A design-driven validation approach using Bayesian prediction models

Wei Chen*, Ying Xiong, Kwok Leung Tsui, Shuchun Wang

*Corresponding author for this work

Research output: Contribution to journalArticle

100 Scopus citations

Abstract

In most of the existing work, model validation is viewed as verifying the model accuracy, measured by the agreement between computational and experimental results. Due to the lack of resource, accuracy can only be assessed at very limited test points. However from the design perspective, a good model should be considered the one that can provide the discrimination (with good resolution) between competing design candidates under uncertainty. In this work, a design-driven validation approach is presented. By combining data from both physical experiments and the computer model, a Bayesian approach is employed to develop a prediction model as the replacement of the original computer model for the purpose of design. Based on the uncertainly quantification with the Bayesian prediction and, subsequently, that of a design objective, some decision validation metrics are further developed to assess the confidence of using the Bayesian prediction model in making a specific design choice. We demonstrate that the Bayesian approach provides a flexible framework for drawing inferences for predictions in the intended, but maybe untested, design domain. The applicability of the proposed decision validation metrics is examined for designs with either a discrete or continuous set of design alternatives. The approach is demonstrated through an illustrative example of a robust engine piston design.

Original languageEnglish (US)
Article number021101
JournalJournal of Mechanical Design, Transactions Of the ASME
Volume130
Issue number2
DOIs
StatePublished - Feb 1 2008

Keywords

  • Bayesian prediction
  • Design confidence
  • Engineering design
  • Model validation
  • Uncertainty quantification
  • Validation metrics

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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