报告人：郭玲教授

报告题目：Uncertainty Quantification in Scientific Machine Learning

报告摘要：

（1）Neural networks (NNs) are currently changing the computational paradigm on how to combine data with mathematical laws in physics and engineering in a profound way, tackling challenging inverse and ill-posed problems not solvable with traditional methods. However, quantifying errors and uncertainties in NN-based inference is more complicated than in traditional methods. Although there are some recent works on uncertainty quantification (UQ) in NNs, there is no systematic investigation of suitable methods towards quantifying the total uncertainty effectively and efficiently even for function approximation, and there is even less work on solving partial differential equations and learning operator mappings between infinite-dimensional function spaces using NNs. In this talk, we will present a comprehensive framework that includes uncertainty modeling, new and existing solution methods, as well as evaluation metrics and post-hoc improvement approaches.

（2）To demonstrate the applicability and reliability of our framework, we will also present an extensive comparative study in which various methods are tested on prototype problems, including problems with mixed input-output data, and stochastic problems in high dimensions.

报告时间：2022年10月29日上午11:00-14:00

报告形式：腾讯会议；会议号：101 239 656

获取会议密码请发邮件至：mathssz@hit.edu.cn

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 Journal on Scientific Computing，Journal of Computational Physics等高水平杂志发表论文多篇。荣获上海市育才奖和上海市教育系统三八红旗手称号。