Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/136508
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
Source: 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)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
LiGL.pdf9.14 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

30
checked on Jan 22, 2018

Download(s)

20
checked on Jan 22, 2018

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.