Stochastic Kriging for random simulation metamodeling with finite sampling

Bo Wang, Junqiang Bai, Hae Chang Gea

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

As a metamodeling method, Kriging has been intensively developed for deterministic design in the past few decades. However, Kriging is not able to deal with the uncertainty of many engineering processes. By incorporating the uncertainty of data, Stochastic Kriging methods has been developed to analyze and predict random simulation results, but the results cannot fit the problem with uncertainty well. In this paper, deterministic Kriging are extended to stochastic space theoretically, where a novel form of Stochastic Kriging that fully considers the intrinsic uncertainty of data and number of replications is proposed on the basis of finite inputs. It formulates a more reasonable optimization problem via a stochastic process, and then derives the spatial correlation models underlying a random simulation. The obtained results are more general than Kriging, which can fit well with many uncertainty-based problems. Three examples will illustrate the method's application through comparison with the existing methods: the novel method shows that the results are much closer to reality.

Original languageEnglish (US)
Title of host publication39th Design Automation Conference
PublisherAmerican Society of Mechanical Engineers
ISBN (Print)9780791855898
DOIs
StatePublished - 2013
EventASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2013 - Portland, OR, United States
Duration: Aug 4 2013Aug 7 2013

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume3 B

Other

OtherASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2013
Country/TerritoryUnited States
CityPortland, OR
Period8/4/138/7/13

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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