Speaker: Dr. Sizhou WU 吴思洲 博士

Time: 15:45-16:30, 28 May 2026 (Tue.)

Venue: T2-102

Abstract

In this talk, we introduce two modern numerical algorithms to approximate semi-linear parabolic partial integro-differential equations (PIDEs). The first algorithm is the so-called multilevel Picard (MLP) approximation based on Monte Carlo simulation, and the other one is the random deep-splitting algorithm using random neural networks. For both algorithms, we provide full error analysis and complexity analysis. Furthermore, we present a numerical example in which both algorithms are applied to price high-dimensional financial derivatives in a jump-diffusion model. This is joint work with Ariel Neufeld (NTU) and Philipp Schmocker (ETH).

About the Speaker

Dr. Sizhou Wu is a lecturer in school of mathematics at Shanghai University of Finance and Economics. He obtained his PhD degree (in probability & stochastics) from University of Edinburgh, and he worked as a postdoc at Nanyang technological university. His research interest focuses on analysis and numerics of (stochastic) PDEs and their applications in mathematical finance. His research has led to several papers which have been published on well-known probability and applied mathematics journals such as Stochastic processes and their applications, Stochastics & PDEs, Potential Analysis, Journal of Complexity, etc.