rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    non sort: The
    title: method of manufactured universes for testing uncertainty quantification methods
  name:
    name part: Stripling, Hayes Franklin.
    role:
      role term: creator
  name:
    name part: Adams, Marvin L.
    role:
      role term: thesis advisor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2011
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: The Method of Manufactured Universes is presented as a validation framework for uncertainty quantification (UQ) methodologies and as a tool for exploring the effects of statistical and modeling assumptions embedded in these methods. The framework calls for a manufactured reality from which "experimental" data are created (possi- bly with experimental error), an imperfect model (with uncertain inputs) from which simulation results are created (possibly with numerical error), the application of a system for quantifying uncertainties in model predictions, and an assessment of how accurately those uncertainties are quantified. The application presented for this re- search manufactures a particle-transport "universe," models it using diffusion theory with uncertain material parameters, and applies both Gaussian process and Bayesian MARS algorithms to make quantitative predictions about new "experiments" within the manufactured reality. To test further the responses of these UQ methods, we conduct exercises with "experimental" replicates, "measurement" error, and choices of physical inputs that reduce the accuracy of the diffusion model's approximation of our manufactured laws. Our first application of MMU was rich in areas for exploration and highly informative. In the case of the Gaussian process code, we found that the fundamental statistical formulation was not appropriate for our functional data, but that the code allows a knowledgable user to vary parameters within this formulation to tailor its behavior for a specific problem. The Bayesian MARS formulation was a more natural emulator given our manufactured laws, and we used the MMU framework to develop further a calibration method and to characterize the diffusion model discrepancy. Overall, we conclude that an MMU exercise with a properly designed universe (that is, one that is an adequate representation of some real-world problem) will provide the modeler with an added understanding of the interaction between a g…
  note: by Hayes Franklin Stripling.
  note: "Major Subject: Nuclear Engineering"
  note: Title from author supplied metadata (automated record created 2011-02-28 11:02:00).
  note: Electronic resource.
  note: Master of Science Texas A&M University 2010 http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8986
  note: Includes bibliographical references.
  note: Text (Thesis)
  subject:
    topic: Major nuclear engineering
  subject:
    topic: Validation
  subject:
    topic: Uncertainty Quantification
  subject:
    topic: Calibration
  subject:
    topic: Bayesian Inversion
  identifier: http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8986
  record info:
    record content source: TXA
    record creation date: 110307
    record change date: 20151202115632.0
    record identifier: in00002673297