C. Devon Lin (Queen's University)

Some development on dynamic computer experiments

Computer experiments refer to the study of real systems using complex simulation models. They have been widely used as efficient, economical alternatives to physical experiments. Computer experiments with time series outputs are called dynamic computer experiments. In this talk, we consider two problems of such experiments: emulation of large-scale dynamic computer experiments and inverse problem. For the first problem, we proposed a computationally efficient modelling approach which sequentially finds a set of local design points based on a new criterion specifically designed for emulating dynamic computer simulators. Singular value decomposition based Gaussian process models are built with the sequentially chosen local data. To update the models efficiently, an empirical Bayesian approach is introduced. The second problem aims to extract an optimal input of dynamic computer simulator whose response matches a field observation as closely as possible. A sequential design approach is employed and a novel expected improvement criterion is proposed. A real application is discussed to support the efficiency of the proposed approaches. This is joint work with Ru Zhang at Queen’s University and Pritam Ranjan at Indian Institute of Management Indore.