Many complex engineering systems are often described by large-scale computer models. Quantifying uncertainty in such systems often requires a large number of simulations of such intensive computer models, which renders the total computational cost prohibitive. To this end, one possible solution is to construct some computationally efficient surrogate models of the systems and use them in the simulations. In this talk, we will discuss a popular surrogate model - the Gaussian Process regression, and the application of it in bot forward and inverse Uncertainty Quantification problems.
Jinglai Li is a Reader in the Department of Mathematical Sciences at the University of Liverpool. He received the PhD degree in Mathematics from SUNY Buffalo and did postdoctoral research at Northwestern University and MIT respectively. Prior to joining the University of Liverpool, he was an Associate Professor at Shanghai Jiao Tong University. His research interests are in scientific computing, computational statistics, uncertainty quantification, data science and their applications in various scientific and engineering problems.