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|Title:||UNCERTAINTY QUANTIFICATION IN ENGINEERING OPTIMIZATION APPLICATIONS||Authors:||LI GUILIN||Keywords:||engineering optimization, parameter uncertainty, zero-inflated model, Bayesian optimal design, Shannon information, uncertain objective function||Issue Date:||30-Mar-2017||Citation:||LI GUILIN (2017-03-30). UNCERTAINTY QUANTIFICATION IN ENGINEERING OPTIMIZATION APPLICATIONS. ScholarBank@NUS Repository.||Abstract:||In this dissertation, we propose three novel methodologies for modeling the uncertainties in engineering design problems. The first work proposes a multilevel zero-inflated model to capture the various types of variations in high-quality manufacturing processes. The second work focuses on the development of Bayesian optimal designs for the efficient estimation of the optimum design setting. The developed framework employs a Shannon information utility measure to quantify the reduction in the uncertainty of the optimum setting from an experiment. In the third work, we look into metamodel-based optimization of stochastic computer models where the objective functions are uncertain. We leverage on the flexible and efficient radial basis function metamodel and a novel experimental design approach to model the objective function as a function of both the design factors and the uncertain objective function parameters. These three developed methodologies together contribute to improving the engineering design process and facilitate robust decisions.||URI:||http://scholarbank.nus.edu.sg/handle/10635/136508|
|Appears in Collections:||Ph.D Theses (Open)|
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