受公司国际项目管理中心资助，应002资讯网郭玉坤副教授的邀请，美国密歇根理工大学孙继广教授将于近日在公司做两场线上学术讲座，报告内容为波动方程反问题的贝叶斯方法，以下是报告信息，欢迎感兴趣的师生参加。

报告一

时间：2020年7月20日上午10:00-11:30

平台：腾讯会议，会议ID：317 605 028，可点击链接加入会议：https://meeting.tencent.com/s/VXbJpCvHZRnB

题目：Bayesian Inversion and Inverse Scattering Problems with Non-unique Solutions

摘要：Inverse scattering problems arise from many important applications. In this talk, we propose a new method combining the non-iterative method and the Bayesian approach. The problem is formulated as a statistical model using the Baye's formula. The well-posedness is proved in the sense of the Hellinger metric. The direction method is used to obtain information to construct priors, which is critical to the convergence of the MCMC algorithm. In particular, we study some inverse scattering problems with non-unique solutions and demonstrate the performance of the proposed method.

报告二

时间：2020年7月21日上午10:00-11:30

平台：腾讯会议，会议ID：783 657 649，可点击链接加入会议：https://meeting.tencent.com/s/QHgdCwPA0KYh

题目：Quality-Bayesian Approach to Inverse Acoustic Source Problems with Partial Data

摘要：A quality-Bayesian approach, combining the direct sampling method and the Bayesian inversion, is proposed to reconstruct the locations and intensities of the unknown acoustic sources using partial data. First, we extend the direct sampling method by constructing a new indicator function to obtain the approximate locations of the sources. The behavior of the indicator is analyzed. Second, the inverse problem is formulated as a statistical inference problem using the Bayes' formula. The well-posedness of the posterior distribution is proved. The source locations obtained in the first step are coded in the priors. Then an Metropolis-Hastings MCMC algorithm is used to explore the posterior density. Both steps use the same physical model and measured data. Numerical experiments show that the proposed method is effective even with partial data.

报告人简介：孙继广1996年毕业于清华大学应用数学系。在University of Delaware师从于麦克斯韦有限元方法的专家Peter Monk，获得计算机科学硕士和应用数学博士。之后在University of North Carolina, Charlotte从事博士后研究。现在任Michigan Technological University终身正教授（tenured full professor）。孙继广的研究方向包括特征值问题有限元方法和逆散射理论。近年来从事传输特征值计算的研究。从2004年至今在Inverse Problems, SIAM Journal of Numerical Analysis, Numerische Mathematik, SIAM Journal on Imaging Sciences, Journal of Computational Physics等杂志上发表60余篇文章。与中科院计算所的周爱辉教授合作的专著Finite Element Methods for Eigenvalue Problems由Taylor & Francis公司于2016年出版。