@inproceedings{d5804f04111549ebb44987c9503a13da,
title = "A deep learning-based high-order operator splitting method for high-dimensional nonlinear parabolic PDEs via Malliavin calculus: application to CVA computation",
abstract = "The paper introduces a deep learning-based high-order operator splitting method for nonlinear parabolic partial differential equations (PDEs) by using a Malliavin calculus approach. Through the method, a solution of a nonlinear PDE is accurately approximated even when the dimension of the PDE is high. As an application, the method is applied to the CVA computation in high-dimensional finance models. Numerical experiments performed on GPUs show the efficiency of the proposed method.",
keywords = "CVA, Deep learning, GPU, Malliavin calculus, Nonlinear parabolic PDEs",
author = "Riu Naito and Toshihiro Yamada",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics, CIFEr 2022 ; Conference date: 04-05-2022 Through 05-05-2022",
year = "2022",
doi = "10.1109/CIFEr52523.2022.9776096",
language = "英語",
series = "2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics, CIFEr 2022 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics, CIFEr 2022 - Proceedings",
}