arXiv Machine Learning By Pere Diaz-Lozano, Giulia Di Nunno

Deep Operator BSDE: a Numerical Scheme to Approximate Solution Operators

Read the original on arXiv Machine Learning →

arXiv:2412. 03405v3 Announce Type: replace-cross Abstract: Motivated by dynamic risk measures and conditional $g$-expectations, in this work we propose a numerical method to approximate the solution operator given by a Backward Stochastic Differential Equation (BSDE).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jun 24

Deep numerical schemes for systems of Ergodic BSDEs with applications to regime-switching forward utilities

arXiv:2606. 24271v1 Announce Type: cross Abstract: In this paper, we introduce two neural-network-based numerical schemes for solving systems of coupled ergodic Backward Stochastic Differential Equations (eBSDEs), motivated by the approximation of optimal strategies within the framework of forward utilities in a regime-switching stochastic factor model.

By Guillaume Broux-Quemerais (LMM), Sarah Kaakai (LAGA), Anis Matoussi (LMM), Wissal Sabbagh (LMM)
Hugging Face Trending Papers
Jun 23

Deep numerical schemes for systems of Ergodic BSDEs with applications to regime-switching forward utilities

In this paper, we introduce two neural-network-based numerical schemes for solving systems of coupled ergodic Backward Stochastic Differential Equations (eBSDEs), motivated by the approximation of optimal strategies within the framework of forward utilities in a regime-switching stochastic factor model. Our approach builds on the representation of such models through systems of eBSDEs introduced in [HLT20].