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This paper presents an analysis of properties of two hybrid discretization methods for Gaussian derivatives, based on convolutions with either the normalized sampled Gaussian kernel or the integrated Gaussian kernel followed by central…

Numerical Analysis · Mathematics 2025-01-31 Tony Lindeberg

We use the polyharmonic extension approach to develop a numerical technique for discretizing higher-order powers of the spectral fractional Laplacian $(-\Delta)^s$ with $s \in (1,2)$.

Numerical Analysis · Mathematics 2026-03-10 Enrique Otárola , Abner J. Salgado

A discrete gradient model for interfaces is studied. The interaction potential is a non-convex perturbation of the quadratic gradient potential. Based on a representation for the finite volume Gibbs measure obtained via a renormalization…

Mathematical Physics · Physics 2016-03-16 Susanne Hilger

In this paper we deduce a formula for the fractional Laplace operator $(-\Delta)^{s}$ on radially symmetric functions useful for some applications. We give a criterion of subharmonicity associated with $(-\Delta)^{s}$, and apply it to a…

Analysis of PDEs · Mathematics 2012-03-15 Fausto Ferrari , Igor E. Verbitsky

This paper develops an in-depth treatment concerning the problem of approximating the Gaussian smoothing and Gaussian derivative computations in scale-space theory for application on discrete data. With close connections to previous…

Computer Vision and Pattern Recognition · Computer Science 2024-06-25 Tony Lindeberg

We consider scalar field theory defined over a direct product of the real and $p$-adic numbers. An adjustable dynamical scaling exponent $z$ enters into the microscopic lagrangian, so that the Gaussian theories provide a line of fixed…

High Energy Physics - Theory · Physics 2020-01-29 Steven S. Gubser , Christian Jepsen , Ziming Ji , Brian Trundy

We show that the centered maximum of a sequence of log-correlated Gaussian fields in any dimension converges in distribution, under the assumption that the covariances of the fields converge in a suitable sense. We identify the limit as a…

Probability · Mathematics 2024-02-23 Jian Ding , Rishideep Roy , Ofer Zeitouni

To connect conformal field theories (CFT) to probabilistic lattice models, recent works [HKV22, Ada23] have introduced a novel definition of local fields of the lattice models. Local fields in this picture are probabilistically concrete:…

Mathematical Physics · Physics 2024-07-30 David Adame-Carrillo , Delara Behzad , Kalle Kytölä

The Discrete Gaussian model is the lattice Gaussian free field conditioned to be integer-valued. In two dimensions, at sufficiently high temperature, we show that its macroscopic scaling limit on the torus is a multiple of the Gaussian free…

Probability · Mathematics 2024-07-11 Roland Bauerschmidt , Jiwoon Park , Pierre-François Rodriguez

The fractional Laplacian $(-\Delta)^{\alpha/2}$ is a non-local operator which depends on the parameter $\alpha$ and recovers the usual Laplacian as $\alpha \to 2$. A numerical method for the fractional Laplacian is proposed, based on the…

Numerical Analysis · Mathematics 2014-11-14 Yanghong Huang , Adam Oberman

We consider a family of gradient Gaussian vector fields on $\Z^d$, where the covariance operator is not translation invariant. A uniform finite range decomposition of the corresponding covariance operators is proven, i.e., the covariance…

Mathematical Physics · Physics 2015-10-27 Eris Runa

Maxima of the linear density field form a point process that can be used to understand the spatial distribution of virialized halos that collapsed from initially overdense regions. However, owing to the peak constraint, clustering…

Cosmology and Nongalactic Astrophysics · Physics 2013-05-30 Vincent Desjacques

In this article, we study complex Gaussian multiplicative chaos. More precisely, we study the renormalization theory and the limit of the exponential of a complex log-correlated Gaussian field in all dimensions (including Gaussian Free…

Probability · Mathematics 2015-02-17 Hubert Lacoin , Rémi Rhodes , Vincent Vargas

This paper is devoted to the problem of sampling Gaussian fields in high dimension. Solutions exist for two specific structures of inverse covariance : sparse and circulant. The proposed approach is valid in a more general case and…

Computation · Statistics 2011-05-31 F. Orieux , O. Féron , J. -F. Giovannelli

We propose an aggregated random-field model, and investigate the scaling limits of the aggregated partial-sum random fields. In our model, each copy of the random field in the aggregation is built from two correlated one-dimensional random…

Probability · Mathematics 2019-07-29 Yi Shen , Yizao Wang

The computation of multifractal scaling properties associated with a critical field theory involves non-local operators and remains an open problem using conventional techniques of field theory. We propose a new description of Gaussian…

Condensed Matter · Physics 2009-10-28 Claudio de C. Chamon , Christopher Mudry , Xiao-Gang Wen

Variational approximation methods have proven to be useful for scaling Bayesian computations to large data sets and highly parametrized models. Applying variational methods involves solving an optimization problem, and recent research in…

Methodology · Statistics 2017-01-13 Victor M. -H. Ong , David J. Nott , Michael S. Smith

We show that the rescaled maximum of the discrete Gaussian Free Field (DGFF) in dimension larger or equal to 3 is in the maximal domain of attraction of the Gumbel distribution. The result holds both for the infinite-volume field as well as…

Probability · Mathematics 2016-04-05 Alberto Chiarini , Alessandra Cipriani , Rajat Subhra Hazra

We define and study the Dirichlet fractional Gaussian fields on the Sierpinski gasket and show that they are limits of fractional discrete Gaussian fields defined on the sequence of canonical approximating graphs.

Probability · Mathematics 2023-05-12 Fabrice Baudoin , Li Chen

In this article we aim at defining the discrete Gaussian free field (DGFF) on a compact manifold. Since there is no canonical grid approximation of a manifold, we construct a random graph that suitably replaces the square lattice…

Probability · Mathematics 2020-01-07 Alessandra Cipriani , Bart van Ginkel