Generalized Stochastic Burgers' Equation with Non-Lipschitz Diffusion Coefficient

Multidimensional Stochastic Burgers Equation

We consider multidimensional stochastic Burgers equation on the torus T and the whole space R . In both cases we show that for positive viscosity ν > 0 there exists a unique strong global solution in L for p > d. In the case of torus we also establish a uniform in ν a priori estimate and consider a limit ν ↘ 0 for potential solutions. In the case of R uniform with respect to ν a priori estimate...

Reproducing Kernel Space Hilbert Method for Solving Generalized Burgers Equation

In this paper, we present a new method for solving Reproducing Kernel Space (RKS) theory, and iterative algorithm for solving Generalized Burgers Equation (GBE) is presented. The analytical solution is shown in a series in a RKS, and the approximate solution u(x,t) is constructed by truncating the series. The convergence of u(x,t) to the analytical solution is also proved.

Algorithm refinement for the stochastic Burgers' equation

In this paper, we develop an algorithm refinement (AR) scheme for an excluded random walk model whose mean field behavior is given by the viscous Burgers’ equation. AR hybrids use the adaptive mesh refinement framework to model a system using a molecular algorithm where desired while allowing a computationally faster continuum representation to be used in the remainder of the domain. The focus ...

Large Deviations for a Stochastic Burgers’ Equation

We prove the large deviations principle (LDP) for the law of the solutions to a stochastic Burgers’ equation in the presence of an additive noise. Our proof is based on the weak convergence approach.

On Numerical Approximation of Stochastic Burgers’ Equation