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Consider the stochastic partial differential equation $$ \frac{\partial }{\partial t}u_t(\mathbf{x})= -(-\Delta)^{\frac{\alpha}{2}}u_t(\mathbf{x}) +b\left(u_t(\mathbf{x})\right)+\sigma\left(u_t(\mathbf{x})\right) \dot F(t, \mathbf{x}), \ \…

Probability · Mathematics 2023-11-13 Ran Wang

We consider an Ito stochastic differential equation with delay, driven by brownian motion, whose solution, by an appropriate reformulation, defines a Markov process $X$ with values in a space of continuous functions $\mathbf C$, with…

Probability · Mathematics 2013-04-10 Marco Fuhrman , Federica Masiero , Gianmario Tessitore

Uniform large deviations for the laws of the paths of the solutions of the stochastic nonlinear Schrodinger equation when the noise converges to zero are presented. The noise is a real multiplicative Gaussian noise. It is white in time and…

Analysis of PDEs · Mathematics 2007-11-08 Eric Gautier

The key difficulty to develop efficient high-order methods for integrating stochastic differential equations lies in the calculations of the multiple stochastic integrals. This letter suggests a scheme to compute the stochastic integrals…

Chemical Physics · Physics 2019-09-30 Shuanglin Sun , Yun-An Yan

Stochastic Maxwell equations with additive noise are a system of stochastic Hamiltonian partial differential equations intrinsically, possessing the stochastic multi-symplectic conservation law.It is shown that the averaged energy increases…

Numerical Analysis · Mathematics 2015-09-29 Chuchu Chen , Jialin Hong , Liying Zhang

In the pathwise stochastic calculus framework, the paper deals with the general study of equations driven by an additive Gaussian noise, with a drift function having an infinite limit at point zero. An ergodic theorem and the convergence of…

Probability · Mathematics 2019-01-16 Nicolas Marie

In this paper, we extend the results of Elliott and Yang \cite{elliott3} and discuss the control of a stochastic process for which the driving noise is provided by a martingale associated with a semi-Markov Chain. An existence and a…

Probability · Mathematics 2025-12-23 Robert J. Elliott , Zhe Yang

In this paper, we establish the existence and uniqueness of invariant measures for a class of semilinear stochastic partial differential equations driven by multiplicative noise on a bounded domain. The main results can be applied to SPDEs…

Probability · Mathematics 2018-12-12 Zhao Dong , Rangrang Zhang

In this article, we consider the following class of stochastic partial differential equations (SPDE): \begin{equation*} \left\{\begin{aligned}\mathrm{d} \mathbf{X}(t)&=\mathrm{A}(t,\mathbf{X}(t))\mathrm{d}…

Probability · Mathematics 2022-09-15 Ankit Kumar , Manil T. Mohan

We consider multistage stochastic linear optimization problems combining joint dynamic probabilistic constraints with hard constraints. We develop a method for projecting decision rules onto hard constraints of wait-and-see type. We…

Optimization and Control · Mathematics 2016-09-16 Vincent Guigues , Rene Henrion

In this paper we prove the local existence and uniqueness of solutions for a class of stochastic fractional partial differential equations driven by multiplicative noise. We also establish that for this class of equations adding linear…

Probability · Mathematics 2013-07-17 Michael Rockner , Rongchan Zhu , Xiangchan Zhu

We consider ergodic backward stochastic differential equations in a discrete time setting, where noise is generated by a finite state Markov chain. We show existence and uniqueness of solutions, along with a comparison theorem. To obtain…

Probability · Mathematics 2015-09-02 Andrew L. Allan , Samuel N. Cohen

We develop in this work a numerical method for stochastic differential equations (SDEs) with weak second order accuracy based on Gaussian mixture. Unlike the conventional higher order schemes for SDEs based on It\^o-Taylor expansion and…

Numerical Analysis · Mathematics 2021-08-12 Lei Li , Jianfeng Lu , Jonathan Mattingly , Lihan Wang

We study the Kardar-Parisi-Zhang (KPZ) growth equation in one dimension with a noise variance $c(t)$ depending on time. We find that for $c(t)\propto t^{-\alpha}$ there is a transition at $\alpha=1/2$. When $\alpha>1/2$, the solution…

Statistical Mechanics · Physics 2020-04-29 Guillaume Barraquand , Pierre Le Doussal , Alberto Rosso

Cooperative effects of periodic force and noise in globally Cooperative effects of periodic force and noise in globally coupled systems are studied using a nonlinear diffusion equation for the number density. The amplitude of the order…

Chaotic Dynamics · Physics 2009-11-07 Hidetsugu Sakaguchi

Using the white noise space setting, we define and study stochastic integrals with respect to a class of stationary increment Gaussian processes. We focus mainly on continuous functions with values in the Kondratiev space of stochastic…

Probability · Mathematics 2010-08-03 Daniel Alpay , Haim Attia , David Levanony

In this manuscript we analyze the collective behavior of mean-field limits of large-scale, spatially extended stochastic neuronal networks with delays. Rigorously, the asymptotic regime of such systems is characterized by a very intricate…

Dynamical Systems · Mathematics 2017-02-21 Jonathan Touboul

The solvability of a delay differential equation arising in the construction of quadratic cost functionals, i.e. Lyapunov functionals, for a linear time-delay system with a constant and a distributed delay is investigated. We present a…

Systems and Control · Computer Science 2019-09-23 Suat Gumussoy , Murad Abu-Khalaf

This paper is concerned with the existence and uniqueness of weak solutions to the Cauchy-Dirichlet problem of backward stochastic partial differential equations (BSPDEs) with nonhomogeneous terms of quadratic growth in both the gradient of…

Probability · Mathematics 2012-07-24 Kai Du , Shaokuan Chen

We prove quantitative convergence rates at which discrete Langevin-like processes converge to the invariant distribution of a related stochastic differential equation. We study the setup where the additive noise can be non-Gaussian and…

Machine Learning · Computer Science 2020-11-20 Xiang Cheng , Dong Yin , Peter L. Bartlett , Michael I. Jordan
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