readers with Masters-level training in Statistics, the book is written in sufficient detail to be useful for practitioners and researchers. New to this revised and expanded edition: . An expanded presentation of basic material on computer experiments and Gaussian processes with additional simulations and examples . A new comparison of plug-in prediction methodologies for real-valued simulator output . An enlarged discussion of space-filling designs including Latin Hypercube designs (LHDs), near-orthogonal designs, and nonrectangular regions . A chapter length description of process-based designs for optimization, to improve good overall fit, quantile estimation, and Pareto optimization . A new chapter describing graphical and numerical sensitivity analysis tools . Substantial new material on calibration-based prediction and inference for calibration parameters . Lists of software that can be used to fit models discussed in the book to aid practitioners