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In this paper, we investigate the stochastic damped Burgers equation with multiplicative noise defined on the entire real line. We demonstrate the existence and uniqueness of a mild solution to the stochastic damped Burgers equation and…

Dynamical Systems · Mathematics 2025-06-10 Zhenxin Liu , Zhiyuan Shi

Burgers equation is one of the simplest nonlinear partial differential equations-it combines the basic processes of diffusion and nonlinear steepening. In some applications it is appropriate for the diffusion coefficient to be a…

Mathematical Physics · Physics 2007-05-23 Zhenquan Li , A. J. Roberts

Short wave equations were introduced in connection with the nonlinear reflection of weak shock waves. They also relate to the modulation of a gas-fluid mixture. Khokhlov-Zabolotskaya equation are used to describe the propagation of a…

Mathematical Physics · Physics 2008-11-25 Xiaoping Xu

We study a generalized 1d periodic SPDE of Burgers type: $$ \partial_t u =- A^\theta u + \partial_x u^2 + A^{\theta/2} \xi $$ where $\theta > 1/2$, $-A$ is the 1d Laplacian, $\xi$ is a space-time white noise and the initial condition $u_0$…

Probability · Mathematics 2013-04-10 M. Gubinelli , M. Jara

We develop a new method to uniquely solve a large class of heat equations, so-called Kolmogorov equations in infinitely many variables. The equations are analyzed in spaces of sequentially weakly continuous functions weighted by proper…

Probability · Mathematics 2016-08-16 Michael Röckner , Zeev Sobol

We employ weak hypocoercivity methods to study the long-term behavior of operator semigroups generated by degenerate Kolmogorov operators with variable second-order coefficients, which solve the associated abstract Cauchy problem. We prove…

Probability · Mathematics 2021-10-13 Alexander Bertram , Martin Grothaus

Strong convergence rates for numerical approximations of semilinear stochastic partial differential equations (SPDEs) with smooth and regular nonlinearities are well understood in the literature. Weak convergence rates for numerical…

Probability · Mathematics 2016-12-13 Mario Hefter , Arnulf Jentzen , Ryan Kurniawan

Motivated by applications to a manifold of semilinear and quasilinear stochastic partial differential equations (SPDEs) we establish the existence and uniqueness of strong solutions to coercive and locally monotone SPDEs driven by L\'{e}vy…

Analysis of PDEs · Mathematics 2013-05-22 Zdzisław Brzeźniak , Wei Liu , Jiahui Zhu

This paper aims to investigate the numerical approximation of a general second order parabolic stochastic partial differential equation(SPDE) driven by multiplicative and additive noise. Our main interest is on such SPDEs where the…

Numerical Analysis · Mathematics 2020-11-19 Jean Daniel Mukam , Antoine Tambue

Strong convergence rates for (temporal, spatial, and noise) numerical approximations of semilinear stochastic evolution equations (SEEs) with smooth and regular nonlinearities are well understood in the scientific literature. Weak…

Probability · Mathematics 2021-11-02 Daniel Conus , Arnulf Jentzen , Ryan Kurniawan

In this paper we prove the existence and uniqueness of strong solutions for SPDE in Hilbert space with locally monotone coefficients, which is a generalization of the classical result of Krylov and Rozovskii for monotone coefficients. Our…

Probability · Mathematics 2010-10-25 Wei Liu , Michael Röckner

We consider a nonlinear stochastic partial differential equation (SPDE) in divergence form where the forcing term is a Gaussian noise, that is white in time and colored in space such that the gradient of the solution is H\"older-continuous,…

Analysis of PDEs · Mathematics 2022-02-03 Florian Kunick

We associate backward and forward Kolmogorov equations to a class of fully nonlinear Stochastic Volterra Equations (SVEs) with convolution kernels $K$ that are singular at the origin. Working on a carefully chosen Hilbert space…

Probability · Mathematics 2025-09-29 Ioannis Gasteratos , Alexandre Pannier

The paper introduces a very simple and fast computation method for high-dimensional integrals to solve high-dimensional Kolmogorov partial differential equations (PDEs). The new machine learning-based method is obtained by solving a…

Numerical Analysis · Mathematics 2021-02-12 Riu Naito , Toshihiro Yamada

In this paper we treat semilinear stochastic partial differential equations by two methods. First, we extend the framework of [BDR10] from a Hilbert space to a Gelfand triple and as an application we prove the existence of solutions for the…

Probability · Mathematics 2014-02-05 Michael Röckner , Rongchan Zhu , Xiangchan Zhu

We formulate a short-time expansion for one-dimensional Fokker-Planck equations with spatially dependent diffusion coefficients, derived from stochastic processes with Gaussian white noise, for general values of the discretization parameter…

Biological Physics · Physics 2026-02-16 Tom Dupont , Stefano Giordano , Fabrizio Cleri , Ralf Blossey

This work is concerned with the development of a family of Galerkin finite element methods for the classical Kolmogorov's equation. Kolmogorov's equation serves as a sufficiently rich, for our purposes, model problem for kinetic-type…

Numerical Analysis · Mathematics 2020-12-18 Emmanuil H. Georgoulis

In this paper, we study the regularities of solutions of nonlinear stochastic partial differential equations in the framework of Hilbert scales. Then we apply our general result to several typical nonlinear SPDEs such as stochastic Burgers…

Probability · Mathematics 2008-01-28 Xicheng Zhang

Due to technical reasons, existing results concerning Harnack type inequalities for SPDEs with multiplicative noise apply only to the case where the coefficient in the noise term is an Hilbert-Schmidt perturbation of a fixed bounded…

Probability · Mathematics 2012-10-25 Feng-Yu Wang , Tusheng Zhang

We prove a modification to the classical maximal inequality for stochastic convolutions in 2-smooth Banach spaces using the factorization method. This permits to study semilinear stochastic partial differential equations with unbounded…

Probability · Mathematics 2020-10-20 Florian Bechtold