View in EDS

A sequential approach for stochastic computer model calibration and prediction

Saved in:
Bibliographic Details
Title: A sequential approach for stochastic computer model calibration and prediction
Authors: Yuan, Jun1 jyuan@nus.edu.sg, Ng, Szu Hui isensh@nus.edu.sg
Source: Reliability Engineering & System Safety. Mar2013, Vol. 111, p273-286. 14p.
Subjects: Sequential approach (Teaching method), Stochastic analysis, Calibration, Prediction models, Computer simulation, Computational complexity, Decision making
Abstract: Abstract: Computer models are widely used to simulate complex and costly real processes and systems. When the computer model is used to assess and certify the real system for decision making, it is often important to calibrate the computer model so as to improve the model’s predictive accuracy. A sequential approach is proposed in this paper for stochastic computer model calibration and prediction. More precisely, we propose a surrogate based Bayesian approach for stochastic computer model calibration which accounts for various uncertainties including the calibration parameter uncertainty in the follow up prediction and computer model analysis. We derive the posterior distribution of the calibration parameter and the predictive distributions for both the real process and the computer model which quantify the calibration and prediction uncertainty and provide the analytical calibration and prediction results. We also derive the predictive distribution of the discrepancy term between the real process and the computer model that can be used to validate the computer model. Furthermore, in order to efficiently use limited data resources to obtain a better calibration and prediction performance, we propose a two-stage sequential approach which can effectively allocate the limited resources. The accuracy and efficiency of the proposed approach are illustrated by the numerical examples. [Copyright &y& Elsevier]
Copyright of Reliability Engineering & System Safety is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 84650734
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A sequential approach for stochastic computer model calibration and prediction
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Yuan%2C+Jun%22">Yuan, Jun</searchLink><relatesTo>1</relatesTo><i> jyuan@nus.edu.sg</i><br /><searchLink fieldCode="AR" term="%22Ng%2C+Szu+Hui%22">Ng, Szu Hui</searchLink><i> isensh@nus.edu.sg</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Reliability+Engineering+%26+System+Safety%22">Reliability Engineering & System Safety</searchLink>. Mar2013, Vol. 111, p273-286. 14p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Sequential+approach+%28Teaching+method%29%22">Sequential approach (Teaching method)</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+analysis%22">Stochastic analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Calibration%22">Calibration</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+complexity%22">Computational complexity</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Abstract: Computer models are widely used to simulate complex and costly real processes and systems. When the computer model is used to assess and certify the real system for decision making, it is often important to calibrate the computer model so as to improve the model’s predictive accuracy. A sequential approach is proposed in this paper for stochastic computer model calibration and prediction. More precisely, we propose a surrogate based Bayesian approach for stochastic computer model calibration which accounts for various uncertainties including the calibration parameter uncertainty in the follow up prediction and computer model analysis. We derive the posterior distribution of the calibration parameter and the predictive distributions for both the real process and the computer model which quantify the calibration and prediction uncertainty and provide the analytical calibration and prediction results. We also derive the predictive distribution of the discrepancy term between the real process and the computer model that can be used to validate the computer model. Furthermore, in order to efficiently use limited data resources to obtain a better calibration and prediction performance, we propose a two-stage sequential approach which can effectively allocate the limited resources. The accuracy and efficiency of the proposed approach are illustrated by the numerical examples. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Reliability Engineering & System Safety is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=84650734
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.ress.2012.11.004
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 273
    Subjects:
      – SubjectFull: Sequential approach (Teaching method)
        Type: general
      – SubjectFull: Stochastic analysis
        Type: general
      – SubjectFull: Calibration
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Computer simulation
        Type: general
      – SubjectFull: Computational complexity
        Type: general
      – SubjectFull: Decision making
        Type: general
    Titles:
      – TitleFull: A sequential approach for stochastic computer model calibration and prediction
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Yuan, Jun
      – PersonEntity:
          Name:
            NameFull: Ng, Szu Hui
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 03
              Text: Mar2013
              Type: published
              Y: 2013
          Identifiers:
            – Type: issn-print
              Value: 09518320
          Numbering:
            – Type: volume
              Value: 111
          Titles:
            – TitleFull: Reliability Engineering & System Safety
              Type: main
ResultId 1