*主持人：郑海标 副教授

*讲座内容简介：

In this talk, I will introduce our recent work on low-rank methods for Bayesian inverse problems. For linear problems with Gaussian noise and Gaussian prior, the posterior is also Gaussian and characterized by the posterior mean and covariance. We propose a low rank Arnoldi method to approximate the large dense posterior covariance matrix by making use of tensor computations. For nonlinear systems, the posterior is not Gaussian anymore,however, can often be approximated by a Gaussian distribution using the ensemble Kalman filter (EnKF) or the extended Kalman filter (ExKF). We propose a randomized low-rank method to reduce the computational complexity of the EnKF. We use numerical experiments to show the efficiency of our low-rank methods.

*主讲人简介：

邱越于2015 年12 月获荷兰代尔夫特理工大学应用数学博士学位，此前分别于2011 年和2009 年获得东北大学硕士学位和学士学位。邱越博士在德国马克斯普朗克学会复杂动力系统研究所担任助理研究员至2019 年。之后，他全职加入上海科技大学信息学院担任助理教授、研究员、博导。主要研究领域为不确定性量化、科学计算、数据科学、数据同化等。论文发表在包括SIAM/ASAJournal on Uncertainty Quantification 、Numerical Linear Algebra with Applications 等国际知名期刊。作为负责人获国家自然科学基金（青年基金）资助并入选上海市海外高层次人才引进计划（第十一批）。