@inbook{814e045bca6a486e9e501447f57b3b05,
title = "Ch. 7. A review of design and modeling in computer experiments",
abstract = "In this chapter, we provide a review of statistical methods that are useful in conducting computer experiments. Our focus is primarily on the task of metamodeling, which is driven by the goal of optimizing a complex system via a deterministic simulation model. However, we also mention the case of a stochastic simulation, and examples of both cases are discussed. The organization of our review separates the two primary tasks for metamodeling: (1) select an experimental design; (2) fit a statistical model. We provide an overview of the general strategy and discuss applications in electrical engineering, chemical engineering, mechanical engineering, and dynamic programming. Then, we dedicate a section to statistical modeling methods followed by a section on experimental designs. Designs are discussed in two paradigms, model-dependent and model-independent, to emphasize their different objectives. Both classical and modern methods are discussed.",
author = "Chen, {Victoria C.P.} and Tsui, {Kwok Leung} and Barton, {Russell R.} and Allen, {Janet K.}",
note = "Funding Information: In the design of complex systems, computer experiments are frequently the only practical approach to obtaining a solution. Typically, a simulation model of system performance is constructed based on knowledge of how the system operates. Performance measures are specified to be incorporated into optimization criteria and constraints, and the design parameters which affect performance are identified. The design solution method depends on the computational demands of the simulation model. In the *Partially supported by NSF Grant #DMI 0100123 and a Technology for Sustainable Environment (TSE) grant under the U.S. EPA's Science to Achieve Results (STAR) program (Contract #1{-82820701-0). ~Partially supported by NSF Grants #DMI 0100123 and #DMI 0100123, and The Logistics Institute-Asia Pacific in Singapore. Spartially supported by NSF Grants # DMI 970040 and #DMI 0084918. §Parfially supported by NSF Grant #DMI 010012.",
year = "2003",
doi = "10.1016/S0169-7161(03)22009-5",
language = "English (US)",
isbn = "9780444506146",
series = "Handbook of Statistics",
pages = "231--261",
editor = "R. Khattree and C.R. Rao",
booktitle = "Statistics in Industry",
}