报告时间：2023年12月1日12:30-13:30
报告地点：红瓦楼 726
报告人：郭玲 教授 （上海师范大学 ）
报告摘要：In this talk, we will present a novel framework for uncertainty quantification via information bottleneck (IB-UQ) in scientific machine learning tasks, including deep neural network (DNN) regression and neural operator learning (DeepONet). IB-UQ can provide both mean and variance in the label prediction by explicitly modeling the representation variables. Compared to most DNN regression methods and the deterministic DeepONet, the proposed model can be trained on noisy data and provide accurate predictions with reliable uncertainty estimates on unseen noisy data. The capability of the proposed IB-UQ framework is demonstrated with some numerical examples.
报告人简介：郭玲，上海师范大学数学系教授，博士生导师。主要研究领域为不确定性量化与深度学习。先后主持国家自然科学基金等多项课题，在《SIAM REVIEW》、《SIAM J. Sci. Comp.》等国际权威杂志发表论文多篇。
报告邀请人：黄学海 教授