Comparative studies of metamodelling techniques under multiple modelling criteria

R. Jin, W. Chen, Timothy William Simpson

Research output: Contribution to journalReview articlepeer-review

1401 Scopus citations


Despite advances in computer capacity, the enormous computational cost of running complex engineering simulations makes it impractical to rely exclusively on simulation for the purpose of design optimization. To cut down the cost, surrogate models, also known as metamodels, are constructed from and then used in place of the actual simulation models. In this paper, we systematically compare four popular metamodelling techniques - polynomial regression, multivariate adaptive regression splines, radial basis functions, and kriging - based on multiple performance criteria using fourteen test problems representing different classes of problems. Our objective in this study is to investigate the advantages and disadvantages of these four metamodelling techniques using multiple criteria and multiple test problems rather than a single measure of merit and a single test problem.

Original languageEnglish (US)
Pages (from-to)1-13
Number of pages13
JournalStructural and Multidisciplinary Optimization
Issue number1
StatePublished - Dec 1 2001

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Control and Optimization


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