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arXiv 2206.06451math.PRcs.NAmath.NA
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通过深度学习方法求解希尔伯特空间中的Kolmogorov Infinite Dimensional方程

The Kolmogorov Infinite Dimensional Equation in a Hilbert space Via Deep Learning Methods

Javier Castro

摘要:

本文考虑了希尔伯特空间$H$中的非线性Kolmogorov方程,该空间不一定是有限维的. Cox等人最近在随机波动模型的弱收敛率框架中研究了该模型。在这里,我们提出了一种补充方法,通过提供无限维深度学习方法来近似该模型的合适解。基于Hure,Pham和Warin [45]关于有限维情形的工作,以及我们以前关于Lévy基过程的工作[20],我们将正倒向随机微分方程的Euler格式和相容性结果推广到无穷维Hilbert值情形.由于我们的框架是通用的,我们需要最近开发的DeepOnets神经网络[21,51]来详细描述近似过程。此外,由Fuhrman和Tessitore [35]开发的用于充分描述随机逼近的框架将适用于我们的设置

英文摘要:

We consider the nonlinear Kolmogorov equation posed in a Hilbert space $H$, not necessarily of finite dimension. This model was recently studied by Cox et al. [24] in the framework of weak convergence rates of stochastic wave models. Here, we propose a complementary approach by providing an infinite-dimensional Deep Learning method to approximate suitable solutions of this model. Based in the work by Hure, Pham and Warin [45] concerning the finite dimensional case, and our previous work [20] dealing with Lévy based processes, we generalize an Euler scheme and consistency results for the Forward Backward Stochastic Differential Equations to the infinite dimensional Hilbert valued case. Since our framework is general, we require the recently developed DeepOnets neural networks [21, 51] to describe in detail the approximation procedure. Also, the framework developed by Fuhrman and Tessitore [35] to fully describe the stochastic approximations will be adapted to our setting

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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