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:
    title: BERRU Predictive Modeling
    sub title: Best Estimate Results with Reduced Uncertainties
  name:
    name part: Cacuci, Dan Gabriel.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIV, 451 p. 1 illus. online resource.
  abstract: This book addresses the experimental calibration of best-estimate numerical simulation models. The results of measurements and computations are never exact. Therefore, knowing only the nominal values of experimentally measured or computed quantities is insufficient for applications, particularly since the respective experimental and computed nominal values seldom coincide. In the author's view, the objective of predictive modeling is to extract "best estimate" values for model parameters and predicted results, together with "best estimate" uncertainties for these parameters and results. To achieve this goal, predictive modeling combines imprecisely known experimental and computational data, which calls for reasoning on the basis of incomplete, error-rich, and occasionally discrepant information. The customary methods used for data assimilation combine experimental and computational information by minimizing an a priori, user-chosen, "cost functional" (usually a quadratic functional that represents the weighted errors between measured and computed responses). In contrast to these user-influenced methods, the BERRU (Best Estimate Results with Reduced Uncertainties) Predictive Modeling methodology developed by the author relies on the thermodynamics-based maximum entropy principle to eliminate the need for relying on minimizing user-chosen functionals, thus generalizing the "data adjustment" and/or the "4D-VAR" data assimilation procedures used in the geophysical sciences. The BERRU predictive modeling methodology also provides a "model validation metric" which quantifies the consistency (agreement/disagreement) between measurements and computations. This "model validation metric" (or "consistency indicator") is constructed from parameter covariance matrices, response covariance matrices (measured and computed), and response sensitivities to model parameters. Traditional methods for computing response sensitivities are hampered by the "curse of dimensionality," which m…
  table of contents: Basics of predictive best-estimate model calibration -- Predictive best-estimate model-validation, model-calibration and model-verification concerning open and chaotic systems -- Differences to traditional statostic evaluation methods -- Examples.
  note: by Dan Gabriel Cacuci.
  subject:
    topic: Engineering mathematics
  subject:
    topic: Computer simulation
  subject:
    topic: Mathematical models
  subject:
    topic: Quality control
  subject:
    topic: Reliability
  subject:
    topic: Industrial safety
  subject:
    topic: Engineering Mathematics
  subject:
    topic: Simulation and Modeling
  subject:
    topic: Mathematical Modeling and Industrial Mathematics
  subject:
    topic: Quality Control, Reliability, Safety and Risk
  classification: TA329-348
  classification: 620.00151
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783662583951
  identifier: https://doi.org/10.1007/978-3-662-58395-1
  record info:
    record creation date: 181229
    record change date: 20191024172347.0
    record identifier: 978-3-662-58395-1