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arXiv 1907.03332math.PRcs.NAmath.NA
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基于高斯分析的高维Kolmogorov方程数值解法

A numerical approach to Kolmogorov equation in high dimension based on Gaussian analysis

Franco Flandoli, Dejun Luo, Cristiano Ricci

摘要:

针对高维有限维随机微分方程的Kolmogorov方程,提出了一种替代Monte Carlo模拟的数值方法.的结构的启发随机偏微分方程(SPDE),因此包含一个潜在的高斯过程,这是算法的关键。一系列的发展的解决方案中的迭代积分的高斯过程中,它被证明是收敛的-也在无限维的限制-它是数值测试中的一些例子。

英文摘要:

For Kolmogorov equations associated to finite dimensional stochastic differential equations (SDEs) in high dimension, a numerical method alternative to Monte Carlo simulations is proposed. The structure of the SDE is inspired by stochastic Partial Differential Equations (SPDE) and thus contains an underlying Gaussian process which is the key of the algorithm. A series development of the solution in terms of iterated integrals of the Gaussian process is given, it is proved to converge - also in the infinite dimensional limit - and it is numerically tested in a number of examples.

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。

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