arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1803.09488math.PR
收藏

解线性抛物型粗糙偏微分方程

Solving linear parabolic rough partial differential equations

Christian Bayer, Denis Belomestny, Martin Redmann, Sebastian Riedel, John Schoenmakers

摘要:

我们在[Friz and Hairer,Springer,2014,Chapter 12]的背景下研究线性粗糙偏微分方程。更精确地说,我们考虑了一个线性抛物型偏微分方程驱动的确定性粗糙路径$\mathbf{W}$的Hölder正则性$α$与$1/3 < α \le 1/2$。基于粗糙偏微分方程解的随机表示,提出了一种时空近似的回归蒙特卡罗算法。我们提供了一个完整的收敛性分析所提出的近似方法,基本上依赖于新的边界的高阶导数的解决方案在空间中。最后,一个全面的仿真研究表明所提出的算法的适用性。

英文摘要:

We study linear rough partial differential equations in the setting of [Friz and Hairer, Springer, 2014, Chapter 12]. More precisely, we consider a linear parabolic partial differential equation driven by a deterministic rough path $\mathbf{W}$ of Hölder regularity $α$ with $1/3 < α \le 1/2$. Based on a stochastic representation of the solution of the rough partial differential equation, we propose a regression Monte Carlo algorithm for spatio-temporal approximation of the solution. We provide a full convergence analysis of the proposed approximation method which essentially relies on the new bounds for the higher order derivatives of the solution in space. Finally, a comprehensive simulation study showing the applicability of the proposed algorithm is presented.

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。

↑