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In this paper, we give an estimate of sub-Laplacian of Riemannian distance functions in pseudo-Hermitian geometry which plays a similar role as Laplacian comparison theorem in Riemannian geometry, and deduce a prior horizontal gradient…

Differential Geometry · Mathematics 2019-04-23 Tian Chong , Yuxin Dong , Yibin Ren , Zhang Wei

This paper aims at proving the local boundedness and continuity of solutions of the heat equation in the context of Dirichlet spaces under some rather weak additional assumptions. We consider symmetric local regular Dirichlet forms which…

Analysis of PDEs · Mathematics 2020-11-16 Qi Hou , Laurent Saloff-Coste

We consider Laplacian eigenfunctions in circular, spherical and elliptical domains in order to discuss three kinds of high-frequency localization: whispering gallery modes, bouncing ball modes, and focusing modes. Although the existence of…

Mathematical Physics · Physics 2020-01-03 Binh-Thanh Nguyen , Denis Grebenkov

We provide sharp two-sided estimates on the Dirichlet heat kernel $k_1(t,x,y)$ for the Laplacian in a ball. The result accurately describes the exponential behaviour of the kernel for small times and significantly improves the qualitatively…

Analysis of PDEs · Mathematics 2017-04-05 Jacek Malecki , Grzegorz Serafin

The spherical domains $S^d_\beta$ with conical singularities are a convenient arena for studying the properties of tensor Laplacians on arbitrary manifolds with such a kind of singular points. In this paper the vector Laplacian on…

High Energy Physics - Theory · Physics 2016-09-06 Lara De Nardo , Dmitri V. Fursaev , Gennaro Miele

The goal of the paper is four-fold. In the setting of non-smooth spaces with Ricci curvature lower bounds (more precisely RCD(K,N) metric measure spaces): - we develop an intrinsic theory of Laplacian bounds in viscosity sense and in a…

Differential Geometry · Mathematics 2023-02-17 Andrea Mondino , Daniele Semola

Gaussian processes are an effective model class for learning unknown functions, particularly in settings where accurately representing predictive uncertainty is of key importance. Motivated by applications in the physical sciences, the…

Machine Learning · Statistics 2023-04-19 Viacheslav Borovitskiy , Alexander Terenin , Peter Mostowsky , Marc Peter Deisenroth

We give a short overview of the effective action approach in quantum field theory and quantum gravity and describe various methods for calculation of the asymptotic expansion of the heat kernel for second-order elliptic partial differential…

Mathematical Physics · Physics 2009-11-07 Ivan Avramidi

We consider the heat-kernel expansion of the massive Laplace operator on the three dimensional ball with Dirichlet boundary conditions. Using this example, we illustrate a very effective scheme for the calculation of an (in principle)…

High Energy Physics - Theory · Physics 2016-09-06 K. Kirsten , M. Bordag

Sharp comparison theorems are derived for all eigenvalues of the (weighted) Laplacian, for various classes of weighted-manifolds (i.e. Riemannian manifolds endowed with a smooth positive density). Examples include Euclidean space endowed…

Spectral Theory · Mathematics 2018-05-07 Emanuel Milman

Laplacian-based methods are popular for the dimensionality reduction of data lying in $\mathbb{R}^N$. Several theoretical results for these algorithms depend on the fact that the Euclidean distance locally approximates the geodesic distance…

Machine Learning · Computer Science 2025-09-24 Liane Xu , Amit Singer

Practical applications of kernel methods often use variable bandwidth kernels, also known as self-tuning kernels, however much of the current theory of kernel based techniques is only applicable to fixed bandwidth kernels. In this paper, we…

Spectral Theory · Mathematics 2015-01-15 Tyrus Berry , John Harlim

We prove (i) a simple sufficient geometric condition for localisation of a sequence of first Dirichlet eigenfunctions provided the corresponding Dirichlet Laplacians satisfy a uniform Hardy inequality, and (ii) localisation of a sequence of…

Spectral Theory · Mathematics 2025-01-28 Michiel van den Berg , Dorin Bucur

We tackle the problem of optimizing over all possible positive definite radial kernels on Riemannian manifolds for classification. Kernel methods on Riemannian manifolds have recently become increasingly popular in computer vision. However,…

Computer Vision and Pattern Recognition · Computer Science 2014-12-16 Sadeep Jayasumana , Richard Hartley , Mathieu Salzmann , Hongdong Li , Mehrtash Harandi

By using Duhamel's formula, we prove sharp two-sided estimates for the heat kernel of spectral fractional Laplacian with time-dependent gradient perturbation in bounded $C^{1,1}$ domains. Moreover, we also obtain gradient estimate as well…

Probability · Mathematics 2017-12-21 Renming Song , Longjie Xie , Yingchao Xie

We derive a local Gaussian upper bound for the $f$-heat kernel on complete smooth metric measure space $(M,g,e^{-f}dv)$ with nonnegative Bakry-\'{E}mery Ricci curvature, which generalizes the classic Li-Yau estimate. As applications, we…

Differential Geometry · Mathematics 2015-09-08 Jia-Yong Wu , Peng Wu

We consider the Hodge Laplacian on manifolds with incomplete edge singularities, with infinite dimensional von Neumann spaces and intricate elliptic boundary value theory. We single out a class of its algebraic self-adjoint extensions. Our…

Spectral Theory · Mathematics 2015-06-15 Boris Vertman

Integral invariants obtained from Principal Component Analysis on a small kernel domain of a submanifold encode important geometric information classically defined in differential-geometric terms. We generalize to hypersurfaces in any…

Differential Geometry · Mathematics 2018-04-16 Javier Álvarez-Vizoso , Michael Kirby , Chris Peterson

It is shown that the heat kernel operator for the Laplace operator on any covariantly constant curved background, i.e. in symmetric spaces, may be presented in form of an averaging over the Lie group of isometries with some nontrivial…

High Energy Physics - Theory · Physics 2009-10-28 Ivan G. Avramidi

Heat kernels arise in a variety of contexts including probability, geometry, and functional analysis; the automorphic heat kernel is particularly important in number theory and string theory. The typical construction of an automorphic heat…

Number Theory · Mathematics 2021-07-28 Amy T. DeCelles
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