20.500.12556/RUL-169570 Deep learning for conditional McKean-Vlasov jump diffusions The current paper focuses on using deep learning methods to optimize the control of conditional McKean-Vlasov jump diffusions. We begin by exploring the dynamics of multi-particle jump-diffusion and presenting the propagation of chaos. The optimal control problem in the context of conditional McKean-Vlasov jump-diffusion is introduced along with the verification theorem (HJB equation). A linear quadratic conditional mean-field (LQ CMF) is discussed to illustrate these theoretical concepts. Then, we introduce a deep-learning algorithm that combines neural networks for optimization with path signatures for conditional expectation estimation. The algorithm is applied to practical examples, including LQ CMF and interbank systemic risk, and we share the resulting numerical outcomes. McKean-Vlasov jump diffusion signatures common noise deep learning true false true Angleški jezik Angleški jezik Članek v reviji 2025-06-04 12:57:31 2025-06-04 12:57:31 2025-06-06 04:03:08 0000-00-00 00:00:00 2025 0 0 15 str. [article no.] 106100 Vol. 201 Jul. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2025-07-01 519.2:517.997 0167-6911 10.1016/j.sysconle.2025.106100 238288387 1-s2.0-S0167691125000829-main.pdf 1-s2.0-S0167691125000829-main.pdf 1 97298A402F18269DF7DF05B814023FE1 28a190a7fe7b631b3e1a083ab93b5302455ac1351fb6d100184c2e5f1c334b99 d005bc60-4131-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=211050 https://www.sciencedirect.com/science/article/pii/S0167691125000829 1 6507c70c-4131-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=211049 Fakulteta za matematiko in fiziko 0 0 0