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https://scholarbank.nus.edu.sg/handle/10635/136508
DC Field | Value | |
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dc.title | UNCERTAINTY QUANTIFICATION IN ENGINEERING OPTIMIZATION APPLICATIONS | |
dc.contributor.author | LI GUILIN | |
dc.date.accessioned | 2017-08-31T18:00:43Z | |
dc.date.available | 2017-08-31T18:00:43Z | |
dc.date.issued | 2017-03-30 | |
dc.identifier.citation | LI GUILIN (2017-03-30). UNCERTAINTY QUANTIFICATION IN ENGINEERING OPTIMIZATION APPLICATIONS. ScholarBank@NUS Repository. | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/136508 | |
dc.description.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. | |
dc.language.iso | en | |
dc.subject | engineering optimization, parameter uncertainty, zero-inflated model, Bayesian optimal design, Shannon information, uncertain objective function | |
dc.type | Thesis | |
dc.contributor.department | INDUSTRIAL SYSTEMS ENGINEERING & MGT | |
dc.contributor.supervisor | NG SZU HUI | |
dc.description.degree | Ph.D | |
dc.description.degreeconferred | DOCTOR OF PHILOSOPHY | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Ph.D Theses (Open) |
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LiGL.pdf | 9.14 MB | Adobe PDF | OPEN | None | View/Download |
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