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dc.contributor.authorHuang, Jiangengen_US
dc.date.accessioned2019-07-24T08:00:28Z
dc.date.available2019-07-24T08:00:28Z
dc.date.issued2019-07-23
dc.identifier.othervt_gsexam:21821en_US
dc.identifier.urihttp://hdl.handle.net/10919/91935
dc.description.abstractWith remarkable advances in computing power, computer experiments continue to expand the boundaries and drive down the cost of various scientific discoveries. New challenges keep arising from designing, analyzing, modeling, calibrating, optimizing, and predicting in computer experiments. This dissertation consists of six chapters, exploring statistical methodologies in sequential learning, model calibration, and uncertainty quantification for heteroskedastic computer experiments and large-scale computer experiments. For heteroskedastic computer experiments, an optimal lookahead based sequential learning strategy is presented, balancing replication and exploration to facilitate separating signal from input-dependent noise. Motivated by challenges in both large data size and model fidelity arising from ever larger modern computer experiments, highly accurate and computationally efficient divide-and-conquer calibration methods based on on-site experimental design and surrogate modeling for large-scale computer models are developed in this dissertation. The proposed methodology is applied to calibrate a real computer experiment from the gas and oil industry. This on-site surrogate calibration method is further extended to multiple output calibration problems.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectsequential learningen_US
dc.subjectcomputer experimentsen_US
dc.subjectuncertainty quantificationen_US
dc.subjectbig dataen_US
dc.subjecthierarchical modelingen_US
dc.titleSequential learning, large-scale calibration, and uncertainty quantificationen_US
dc.typeDissertationen_US
dc.contributor.departmentStatisticsen_US
dc.description.degreeDoctor of Philosophyen_US
thesis.degree.nameDoctor of Philosophyen_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineStatisticsen_US
dc.contributor.committeechairGramacy, Robert B.en_US
dc.contributor.committeememberHouse, Leanna L.en_US
dc.contributor.committeememberHigdon, Daviden_US
dc.contributor.committeememberFranck, Christopher Thomasen_US
dc.description.abstractgeneralWith remarkable advances in computing power, complex physical systems today can be simulated comparatively cheaply and to high accuracy through computer experiments. Computer experiments continue to expand the boundaries and drive down the cost of various scientific investigations, including biological, business, engineering, industrial, management, health-related, physical, and social sciences. This dissertation consists of six chapters, exploring statistical methodologies in sequential learning, model calibration, and uncertainty quantification for heteroskedastic computer experiments and large-scale computer experiments. For computer experiments with changing signal-to-noise ratio, an optimal lookahead based sequential learning strategy is presented, balancing replication and exploration to facilitate separating signal from complex noise structure. In order to effectively extract key information from massive amount of simulation and make better prediction for the real world, highly accurate and computationally efficient divide-and-conquer calibration methods for large-scale computer models are developed in this dissertation, addressing challenges in both large data size and model fidelity arising from ever larger modern computer experiments. The proposed methodology is applied to calibrate a real computer experiment from the gas and oil industry. This large-scale calibration method is further extended to solve multiple output calibration problems.en


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