High-dimensional parabolic partial integro-differential equations (PIDEs) appear in many applications in insurance and finance. Existing numerical methods suffer from the curse of dimensionality or provide solutions only for a given space-time point. This gave rise to a growing literature on deep learning based methods for solving partial differential equations; results for integro-differential equations on the other hand are scarce. In this paper we consider an extension of the deep splitting scheme due to Beck, Becker, Cheridito, Jentzen, Neufeld (2021) and Germain, Pham, Warin (2022) to PIDEs. Our main contribution is a convergence analysis of the scheme. Moreover we discuss several test case studies to show the viability of our approach.
翻译:在保险和金融的许多应用中,出现了高维面抛物线部分分化方程式(PIDES),现有数字方法受到维度的诅咒,或只为某一时点提供解决办法,这导致关于解决部分差别方程式的深层次学习基础方法的文献越来越多;另一方面,异质分化方程式的结果很少。在本文中,我们认为由于Beck、Becker、Ceridito、Jentzen、Neutfeld(2021年)和Germain、Pham、Warin(2022年)对PIDES的深刻分裂计划延伸,我们对该计划的主要贡献是趋同性分析。此外,我们讨论了若干试验案例研究,以显示我们的方法的可行性。