conditions on compact Riemannian manifolds. These consistency conditions are derived by using spectral…

High Energy Physics - Theory · Physics 2022-02-23 James Bonifacio

This paper introduces a novel method for the efficient and accurate computation of the volume of a domain whose boundary is given by an orientable hypersurface which is implicitly given as the iso-contour of a sufficiently smooth level-set…

Fluid Dynamics · Physics 2021-01-13 Johannes Kromer , Dieter Bothe

The conjugate function method is an algorithm for numerical computation of conformal mappings for simply and multiply connected domains. In this paper, the conjugate function method is extended to cover conformal mappings between Riemannian…

Numerical Analysis · Mathematics 2024-04-22 Harri Hakula , Antti Rasila

In computer vision and graphics, various types of symmetries are extensively studied since symmetry present in objects is a fundamental cue for understanding the shape and the structure of objects. In this work, we detect the intrinsic…

Computer Vision and Pattern Recognition · Computer Science 2018-12-06 Rajendra Nagar , Shanmuganathan Raman

The spectral structure of the Laplacian-Beltrami operator (LBO) on manifolds has been widely used in many applications, include spectral clustering, dimensionality reduction, mesh smoothing, compression and editing, shape segmentation,…

Numerical Analysis · Mathematics 2015-06-19 Zuoqiang Shi , Jian Sun

Motion degradation is a central problem in Magnetic Resonance Imaging (MRI). This work addresses the problem of how to obtain higher quality, super-resolved motion-free, reconstructions from highly undersampled MRI data. In this work, we…

Image and Video Processing · Electrical Eng. & Systems 2019-08-19 Veronica Corona , Angelica I. Aviles-Rivero , Noémie Debroux , Carole Le Guyader , Carola-Bibiane Schönlieb

Using single-task deep learning methods to reconstruct Magnetic Resonance Imaging (MRI) data acquired with different imaging sequences is inherently challenging. The trained deep learning model typically lacks generalizability, and the…

Image and Video Processing · Electrical Eng. & Systems 2024-04-23 Wanyu Bian , Albert Jang , Fang Liu

The question whether one can recover the shape of a geometric object from its Laplacian spectrum ('hear the shape of the drum') is a classical problem in spectral geometry with a broad range of implications and applications. While…

Computational Geometry · Computer Science 2020-09-09 Luca Cosmo , Mikhail Panine , Arianna Rampini , Maks Ovsjanikov , Michael M. Bronstein , Emanuele Rodolà

Exploring the relationship between geometry and the resonant frequencies of a shape is of interest to pure and applied mathematicians. These resonant frequencies are related to the spectrum of the Laplacian, a partial differential operator.…

Spectral Theory · Mathematics 2018-08-23 Neal Coleman

Upper bounds for the eigenvalues of the Laplace-Beltrami operator on a hypersurface bounding a domain in some ambient Riemannian manifold are given in terms of the isoperimetric ratio of the domain. These results are applied to the…

Metric Geometry · Mathematics 2014-09-17 Bruno Colbois , Ahmad El Soufi , Alexandre Girouard

This paper concerns with iterative schemes for the perfect reconstruction of functions belonging to multiresolution spaces on bounded manifolds from nonuniform sampling. The schemes have optimal complexity in the sense that the…

Numerical Analysis · Mathematics 2007-05-23 Massimo Fornasier , Laura Gori

Modern sample points in many applications no longer comprise real vectors in a real vector space but sample points of much more complex structures, which may be represented as points in a space with a certain underlying geometric structure,…

Machine Learning · Statistics 2022-02-07 Zhigang Yao , Bingjie Li , Wee Chin Tan

Numerical experiments with smooth surface extension and image inpainting using harmonic and biharmonic functions are carried out. The boundary data used for constructing biharmonic functions are the values of the Laplacian and normal…

Analysis of PDEs · Mathematics 2018-09-25 S. B. Damelin , N. S. Hoang

We introduce a new local meshfree method for the approximation of the Laplace-Beltrami operator on a smooth surface of co-dimension one embedded in $\R^3$. A key element of this method is that it does not need an explicit expression of the…

Numerical Analysis · Mathematics 2020-02-05 Diego Alvarez , Pedro Gonzalez-Rodriguez , Manuel Kindelan

Manifold learning aims to discover and represent low-dimensional structures underlying high-dimensional data while preserving critical topological and geometric properties. Existing methods often fail to capture local details with global…

Machine Learning · Computer Science 2025-05-08 Ren Wang , Pengcheng Zhou

Eigenfunctions of the Laplace-Beltrami operator on a hyperboloid are studied in the spirit of the treatment of the spherical harmonics by Stein and Weiss. As a special case, a simple self-contained proof of Laplace's integral for a Legendre…

Spectral Theory · Mathematics 2009-03-12 Amritanshu Prasad , M. K. Vemuri

In the reconstruction process of unknown multiple scattering objects in inverse medium scattering problems, the first important step is to effectively locate some approximate domains that contain all inhomogeneous media. Without such an…

Numerical Analysis · Mathematics 2015-06-12 Keji Liu , Jun Zou

We develop a theoretical framework for the analysis of stabilized cut finite element methods for the Laplace-Beltrami operator on a manifold embedded in $\mathbb{R}^d$ of arbitrary codimension. The method is based on using continuous…

Numerical Analysis · Mathematics 2016-10-07 Erik Burman , Peter Hansbo , Mats G. Larson , Andre Massing

In much of the literature on function approximation by deep networks, the function is assumed to be defined on some known domain, such as a cube or a sphere. In practice, the data might not be dense on these domains, and therefore, the…

Machine Learning · Computer Science 2020-08-21 Hrushikesh Mhaskar