The deep multi-FBSDE method: a robust deep learning method for coupled FBSDEs

2025/03/17 by Andersson, Kristoffer, Andersson, Adam, Oosterlee, Cornelis W. · 2 citations
#35K58 #60H35 #68T07 #93E20 #Computational Finance (q-fin.CP) #FOS: Economics and business #FOS: Mathematics #Numerical Analysis (math.NA) #Optimization and Control (math.OC)

paper · doi:10.48550/arxiv.2503.13193

Abstract

We introduce the deep multi-FBSDE method for robust approximation of coupled forward-backward stochastic differential equations (FBSDEs), focusing on cases where the deep BSDE method of Han, Jentzen, and E (2018) fails to converge. To overcome the convergence issues, we consider a family of FBSDEs that are equivalent to the original problem in the sense that they satisfy the same associated partial differential equation (PDE). Our algorithm proceeds in two phases: first, we approximate the initial condition for the FBSDE family, and second, we approximate the original FBSDE using the initial condition approximated in the first phase. Numerical experiments show that our method converges even when the standard deep BSDE method does not.

Cited by

• High-dimensional Bayesian filtering through deep density approximation
• Nonlinear filtering based on density approximation and deep BSDE prediction

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