Parameter identifications for differential equations represent a wide class of inverse problems. Conventionally this class of inverse problems are solved via an optimization approach, based on Tikhonov regularization, which is then discretized for practical implementation for practical numerical implementation. One important issue from the perspective of numerical analysis is to perform a full error analysis of the overall approach.  In this talk, we present our recent efforts to derive convergence rates for a discrete scheme for recovering a spatially dependent diffusion coefficient in an elliptic or parabolic type problem, by suitably exploiting relevant stability results.

B. Jin received a PhD in Mathematics from the Chinese University of Hong Kong, Hong Kong in 2008. Previously, he was Lecturer and Reader, and Professor at Department of Computer Science, University College London (2014-2022), an assistant professor of Mathematics at the University of California, Riverside (2013–2014), a visiting assistant professor at Texas A&M University (2010–2013), an Alexandre von Humboldt Postdoctoral Researcher at University of Bremen (2009–2010). Currently he is chair Professor of Mathematics at the Chinese University of Hong Kong. His main research interests include inverse problems, numerical analysis and machine learning.