報告人簡介
郭玲，上海師范大學數(shù)學系教授，博士生導師。主要研究領(lǐng)域為不確定性量化與深度學習。先后主持國家自然科學基金等多項課題，在SIAM Review，SISC，JCP等國際知名期刊發(fā)表論文多篇。
內(nèi)容簡介
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.
