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arXiv 1802.04740math.AP
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完全非线性随机偏微分方程黏性解的逼近格式

Approximation schemes for viscosity solutions of fully nonlinear stochastic partial differential equations

Benjamin Seeger

摘要:

本文的目的是发展一种构造完全非线性路径随机偏微分方程黏性解的逼近格式并证明其收敛性的一般方法。我们的结果适用于近似,如显式有限差分格式和Trotter-Kato型混合公式。不规则的时间依赖性破坏了通常的方法从经典的粘性理论创建计划,是单调和收敛的,一个障碍,不能克服的高阶校正项,如随机或粗糙的常微分方程的数值逼近。这里的新颖之处在于,为了保证单调性和收敛性,用非平凡的二次变化来正则化这些驱动路径。我们提出了定性和定量的结果,前者涵盖了各种各样的计划二阶方程。在Hamilton-Jacobi情形下建立了一个误差估计,其优点是它只依赖于通过连续模的路径,而不依赖于导数或全变差.因此,可以选择路径的正则化,以便获得有效的收敛速率。这在具有乘性白噪声的方程在时间上的特定设置中被证明,在这种情况下,收敛以概率1保持。我们还提出了一个例子,使用缩放的随机游动,表现出依分布收敛。

英文摘要:

The aim of this paper is to develop a general method for constructing approximation schemes for viscosity solutions of fully nonlinear pathwise stochastic partial differential equations, and for proving their convergence. Our results apply to approximations such as explicit finite difference schemes and Trotter-Kato type mixing formulas. The irregular time dependence disrupts the usual methods from the classical viscosity theory for creating schemes that are both monotone and convergent, an obstacle that cannot be overcome by incorporating higher order correction terms, as is done for numerical approximations of stochastic or rough ordinary differential equations. The novelty here is to regularize those driving paths with non-trivial quadratic variation in order to guarantee both monotonicity and convergence. We present qualitative and quantitative results, the former covering a wide variety of schemes for second-order equations. An error estimate is established in the Hamilton-Jacobi case, its merit being that it depends on the path only through the modulus of continuity, and not on the derivatives or total variation. As a result, it is possible to choose a regularization of the path so as to obtain efficient rates of convergence. This is demonstrated in the specific setting of equations with multiplicative white noise in time, in which case the convergence holds with probability one. We also present an example using scaled random walks that exhibits convergence in distribution.

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