Solving BSVIE via Deep Learning
Dnr:
NAISS 2026/3-380
Type:
NAISS Medium
Principal Investigator:
Nacira Agram
Affiliation:
Kungliga Tekniska högskolan
Start Date:
2026-05-29
End Date:
2027-06-01
Primary Classification:
10106: Probability Theory and Statistics (Statistics with medical aspects at 30118 and with social aspects at 50907)
Webpage:
Allocation
- Dardel-GPU at PDC: 300 GPU-h/month
- Dardel at PDC: 10 x 1000 core-h/month
Abstract
This project aims to develop and implement machine learning algorithms for solving BSVIE 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.