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Diffusion models have demonstrated remarkable empirical success in the recent years and are considered one of the state-of-the-art generative models in modern AI. These models consist of a forward process, which gradually diffuses the data…

Machine Learning · Computer Science 2026-01-07 Xingyu Xu , Ziyi Zhang , Yorie Nakahira , Guannan Qu , Yuejie Chi

On a complete non-compact Riemannian manifold satisfying the volume doubling property, we give conditions on the negative part of the Ricci curvature that ensure that, unless there are harmonic one-forms, the Gaussian heat kernel upper…

Analysis of PDEs · Mathematics 2016-06-09 Thierry Coulhon , Baptiste Devyver , Adam Sikora

We find a Gaussian off-diagonal heat kernel estimate for uniformly elliptic operators with measurable coefficients acting on regions $\Omega\subseteq\real^N$, where the order $2m$ of the operator satisfies $N<2m$. The estimate is expressed…

Spectral Theory · Mathematics 2007-05-23 Mark P. Owen

We construct a strongly local regular Dirichlet form on the golden ratio Sierpinski gasket, which is a self-similar set without any finitely ramified cell structure, via a study on the trace of electrical networks on an infinite graph. The…

Functional Analysis · Mathematics 2024-05-22 Shiping Cao , Hua Qiu

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 prove sharp two-sided global estimates for the heat kernel associated with a Euclidean sphere of arbitrary dimension. This solves a long-standing open problem.

Classical Analysis and ODEs · Mathematics 2019-11-19 Adam Nowak , Peter Sjögren , Tomasz Z. Szarek

In recent years, quantities derived from the heat equation have become popular in shape processing and analysis of triangulated surfaces. Such measures are often robust with respect to different kinds of perturbations, including…

Computer Vision and Pattern Recognition · Computer Science 2021-06-09 Jose A. Iglesias , Ron Kimmel

We consider the elliptic system of linear elasticity with bounded measurable coefficients in a domain where the second Korn inequality holds. We construct heat kernel of the system subject to Dirichlet, Neumann, or mixed boundary condition…

Analysis of PDEs · Mathematics 2014-09-25 Justin Taylor , Seick Kim , Russell Brown

We analyze the asymptotic behaviour of the heat kernel defined by a stochastically perturbed geodesic flow on the cotangent bundle of a Riemannian manifold for small time and small diffusion parameter. This extends WKB-type methods to a…

Functional Analysis · Mathematics 2009-12-26 Sergio Albeverio , Astrid Hilbert , Vassily Kolokoltsov

Let M be a complete Riemannian manifold with a free cocompact Z^k-action. Let k(t,x,y) be the heat kernel on M. We compute the asymptotics of k(t,x,y) in the limit in which t goes to infinity and d(x,y) is comparable to sqrt{t}. We show…

dg-ga · Mathematics 2008-02-03 John Lott

A diffusion process associated with the real sub-Laplacian $\Delta_b$, the real part of the complex Kohn-Spencer laplacian $\square_b$, on a strictly pseudoconvex CR manifold is constructed via the Eells-Elworthy-Malliavin method by taking…

Probability · Mathematics 2017-01-27 Hiroki Kondo , Setsuo Taniguchi

Learning the distribution of data on Riemannian manifolds is crucial for modeling data from non-Euclidean space, which is required by many applications in diverse scientific fields. Yet, existing generative models on manifolds suffer from…

Machine Learning · Computer Science 2024-06-04 Jaehyeong Jo , Sung Ju Hwang

It has recently been shown that plane-plate heat effusion methods devised for wide-frequency specific-heat spectroscopy do not give the isobaric specific heat, but rather the so-called longitudinal specific heat. Here it is shown that heat…

Soft Condensed Matter · Physics 2009-11-13 Tage Christensen , Niels Boye Olsen , Jeppe C. Dyre

Many contemporary statistical learning methods assume a Euclidean feature space. This paper presents a method for defining similarity based on hyperspherical geometry and shows that it often improves the performance of support vector…

Machine Learning · Statistics 2018-08-07 Chenchao Zhao , Jun S. Song

A diffusion process associated with the real sub-Laplacian $\Delta_b$, the real part of the complex Kohn-Spencer Laplacian $\square_b$, on a strictly pseudoconvex CR manifold has been constructed. In this paper, we investigate diagonal…

Probability · Mathematics 2015-12-01 Hiroki Kondo

We construct a new random probability measure on the sphere and on the unit interval which in both cases has a Gibbs structure with the relative entropy functional as Hamiltonian. It satisfies a quasi-invariance formula with respect to the…

Probability · Mathematics 2007-05-23 Max-K von Renesse , Karl-Theodor Sturm

To study diffusion processes on the p-Wasserstein space $\mathscr P_p$ for $p\in [1,\infty)$ over a separable, reflexive Banach space $X$, we present a criterion on the quasi-regularity of Dirichlet forms in $L^2(\mathscr P_p,\Lambda)$ for…

Probability · Mathematics 2025-06-30 Panpan Ren , Feng-Yu Wang , Simon Wittmann

On doubling metric measure spaces endowed with a strongly local regular Dirichlet form, we show some characterisations of pointwise upper bounds of the heat kernel in terms of global scale-invariant inequalities that correspond respectively…

Analysis of PDEs · Mathematics 2013-11-15 Salahaddine Boutayeb , Thierry Coulhon , Adam Sikora

We construct equivalent semi-norms of non-local Dirichlet forms on the Sierpi\'nski gasket and apply these semi-norms to a convergence problem and a trace problem. We also construct explicitly a sequence of non-local Dirichlet forms with…

Functional Analysis · Mathematics 2017-10-03 Meng Yang

We consider Ising mixed $p$-spin glasses at high-temperature and without external field, and study the problem of sampling from the Gibbs distribution $\mu$ in polynomial time. We develop a new sampling algorithm with complexity of the same…

Probability · Mathematics 2025-10-22 Ahmed El Alaoui , Andrea Montanari , Mark Sellke