This book blends a modern statistical approach with extensive engineering applications and clearly delineates the steps for successfully modeling a problem and analyzing it to find the solution. It introduces basic concepts, then fully examines computer experiment design. The authors present the popular space-filling designs - like Latin hypercube sampling - including their properties, construction, and generating algorithms. Discussion then moves to the modeling of data from computer experiments. Here the authors present various modeling techniques and discuss model interpretation, including sensitivity analysis. Numerous examples clarify the techniques and their implementation.