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: Advances in High-Order Predictive Modeling
    sub title: Methodologies and Illustrative Problems
  name:
    name part: Cacuci, Dan Gabriel
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Taylor & Francis
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: flu
    date issued: 2025
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xiv, 288 pages).
  abstract: Continuing the author⁰́₉s previous work on modeling, this book presents the most recent advances in high-order predictive modeling. The author begins with the mathematical framework of the 2nd-BERRU-PM methodology, an acronym that designates the ⁰́econd-order best-estimate with reduced uncertainties (2nd-BERRU) predictive modeling (PM).⁰́₊ The 2nd-BERRU-PM methodology is fundamentally anchored in physics-based principles stemming from thermodynamics (maximum entropy principle) and information theory, being formulated in the most inclusive possible phase-space, namely the combined phase-space of computed and measured parameters and responses. The 2nd-BERRU-PM methodology provides second-order output (means and variances) but can incorporate, as input, arbitrarily high-order sensitivities of responses with respect to model parameters, as well as arbitrarily high-order moments of the initial distribution of uncertain model parameters, in order to predict best-estimate mean values for the model responses (i.e., results of interest) and calibrated model parameters, along with reduced predicted variances and covariances for these predicted responses and parameters.
  table of contents: CHAPTER 1: 2nd-BERRU-PM: Second-Order Maximum Entropy Predictive Modeling Methodology for Reducing Uncertainties in Predicted Model Responses and Parameters 1.1. Introduction 1.2. Generic Mathematical Modeling of a Physical System 1.3. Construction of the Minimally Discrepant Maximum Entropy Distribution 1.4. Construction of the Second-Order Minimally Discrepant Maximum Entropy Distribution of Experimentally Measured Responses and Parameters 1.5. 2nd-BERRU-PMD: Second Order MaxEnt Predictive Modeling Methodology with Deterministically Included Computed Responses 1.6. 2nd-BERRU-PMP: Second-Order MaxEnt Predictive Modeling Methodology with Probabilistically Included Computed Responses 1.6.1. Second-Order MaxEnt Probabilistic Representation of the Computational Model 1.6.2. General Case 2nd-BERRU-PMP: Inclusion of Additional External Measurements for Both Responses and Parameters 1.6.3. Practical Case 2nd-BERRU-PMP: Inclusion of Response Measurements 1.7. Inter-Comparison: 2nd-BERRU-PMP vs. 2nd-BERRU-PMD 1.7.1. Inter-Comparison: Best-Estimate Predicted Mean Values for Responses 1.7.2. Inter-Comparison: Best-Estimate Predicted Mean Values for Parameters 1.7.3. Inter-Comparison: Best-Estimate Predicted Response Covariances 1.7.4. Inter-Comparison: Best-Estimate Predicted Parameter Covariances 1.7.5. Inter-Comparison: Best-Estimate Predicted Correlations Between Parameters and Responses 1.8. Review of Principles Underlying the Data Adjustment and Data Assimilation Procedures 1.8.1. Principles Underlying the Data Adjustment Procedure 1.8.2. Principles Underlying the Data Assimilation Procedure 1.9. Discussion and Conclusions CHAPTER 2: Application of the 2nd-BERRU-PM Methodology to the PERP Reactor Physics Benchmark 2.1. Introduction 2.2. Mathematical Modeling of the OECD/NEA Polyethylene-Reflected Plutonium Metal Sphere (PERP) Reactor Physics Benchmark 2.3: Mean and Variance of the PERP Benchmark⁰́₉s Computed Leakage Response 2.3.1. ⁰́₋High precision⁰́₊ parameters; unifor…
  target audience: adult
  subject:
    topic: Prediction theory
  subject:
    topic: Sensitivity theory (Mathematics)
  subject:
    topic: Maximum entropy method
  subject:
    topic: Newton-Raphson method
  subject:
    topic: Uncertainty (Information theory)
  subject:
    topic: Théorie de la prévision
  subject:
    topic: Théorie de la sensibilité (Mathématiques)
  subject:
    topic: Méthode d'entropie maximale
  subject:
    topic: Méthode de Newton
  subject:
    topic: Incertitude (Théorie de l'information)
  subject:
    topic: MATHEMATICS / Reference
  subject:
    topic: MATHEMATICS / Research
  classification: QA279.2 .C33 2025eb
  classification: 519.2/87
  related item:
    title info:
      title: Advances in Applied Mathematics
  identifier: 9781003478119
  identifier: 1003478115
  identifier: 9781040193228
  identifier: 1040193226
  identifier: 9781040193204
  identifier: 104019320X
  identifier: 9781032740560
  identifier: 9781032763538
  identifier: 9781003478119 Taylor & Francis
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: TYFRS
    record creation date: 241114
    record change date: 20250113194653.8
    record identifier: in00005612767
    language of cataloging:
      language term: eng