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In this paper, we present an almost sure central limit theorem (ASCLT) for the hyperbolic Anderson model (HAM) with a L\'evy white noise in a finite-variance setting, complementing a recent work by Balan and Zheng…

Probability · Mathematics 2025-10-16 Raluca M. Balan , Panqiu Xia , Guangqu Zheng

In this article, we study the asymptotic behaviour of the spatial integral $F_R(t)$ of the solution to the hyperbolic Anderson model in dimension $d=1$, driven by the L\'evy colored noise introduced in Balan and Jim\'enez (2026). We assume…

Probability · Mathematics 2026-02-27 Raluca M. Balan , William D. Stephenson

This short note is devoted to establishing the almost sure central limit theorem for the parabolic/hyperbolic Anderson models driven by colored-in-time Gaussian noises, completing recent results on quantitative central limit theorems for…

Probability · Mathematics 2025-04-01 Panqiu Xia , Guangqu Zheng

In this paper, we study the spatial averages of the solution to the parabolic Anderson model driven by a space-time Gaussian homogeneous noise that is colored in time and space. We establish quantitative central limit theorems (CLT) of this…

Probability · Mathematics 2022-10-13 David Nualart , Panqiu Xia , Guangqu Zheng

In this paper, we study one-dimensional hyperbolic Anderson models (HAM) driven by space-time pure-jump L\'evy white noise in a finite-variance setting. Motivated by recent active research on limit theorems for stochastic partial…

Probability · Mathematics 2024-03-04 Raluca M. Balan , Guangqu Zheng

We consider stochastic wave equations in spatial dimensions $d \geq 4$. We assume that the driving noise is given by a Gaussian noise that is white in time and has some spatial correlation. When the spatial correlation is given by the Riesz…

Probability · Mathematics 2025-01-09 Masahisa Ebina

In this article, we study the hyperbolic Anderson model driven by a space-time \emph{colored} Gaussian homogeneous noise with spatial dimension $d=1,2$. Under mild assumptions, we provide $L^p$-estimates of the iterated Malliavin derivative…

Probability · Mathematics 2022-01-20 Raluca M. Balan , David Nualart , Lluís Quer-Sardanyons , Guangqu Zheng

In this article, we study the asymptotic behavior of the spatial integral of the solution to the hyperbolic Anderson model in dimension $d\leq 2$, as the domain of the integral gets large (for fixed time $t$). This equation is driven by a…

Probability · Mathematics 2022-01-19 Raluca M. Balan , Wangjun Yuan

In this article, we investigate the asymptotic behaviour of the spatial integral of the solution to the parabolic Anderson model with time independent noise in dimension $d\geq 1$, as the domain of the integral becomes large. We consider 3…

Probability · Mathematics 2022-05-27 Raluca M. Balan , Wangjun Yuan

In this work, we investigate the asymptotic behavior of integral functionals of stationary Gaussian random fields as the integration domain tends to be the whole space. More precisely, using the Wiener chaos expansion and Malliavin-Stein…

Probability · Mathematics 2026-05-18 Leonardo Maini , Maurizia Rossi , Guangqu Zheng

For a large class of expanding maps of the interval, we prove that partial sums of Lipschitz observables satisfy an almost sure central limit theorem (ASCLT). In fact, we provide a speed of convergence in the Kantorovich metric. Maxima of…

Probability · Mathematics 2008-05-15 J. -R. Chazottes , P. Collet

We investigate an almost sure limit theorem (ASCLT) for sequences of random variables having the form of a ratio of two terms such that the numerator satisfies the ASCLT and the denominator is a positive term which converges almost surely…

Probability · Mathematics 2012-09-04 Peggy Cénac , Khalifa Es-Sebaiy

In this paper, we study the random field solution to the stochastic nonlinear wave equation (SNLW) with constant initial conditions and multiplicative noise $\sigma(u)\dot{L}$, where the nonlinearity is encoded in a Lipschitz function…

Probability · Mathematics 2026-04-15 Raluca M. Balan , Guangqu Zheng

Let $\{u(t,x)\}_{t>0,x\in{{\mathbb R}^{d}}}$ denote the solution to a $d$-dimensional parabolic Anderson model with delta initial condition and driven by a multiplicative noise that is white in time and has a spatially homogeneous…

Probability · Mathematics 2024-11-05 Wanying Zhang , Yong Zhang , Jingyu Li

In this article, we study the hyperbolic Anderson model in dimension 1, driven by a time-independent rough noise, i.e. the noise associated with the fractional Brownian motion of Hurst index $H \in (1/4,1/2)$. We prove that, with…

Probability · Mathematics 2023-05-10 Raluca M. Balan , Wangjun Yuan

We consider stochastic approximations of sampling algorithms, such as Stochastic Gradient Langevin Dynamics (SGLD) and the Random Batch Method (RBM) for Interacting Particle Dynamcs (IPD). We observe that the noise introduced by the…

Probability · Mathematics 2023-10-10 Aniket Das , Dheeraj Nagaraj , Anant Raj

In this paper, we present a rate of convergence in the uniform norm for the densities of spatial averages of the solution to the d-dimensional parabolic Anderson model driven by a Gaussian multiplicative noise, which is white in time and…

Probability · Mathematics 2022-05-30 Sefika Kuzgun , David Nualart

We consider the stochastic inviscid Leray-$\alpha$ model on the torus driven by transport noise. Under a suitable scaling of the noise, we prove that the weak solutions converge, in some negative Sobolev spaces, to the unique solution of…

Probability · Mathematics 2023-05-04 Dejun Luo , Bin Tang

We examine the almost-sure asymptotics of the solution to the stochastic heat equation driven by a L\'evy space-time white noise. When a spatial point is fixed and time tends to infinity, we show that the solution develops unusually high…

Probability · Mathematics 2020-06-18 Carsten Chong , Péter Kevei

Fix $d\in\{1,2\}$, we consider a $d$-dimensional stochastic wave equation driven by a Gaussian noise, which is temporally white and colored in space such that the spatial correlation function is integrable and satisfies Dalang's condition.…

Probability · Mathematics 2021-08-18 David Nualart , Guangqu Zheng
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