报告人：郭玲（上海师范大学）

时间：2023年09月23日 10:30-

地点：理科楼LA108

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.

简介：郭玲，上海师范大学数学系教授，博士生导师，中国工业与应用数学学会（CSIAM）不确定性量化专委会副秘书长。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。

邀请人：邱越

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