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，sisc，jcp等国际知名期刊发表论文多篇。
内容简介
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. in this talk, we will present a comprehensive framework that includes uncertainty modeling, new and existing solution methods, as well as information bottleneck based uncertainty quantification for neural function regression and neural operator learning.
