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Statistical emulation and uncertainty quantification in computer experiments

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Title
Statistical emulation and uncertainty quantification in computer experiments
Name (type = personal)
NamePart (type = family)
He
NamePart (type = given)
Linglin
NamePart (type = date)
1990-
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Linglin He
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author
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Hung
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Ying
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Ying Hung
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Advisory Committee
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chair
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NamePart (type = family)
Xiao
NamePart (type = given)
Han
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Han Xiao
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Advisory Committee
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internal member
Name (type = personal)
NamePart (type = family)
Dasgupta
NamePart (type = given)
Tirthankar
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Tirthankar Dasgupta
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Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Jeong
NamePart (type = given)
Myong K
DisplayForm
Myong K Jeong
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
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NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
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School of Graduate Studies
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school
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Text
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theses
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2019
DateOther (qualifier = exact); (type = degree)
2019-05
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English
Abstract (type = abstract)
Computer experiments refer to the study of real systems using complex mathematical models. They have been widely used as alternatives to physical experiments, especially for studying complex systems in science and engineering. Typically, computer experiments require a great deal of time and computing to conduct and they are nearly deterministic in the sense that a particular input will produce almost the same output if given to the computer experiment on another occasion. Therefore, it is desirable to build an interpolator for computer experiment outputs and use it as an emulator for the actual computer experiment. This thesis mainly focuses on building efficient statistical emulators for computer experiments and providing prediction uncertainty with real-world applications.
Gaussian process (GP) models are widely used in the analysis of computer experiments. However, two issues have not been solved satisfactorily. One is the computational issue that hinders GP from broader application, especially for massive data with high-dimensional inputs. The other is the underestimation of prediction uncertainty in GP modeling. To tackle these problems simultaneously, in Chapter 1 we propose two methods to construct GP predictive distributions based on a new version of bootstrap subsampling. The new subsampling procedure borrows the strength of space-filling designs to provide an efficient subsample and thus reduce the computational complexity. It is shown that this procedure not only alleviates the computational difficulty in GP modeling, but also provides unbiased predictors with better quantifications of uncertainty comparing with existing methods. We illustrate the proposed methods by two complex computer experiments with high-dimensional inputs and tens of thousands of simulation outputs.
Traditional GP models are limited in the computational capability of dealing with massive and complex data. To overcome the computational issue without imposing strong assumptions, a spline-based approach is developed to build emulators for computer experiments to handle big spatial-temporal data in Chapter 2. A direct application of the proposed framework is to model Antarctic ice-sheet contributions to sea level rise. Sea level rise is expected to impact millions of people in coastal communities in the coming centuries. Global, regional, and local sea level rise projections are highly uncertain, partially due to uncertainties in Antarctic ice-sheet (AIS) dynamics, and parameterized simulations are expensive to run. We create an ice-sheet emulator to provide near-continuous distributions of the sea-level equivalent of AIS melt based on two input parameters, which alter the behavior AIS mass loss, under two forcing scenarios: the Last Interglacial and RCP 8.5 forcing. The spline-based emulator with Gaussian Process priors on the spline parameters is flexible enough to capture the nonlinearity of the underlying structure, while computationally feasible at the same time. It achieves a good fit for the physical model and provides prediction uncertainties simultaneously.
Subject (authority = local)
Topic
Computer experiment
Subject (authority = RUETD)
Topic
Statistics and Biostatistics
Subject (authority = LCSH)
Topic
Computer simulation
Subject (authority = LCSH)
Topic
Emulators (Computer programs)
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
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ETD_9773
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application/pdf
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text/xml
Extent
1 online resource (xiii, 72 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-7m6x-6j14
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
He
GivenName
Linglin
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-04-10 23:40:54
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Name
Linglin He
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Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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Type
Embargo
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-05-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2021-05-30
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 30th, 2021.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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