Seminar: Deep learning methods for data-driven uncertainty quantification

Seminar| Institute of Mathematical Sciences

Abstract: In this talk, we will present some recent developments on using Physics-informed neural networks (PINNs) to quantify uncertainty propagation in a unified framework forward, inverse and mixed stochastic problems based on scattered measurements. We will also present generative models for data-driven uncertainty quantification, including physics-informed generative adversarial networks and Normalizing field flows.