A Deep BSDE approximation of nonlinear integro-PDEs with unbounded nonlocal operators

Fuente: arXiv
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Auteurs principaux: Jakobsen, Espen Robstad, Mazid, Sehail
Format: Preprint
Publié: 2024
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author Jakobsen, Espen Robstad
Mazid, Sehail
author_facet Jakobsen, Espen Robstad
Mazid, Sehail
contents Machine learning for partial differential equations (PDEs) is a hot topic. In this paper we introduce and analyse a Deep BSDE scheme for nonlinear integro-PDEs with unbounded nonlocal operators -problems arising in e.g. stochastic control and games involving infinite activity jump-processes. The scheme is based on a stochastic forward-backward SDE representation of the solution of the PDE and (i) approximation of small jumps by a Gaussian process, (ii) simulation of the forward part, and (iii) a neural net regression for the backward part. Unlike grid-based schemes, it does not suffer from the curse of dimensionality and is therefore suitable for high dimensional problems. The scheme is designed to be convergent even in the infinite activity/unbounded nonlocal operator case. A full convergence analysis is given and constitutes the main part of the paper.
format Preprint
id arxiv_https___arxiv_org_abs_2407_09284
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A Deep BSDE approximation of nonlinear integro-PDEs with unbounded nonlocal operators
Jakobsen, Espen Robstad
Mazid, Sehail
Analysis of PDEs
Numerical Analysis
Machine learning for partial differential equations (PDEs) is a hot topic. In this paper we introduce and analyse a Deep BSDE scheme for nonlinear integro-PDEs with unbounded nonlocal operators -problems arising in e.g. stochastic control and games involving infinite activity jump-processes. The scheme is based on a stochastic forward-backward SDE representation of the solution of the PDE and (i) approximation of small jumps by a Gaussian process, (ii) simulation of the forward part, and (iii) a neural net regression for the backward part. Unlike grid-based schemes, it does not suffer from the curse of dimensionality and is therefore suitable for high dimensional problems. The scheme is designed to be convergent even in the infinite activity/unbounded nonlocal operator case. A full convergence analysis is given and constitutes the main part of the paper.
title A Deep BSDE approximation of nonlinear integro-PDEs with unbounded nonlocal operators
topic Analysis of PDEs
Numerical Analysis
url https://arxiv.org/abs/2407.09284