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Related papers: Gradient Bounds for Kolmogorov Type Diffusions

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Generative diffusions are a powerful class of Monte Carlo samplers that leverage bridging Markov processes to approximate complex, high-dimensional distributions, such as those found in image processing and language models. Despite their…

Machine Learning · Statistics 2025-02-20 Zheng Zhao , Ziwei Luo , Jens Sjölund , Thomas B. Schön

We present some long-range interaction models for phase coexistence which have recently appeared in the literature, recalling also their relation to classical interface and capillarity problems. In this note, the main focus will be on the…

Analysis of PDEs · Mathematics 2020-01-07 Serena Dipierro , Pietro Miraglio , Enrico Valdinoci

We establish an equivalence between two classes of methods for solving fractional diffusion problems, namely, Reduced Basis Methods (RBM) and Rational Krylov Methods (RKM). In particular, we demonstrate that several recently proposed RBMs…

Numerical Analysis · Mathematics 2021-03-01 Tobias Danczul , Clemens Hofreither

Coded computation techniques provide robustness against straggling servers in distributed computing, with the following limitations: First, they increase decoding complexity. Second, they ignore computations carried out by straggling…

Machine Learning · Computer Science 2018-11-29 Emre Ozfatura , Sennur Ulukus , Deniz Gunduz

Diffusion generative models have recently been applied to domains where the available data can be seen as a discretization of an underlying function, such as audio signals or time series. However, these models operate directly on the…

Machine Learning · Computer Science 2023-02-28 Gavin Kerrigan , Justin Ley , Padhraic Smyth

While it is known that alloy components can segregate to grain boundaries (GBs), and that the atomic mobility in GBs greatly exceeds the atomic mobility in the lattice, little is known about the effect of GB segregation on GB diffusion.…

Materials Science · Physics 2020-08-17 R. K. Koju , Y. Mishin

Generative diffusion models are extensively used in unsupervised and self-supervised machine learning with the aim to generate new samples from a probability distribution estimated with a set of known samples. They have demonstrated…

Fluid Dynamics · Physics 2026-01-28 Wilfried Genuist , Éric Savin , Filippo Gatti , Didier Clouteau

We introduce a new framework that yields spectral bounds on norms of functions of transition maps for finite, homogeneous Markov chains. The techniques employed work for bounded semigroups, in particular for classical as well as for quantum…

Mathematical Physics · Physics 2015-03-16 Oleg Szehr , David Reeb , Michael M. Wolf

Diffusion models have demonstrated empirical successes in various applications and can be adapted to task-specific needs via guidance. This paper studies a form of gradient guidance for adapting a pre-trained diffusion model towards…

Machine Learning · Statistics 2024-10-17 Yingqing Guo , Hui Yuan , Yukang Yang , Minshuo Chen , Mengdi Wang

In this work, we consider a class of second order uniformly elliptic operators with smooth and bounded coefficients. We provide some estimates on the norm of the semigroup generated by these operators acting on weighted Sobolev spaces,…

Analysis of PDEs · Mathematics 2022-12-06 Maxime Hauray , Yen V. Vuong

In this paper we construct a methodology for separating the divergencies due to different topological manifolds dual to Feynman graphs in colored group field theory. After having introduced the amplitude bounds using propagator cuts, we…

General Relativity and Quantum Cosmology · Physics 2013-11-27 Francesco Caravelli

The aim of this paper is to present some results about generation, sectoriality and gradient estimates both for the semigroup and for the resolvent of suitable realizations of the operators [\Ab u(x)=\gamma xu"(x) + b u'(x),] with constants…

Functional Analysis · Mathematics 2013-02-14 Angela A. Albanese , Elisabetta M. Mangino

We prove Cameron-Martin type quasi-invariance results for the heat kernel measure of infinite-dimensional Kolmogorov and related diffusions. We first study quantitative functional inequalities for appropriate finite-dimensional…

Probability · Mathematics 2021-07-20 Fabrice Baudoin , Maria Gordina , Tai Melcher

The gradient discretisation method (GDM) is a generic framework for designing and analysing numerical schemes for diffusion models. In this paper, we study the GDM for the porous medium equation, including fast diffusion and slow diffusion…

Numerical Analysis · Mathematics 2020-04-02 Jerome Droniou , Kim-Ngan Le

Semi-Lagrangian methods have traditionally been developed in the framework of hyperbolic equations, but several extensions of the Semi-Lagrangian approach to diffusion and advection--diffusion problems have been proposed recently. These…

Numerical Analysis · Mathematics 2014-05-20 L. Bonaventura , R. Ferretti

We provide pointwise upper bounds for the transition kernels of semigroups associated with a class of systems of nondegenerate elliptic partial differential equations with unbounded coefficients with possibly unbounded diffusion…

Analysis of PDEs · Mathematics 2024-12-23 Davide Addona , Luca Lorenzi , Marianna Porfido

A version of fractional diffusion on bounded domains, subject to 'homogeneous Dirichlet boundary conditions' is derived from a kinetic transport model with homogeneous inflow boundary conditions. For nonconvex domains, the result differs…

Analysis of PDEs · Mathematics 2016-07-05 Pedro Aceves-Sanchez , Christian Schmeiser

We consider difference schemes for the time-fractional diffusion equation with variable coefficients and nonlocal boundary conditions containing real parameters $\alpha$, $\beta$ and $\gamma$. By the method of energy inequalities, for the…

Numerical Analysis · Mathematics 2014-05-02 A. A. Alikhanov

We derive a Dickman approximation for the small jumps of a large class of multivariate L\'evy processes. We then apply this approximation to develop a simulation method for the class of general multivariate gamma distributions (GMGD). A…

Probability · Mathematics 2025-09-19 Michael Grabchak , Xingnan Zhang

The main goal of this paper is to define and study new methods for the computation of effective coefficients in the homogenization of divergence-form operators with random coefficients. The methods introduced here are proved to have optimal…

Numerical Analysis · Mathematics 2017-11-01 Jean-Christophe Mourrat