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This paper presents a general high-order kernel regularization technique applicable to all four integral operators of Calder\'on calculus associated with linear elliptic PDEs in two and three spatial dimensions. Like previous density…

Numerical Analysis · Mathematics 2021-03-02 Luiz M. Faria , Carlos Pérez-Arancibia , Marc Bonnet

We propose a Variable-Preconditioned Transformed Primal-Dual (VPTPD) method for solving generalized Wasserstein gradient flows based on the structure-preserving JKO scheme. This is a nontrivial extension of the TPD method [Chen et al.…

Numerical Analysis · Mathematics 2025-10-22 Jin Zeng , Dawei Zhan , Ruchi Guo , Chaozhen Wei

We introduce renormalized integrals which generalize conventional measure theoretic integrals. One approximates the integration domain by measure spaces and defines the integral as the limit of integrals over the approximating spaces. This…

Differential Geometry · Mathematics 2012-06-12 Christian Baer

In this paper, the regularity results for the integro-differential operators of the fractional Laplacian type by Caffarelli and Silvestre \cite{CS1} are extended to those for the integro-differential operators associated with symmetric,…

Analysis of PDEs · Mathematics 2014-08-04 Soojung Kim , Yong-Cheol Kim , Ki-Ahm Lee

Variational methods based on optimization strategies are proposed to numerically solve a large family of nonlinear partial differential equations. They are all particular instances of gradient flows with general costs, including the…

Numerical Analysis · Mathematics 2026-04-23 Luis M. Briceño-Arias , José A. Carrillo , Dante Kalise , Francisco J. Silva , Li Wang

This paper extends and analyzes the high-order kernel regularization framework of Beale & Tlupova (arXiv:2510.13639) to all four on-surface boundary integral operators of the Helmholtz Calderon calculus in three dimensions: the…

Numerical Analysis · Mathematics 2026-04-29 Luiz M. Faria , Carlos Perez-Arancibia , Svetlana Tlupova

We study the heat kernel asymptotics for the Laplace type differential operators on vector bundles over Riemannian manifolds. In particular this includes the case of the Laplacians acting on differential p-forms. We extend our results…

Differential Geometry · Mathematics 2007-05-23 Iosif Polterovich

We consider a class of homogeneous partial differential operators on a finite-dimensional vector space and study their associated heat kernels. The heat kernels for this general class of operators are seen to arise naturally as the limiting…

Analysis of PDEs · Mathematics 2016-12-23 Evan Randles , Laurent Saloff-Coste

Approximate solutions to elliptic partial differential equations with known kernel can be obtained via the boundary element method (BEM) by discretizing the corresponding boundary integral operators and solving the resulting linear system…

Numerical Analysis · Mathematics 2019-09-17 Andrea Cagliero

In this thesis, we consider the Wasserstein barycenter problem of discrete probability measures from computational and statistical sides. The statistical focus is estimating the sample size of measures necessary to calculate an…

Optimization and Control · Mathematics 2021-10-26 Darina Dvinskikh

We obtain pointwise upper bounds on the derivatives of the heat kernel on Damek-Ricci spaces. Applying these estimates we prove the $L^p$-boundedness of Littlewood-Paley-Stein operators.

Analysis of PDEs · Mathematics 2020-09-02 Anestis Fotiadis , Effie Papageorgiou

This article presents a three-step framework for learning and solving partial differential equations (PDEs) using kernel methods. Given a training set consisting of pairs of noisy PDE solutions and source/boundary terms on a mesh, kernel…

Machine Learning · Statistics 2023-04-03 Da Long , Nicole Mrvaljevic , Shandian Zhe , Bamdad Hosseini

The goal of this paper is to study the action of the heat operator on the Heisenberg group H^d, and in particular to characterize Besov spaces of negative index on H^d in terms of the heat kernel. That characterization can be extended to…

Analysis of PDEs · Mathematics 2009-09-29 Hajer Bahouri , Isabelle Gallagher

We focus on the fundamental mathematical structure of score-based generative models (SGMs). We first formulate SGMs in terms of the Wasserstein proximal operator (WPO) and demonstrate that, via mean-field games (MFGs), the WPO formulation…

Machine Learning · Statistics 2024-02-12 Benjamin J. Zhang , Siting Liu , Wuchen Li , Markos A. Katsoulakis , Stanley J. Osher

We investigate the heat equation corresponding to the Bessel operators on a symmetric cone $\Omega=G/K$. These operators form a one-parameter family of elliptic self-adjoint second order differential operators and occur in the Lie algebra…

Analysis of PDEs · Mathematics 2013-11-27 Jan Möllers

We consider the heat kernel for higher-derivative and nonlocal operators in $d$-dimensional Euclidean space-time and its asymptotic behavior. As a building block for operators of such type, we consider the heat kernel of the minimal…

High Energy Physics - Theory · Physics 2019-11-11 A. O. Barvinsky , P. I. Pronin , W. Wachowski

Here, we study a discrete Coagulation-Fragmentation equation with a multiplicative coagulation kernel and a constant fragmentation kernel, which is critical. We apply the discrete Bernstein transform to the original…

Analysis of PDEs · Mathematics 2024-09-27 Jiwoong Jang , Hung V. Tran

We obtain pointwise lower bounds for heat kernels of higher order differential operators with Dirichlet boundary conditions on bounded domains in $\R^N$. The bounds exhibit explicitly the nature of the spatial decay of the heat kernel close…

Spectral Theory · Mathematics 2011-10-18 Narinder S Claire

Solutions of partial differential equations can often be written as surface integrals having a kernel related to a singular fundamental solution. Special methods are needed to evaluate the integral accurately at points on or near the…

Numerical Analysis · Mathematics 2025-10-16 J. Thomas Beale , Svetlana Tlupova

In this work, a kernel-based surrogate for integrating Hamiltonian dynamics that is symplectic by construction and tailored to large prediction horizons is proposed. The method learns a scalar potential whose gradient enters a…

Numerical Analysis · Mathematics 2026-01-27 Robin Herkert , Tobias Ehring , Bernard Haasdonk