Jan Rems: Deep Learning for Conditional McKean-Vlasov Jump Diffusions

V četrtek, 9. 5. 2024, ob 14:15 bo v predavalnici 3.06 v okviru seminarja VeSFiM potekalo predavanje Jana Remsa z naslovom Deep Learning for Conditional McKean-Vlasov Jump Diffusions.

Povzetek: This talk 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). Practical examples are 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, and we share the resulting numerical outcomes. Based on joint work with Nacira Agram (KTH).

Predavanje bo potekalo v živo, bo pa omogočen tudi prenos prek interneta. Povezava na videokonferenčni sistem ZOOM: https://uni-lj-si.zoom.us/j/95492285750 ID: 954 9228 5750

Vljudno vabljeni!