Approximations to the Stochastic Burgers Equation
This work highlights a fundamental issue in numerical analysis of stochastic PDEs for researchers in applied mathematics and computational science.
The paper studies finite difference approximations to the stochastic Burgers equation with space-time white noise, showing that different schemes converge to different limits as mesh size goes to zero, and provides theoretical explanation and conjectures.
This article is devoted to the numerical study of various finite difference approximations to the stochastic Burgers equation. Of particular interest in the one-dimensional case is the situation where the driving noise is white both in space and in time. We demonstrate that in this case, different finite difference schemes converge to different limiting processes as the mesh size tends to zero. A theoretical explanation of this phenomenon is given and we formulate a number of conjectures for more general classes of equations, supported by numerical evidence.