arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1005.4438math.PRcs.NAmath.NA
收藏

随机Burgers方程的近似

Approximations to the Stochastic Burgers Equation

Martin Hairer, Jochen Voss

AI总结:

研究随机Burgers方程的不同有限差分近似,发现当驱动噪声为时空白噪声时,不同格式在网格尺寸趋零时收敛到不同极限过程,并给出理论解释与数值证据。

摘要:

本文致力于随机Burgers方程的各种有限差分近似的数值研究。在一维情形中,特别关注驱动噪声在空间和时间上均为白噪声的情况。我们证明,在这种情况下,随着网格尺寸趋于零,不同的有限差分格式收敛到不同的极限过程。给出了这一现象的理论解释,并针对更广泛的方程类别提出了若干猜想,这些猜想得到了数值证据的支持。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑