Speaker: Tao Zhou, Chinese Academy of Sciences

Time: 16:00-17:00 p.m., April 23, 2024, GMT+8

Venue: Room 77201, Jingchunyuan 78, BICMR, PKU 

Abstract: 

We present a new framework for uncertainty quantification via information bottleneck (IB-UQ) in scientific machine learning tasks, including deep neural regression and neural operator learning. IB-UQ can provide uncertainty estimates in the label prediction by explicitly modeling the representation variables. Moreover, IB-UQ can be trained with noisy data and provide accurate predictions with reliable uncertainty estimates on unseen data. We also present the physics-informed version of IB-UQ for PDE-related problems.The capability of the proposed IB-UQ framework is demonstrated with numerical examples.

Source: Beijing International Center for Mathematical Research, PKU