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Related papers: Divergence theorems in path space III: hypoellipti…

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Basic derivative formulas are presented for hypoelliptic heat semigroups and harmonic functions extending earlier work in the elliptic case. Emphasis is placed on developing integration by parts formulas at the level of local martingales.…

Probability · Mathematics 2010-05-02 Marc Arnaudon , Anton Thalmaier

In the context of cell motility modelling and more particularly related to the Filament Based Lamelipodium Model [Manhart et al 2015 & 2017], this work deals with a rigorous mathematical proof of convergence between solutions of two…

Analysis of PDEs · Mathematics 2020-05-20 Vuk Milisic

We prove convergence of symmetric diffusions on Wiener spaces by using stopping times arguments and capacity techniques. The drifts of the diffusions can be singular, we require the densities of the processes to be neither bounded from…

Probability · Mathematics 2007-05-23 Andrea Posilicano , Tusheng Zhang

The nonequilibrium Fokker-Planck dynamics with a non-conservative drift field, in dimension $N\geq 2$, can be related with the non-Hermitian quantum mechanics in a real scalar potential $V$ and in a purely imaginary vector potential -$iA$…

Statistical Mechanics · Physics 2024-05-31 P. Garbaczewski , M. Żaba

We propose and analyse a novel surface finite element method that preserves the invariant regions of systems of semilinear parabolic equations on closed compact surfaces in $\mathbb{R}^3$ under discretisation. We also provide a…

Numerical Analysis · Mathematics 2020-01-20 Massimo Frittelli , Anotida Madzvamuse , Ivonne Sgura , Chandrasekhar Venkataraman

Denoising Diffusion Probabilistic Models (DDPM) are powerful state-of-the-art methods used to generate synthetic data from high-dimensional data distributions and are widely used for image, audio, and video generation as well as many more…

Machine Learning · Statistics 2025-04-25 Iskander Azangulov , George Deligiannidis , Judith Rousseau

We develop a Helmholtz-like theorem for differential forms in Euclidean space $E_{n}$ using a uniqueness theorem similar to the one for vector fields. We then apply it to Riemannian manifolds, $R_{n}$, which, by virtue of the…

General Mathematics · Mathematics 2014-12-02 Jose G. Vargas

We study the well-posedness of a modified degenerate Cahn-Hilliard type model for surface diffusion. With degenerate phase-dependent diffusion mobility and additional stabilizing function, this model is able to give the correct sharp…

Analysis of PDEs · Mathematics 2022-04-19 Xiaohua Niu , Yang Xiang , Xiaodong Yan

In this article we review classical and recent results in anomalous diffusion and provide mechanisms useful for the study of the fundamentals of certain processes, mainly in condensed matter physics, chemistry and biology. Emphasis will be…

Statistical Mechanics · Physics 2019-02-25 Fernando A. Oliveira , Rogelma M. S. Ferreira , Luciano C. Lapas , Mendeli H. Vainstein

We study how the resolvent-family of a diffusion behaves, as thedrift grows to infinity. The limit turns out to be a selfadjoint pseudo-resolvent.After reduction of the underlying Hilbert-space, this pseudo-resolvent becomesa resolvent to a…

Probability · Mathematics 2024-08-22 Brice Franke , Shuenn-Jyi Sheu

A limit theorem for a sequence of diffusion processes on graphs is proved in a case when vary both parameters of the processes (the drift and diffusion coefficients on every edge and the asymmetry coefficients in every vertex), and…

Probability · Mathematics 2007-05-23 Alexey M. Kulik

The paper deals with second order parabolic equations on bounded domains with Dirichlet conditions in arbitrary Euclidean spaces. Their interest comes from being models for describing reaction-diffusion processes in several frameworks. A…

Analysis of PDEs · Mathematics 2018-09-10 Irene Benedetti , Luisa Malaguti , Valentina Taddei

Denoising diffusion models are a novel class of generative algorithms that achieve state-of-the-art performance across a range of domains, including image generation and text-to-image tasks. Building on this success, diffusion models have…

Machine Learning · Computer Science 2024-03-08 Nic Fishman , Leo Klarner , Valentin De Bortoli , Emile Mathieu , Michael Hutchinson

We propose an approach to approximate the boundary crossing probabilities for general one-dimensional diffusion processes, and derive the convergence rate for this approximation scheme. There results are based on the explicit expression of…

Probability · Mathematics 2015-10-28 Jinghai Shao , Liqun Wang

Diffusion models are recent state-of-the-art methods for image generation and likelihood estimation. In this work, we generalize continuous-time diffusion models to arbitrary Riemannian manifolds and derive a variational framework for…

Machine Learning · Computer Science 2022-08-18 Chin-Wei Huang , Milad Aghajohari , Avishek Joey Bose , Prakash Panangaden , Aaron Courville

Describing particle transport at the macroscopic or mesoscopic level in non-ideal environments poses fundamental theoretical challenges in domains ranging from inter and intra-cellular transport in biology to diffusion in porous media. Yet,…

Statistical Mechanics · Physics 2013-09-11 Marta Galanti , Duccio Fanelli , Francesco Piazza

We present a proof for the existence and uniqueness of weak solutions for a cut-off and non cut-off model of non-linear diffusion equation in finite-dimensional space RD useful for modelling flows on porous medium with saturation, turbulent…

General Physics · Physics 2019-08-22 Luiz Carlos Lobato Botelho

This paper presents an extension of the unfolding operator technique, initially applied to two-dimensional domains, to the realm of three-dimensional thin domains. The advancement of this methodology is pivotal, as it enhances our…

Analysis of PDEs · Mathematics 2024-05-10 José M. Arrieta , Jean Carlos Nakasato , Manuel Villanueva-Pesqueira

Calculus and geometry are ubiquitous in the theoretical modelling of scientific phenomena, but have historically been very challenging to apply directly to real data as statistics. Diffusion geometry is a new theory that reformulates…

Differential Geometry · Mathematics 2026-02-09 Iolo Jones , David Lanners

Score-matching generative models have proven successful at sampling from complex high-dimensional data distributions. In many applications, this distribution is believed to concentrate on a much lower $d$-dimensional manifold embedded into…

Machine Learning · Statistics 2025-04-25 Peter Potaptchik , Iskander Azangulov , George Deligiannidis