# PDGM: a Neural Network Approach to Solve Path-Dependent Partial Differential Equations

@article{Saporito2020PDGMAN, title={PDGM: a Neural Network Approach to Solve Path-Dependent Partial Differential Equations}, author={Yuri F. Saporito and Zhaoyu Zhang}, journal={arXiv: Computational Finance}, year={2020} }

In this paper, we propose a novel numerical method for Path-Dependent Partial Differential Equations (PPDEs). These equations firstly appeared in the seminal work of Dupire [2009], where the functional It\^o calculus was developed to deal with path-dependent financial derivatives contracts. More specificaly, we generalize the Deep Galerking Method (DGM) of Sirignano and Spiliopoulos [2018] to deal with these equations. The method, which we call Path-Dependent DGM (PDGM), consists of using a… Expand

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