Seminar| Institute of Mathematical Sciences
Time: Friday, January 16th, 2026,15:00-16:00
Location: IMS, RS408
Speaker: Ling Guo, Shanghai Normal University
Abstract: Physics-informed deep learning approaches have been developed to solve forward and inverse stochastic differential equation (SDE) problems with high-dimensional stochastic space. However, the existing deep learning models have difficulties solving SDEs in the high-dimensional spatial space. In this talk, we will present a scalable physics-informed deep generative model (sPI-GeM), which is capable of solving SDE problems with both high-dimensional stochastic and spatial space. A series of numerical experiments, including approximation of Gaussian and non-Gaussian stochastic processes, forward and inverse SDE problems, are performed to demonstrate the accuracy of the proposed model.