The Springer Series in Statistics is a collection of books on statistical methods and applications, edited by several prominent statisticians. The series includes a variety of titles covering topics such as statistical models based on counting processes, robust diagnostic regression analysis, Bayesian nonparametrics, and more. These books are designed to provide comprehensive coverage of statistical theory and practice for a broad audience. The book "Design & Analysis of Computer Experiments" by Thomas J. Santner, Brian J. Williams, and William I. Notz is part of this series. It addresses the challenges of modeling and analyzing complex physical processes using computer simulations. As computational power increases, computer experiments have become a valuable tool for studying processes that are difficult or impossible to study through traditional experimental methods. The book provides a comprehensive overview of the methodology for designing, modeling, and analyzing computer experiments, making these techniques accessible to a wider audience. The authors aim to make the mathematical content understandable to readers with a master's level background in statistics. They include detailed explanations of complex topics such as Gaussian process models and provide references for further study. The book also includes practical software tools for fitting the models discussed, along with examples and instructions for their use. The book covers various aspects of computer experiments, including prediction methods, space-filling designs, and sensitivity analysis. It discusses different design strategies, such as those based on random sampling and Latin hypercube sampling, and explores various criteria for selecting experimental designs. The authors also address issues related to model validation and the interpretation of results from computer experiments. The book is intended for practitioners and researchers in statistics, computer science, and related fields who are interested in the design and analysis of computer experiments. It serves as a valuable resource for those seeking to understand and apply these methods in their work.The Springer Series in Statistics is a collection of books on statistical methods and applications, edited by several prominent statisticians. The series includes a variety of titles covering topics such as statistical models based on counting processes, robust diagnostic regression analysis, Bayesian nonparametrics, and more. These books are designed to provide comprehensive coverage of statistical theory and practice for a broad audience. The book "Design & Analysis of Computer Experiments" by Thomas J. Santner, Brian J. Williams, and William I. Notz is part of this series. It addresses the challenges of modeling and analyzing complex physical processes using computer simulations. As computational power increases, computer experiments have become a valuable tool for studying processes that are difficult or impossible to study through traditional experimental methods. The book provides a comprehensive overview of the methodology for designing, modeling, and analyzing computer experiments, making these techniques accessible to a wider audience. The authors aim to make the mathematical content understandable to readers with a master's level background in statistics. They include detailed explanations of complex topics such as Gaussian process models and provide references for further study. The book also includes practical software tools for fitting the models discussed, along with examples and instructions for their use. The book covers various aspects of computer experiments, including prediction methods, space-filling designs, and sensitivity analysis. It discusses different design strategies, such as those based on random sampling and Latin hypercube sampling, and explores various criteria for selecting experimental designs. The authors also address issues related to model validation and the interpretation of results from computer experiments. The book is intended for practitioners and researchers in statistics, computer science, and related fields who are interested in the design and analysis of computer experiments. It serves as a valuable resource for those seeking to understand and apply these methods in their work.