The theory of inverse problems has a wide variety of applications because any mathematical model needs to be calibrated before it can be used, and such a calibration is a typical inverse problem. The regularization theory, in its turn, is the algorithmic part of the theory of inverse problems. It provides and analyzes the methods for dealing with ill-posedness that is one of the main issues for inverse problems. The aim of this talk is to provide a brief introduction of inverse problems in the context of the regularization theory under the framework of linear and nonlinear inverse problems.
Shuai Lu received the B.S. and M.S. degrees from Fudan University, Shanghai, China, in 2001, 2004, and the Ph.D. degree (with distinction) from Johannes Kepler University Linz, Austria, in 2007. From 2007 to 2010, he was a postdoctoral fellow in Austrian Academy of Sciences. In 2010, he joined the School of Mathematical Sciences, Fudan University, where he became a full professor in 2015.
He has authored or coauthored over 45 papers in peer-reviewed journals and one monograph. His current research interests include inverse problems, from both deterministic and stochastic viewpoints. In particular, he is currently working on inverse boundary value problems and filter-based methods arising in data assimilation.