Solving Backward Stochastic Differential Equations via Neural Network

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Dnr:

NAISS 2025/22-794

Type:

NAISS Small Compute

Principal Investigator:

Giulia Pucci

Affiliation:

Kungliga Tekniska högskolan

Start Date:

2025-05-22

End Date:

2026-06-01

Primary Classification:

10106: Probability Theory and Statistics (Statistics with medical aspects at 30118 and with social aspects at 50907)

Webpage:

Allocation

Dardel at PDC: 10 x 1000 core-h/month

Abstract

This project aims to develop and implement machine learning algorithms for solving backward stochastic differential equations (BSDEs) with jumps via feedforward neural networks. The primary applications are in contract pricing and decision-making in energy markets, as well as in the planning and deployment of renewable energy infrastructure under uncertainty and market shocks.