Active Learning of Computer Experiment with both Quantitative and Qualitative Inputs

Computer experiments refer to the study of real systems using complex simulation models. They have been widely used as alternatives to physical experiments. Design and analysis of computer experiments have attracted great attention in past three decades. The bulk of the work, however, often focus on experiments with only quantitative inputs. In recent years, research on design and analysis for computer experiments have gain momentum. Statistical methodology for design, modeling and inference of such experiments have been developed. In this chapter, we review some of those key developments, and propose active learning approaches for modeling, optimization, contour estimation of computer experiments with both types of inputs. Numerical studies are conducted to evaluate the performance of the proposed methods in comparison with other existing methods.