报告题目：Deterministic-Statistical Approach for Inverse problems with Partial Data

报告时间：2022年11月14日（周一）上午9:00-10:00

腾讯会议：418-975-887 

报告摘要：We propose a deterministic-statistical approach for inverse problems with partial data. Certain deterministic method is first used to obtain useful (qualitative) information for the unknowns. Then the inverse problem is recast as a statistical inference problem and the Bayesian inversion is employed to obtain more (quantitative) information of the unknowns. Several examples are presented for demonstration. Furthermore, we introduce new statistical estimators to characterize the non-unique solutions of several inverse problems.

报告人简介： 孙继广, 1996年毕业于清华大学应用数学系。2005年在University of Delaware获得计算机科学硕士和应用数学博士。现任Michigan Technological University教授。孙继广的研究方向包括有限元方法，逆散射理论，线性与非线性特征值问题，地球物理电磁场方法。2004年至今在Inverse Problems, SIAM Journal of Numerical Analysis, Numerische Mathematik, Mathematics of Computation, SIAM Journal on Scientific Computing, SIAM Journal on Imaging Sciences, Journal of Computational Physics等杂志上发表70余篇文章，合作专著Finite Element Methods for Eigenvalue Problems，Taylor & Francis，2016。