Scientific Machine Learning (SML) is an emerging interdisciplinary field with wide-ranging applications in domains such as public health, climate science, and drug discovery. The primary goal of SML is to develop data-driven surrogate models that can learn spatiotemporal dynamics or predict key system properties, thereby accelerating time-intensive simulations and reducing the need for real-world experiments. To make SML approaches truly reliable for domain experts, Uncertainty Quantification (UQ) plays a critical role in enabling risk assessment and informed decision-making. In this presentation, I will first introduce our recent advancements in UQ for spatiotemporal and multi-fidelity surrogate modeling with Bayesian deep learning, focusing on applications in accelerating computational epidemiology simulations. Following this, I will demonstrate how quantified uncertainties can be leveraged to design sample-efficient algorithms for adaptive experimental design, with a focus on Bayesian active learning and black-box optimization for scientific discovery.

Speaker: Mr. Dongxia WU
Date: 28 February 2025 (Friday)
Time: 9:30am – 10:30am
Zoom: Link
Poster: Click here