Computer experiments: A review

Sigal Levy*, David M. Steinberg

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

79 Scopus citations


In this paper we provide a broad introduction to the topic of computer experiments. We begin by briefly presenting a number of applications with different types of output or different goals. We then review modelling strategies, including the popular Gaussian process approach, as well as variations and modifications. Other strategies that are reviewed are based on polynomial regression, non-parametric regression and smoothing spline ANOVA. The issue of multi-level models, which combine simulators of different resolution in the same experiment, is also addressed. Special attention is given to modelling techniques that are suitable for functional data. To conclude the modelling section, we discuss calibration, validation and verification. We then review design strategies including Latin hypercube designs and space-filling designs and their adaptation to computer experiments. We comment on a number of special issues, such as designs for multi-level simulators, nested factors and determination of experiment size.

Original languageEnglish
Pages (from-to)311-324
Number of pages14
JournalAStA Advances in Statistical Analysis
Issue number4
StatePublished - 2010


  • Deterministic output
  • Functional data
  • Gaussian process
  • Latin hypercube designs
  • Space filling


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