Numerical simulation under parametric uncertainty presents significant challenges arising from the complexity of the modeled system, the inherent nature of uncertainties, and the associated computational cost. Effectively addressing these challenges requires leveraging advanced computational methods, sophisticated statistical techniques, and specialized domain expertise. In this talk, we will explore two recent advancements: mesh adaptation techniques and acceleration strategies achieved through deep learning-based hybridization.