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bokomslag Advances in High-Order Predictive Modeling
Vetenskap & teknik

Advances in High-Order Predictive Modeling

Dan Gabriel Cacuci

Inbunden

1999:-

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  • 288 sidor
  • 2024
Continuing the authors 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 second-order best-estimate with reduced uncertainties (2nd-BERRU) predictive modeling (PM). 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.
  • Författare: Dan Gabriel Cacuci
  • Format: Inbunden
  • ISBN: 9781032740560
  • Språk: Engelska
  • Antal sidor: 288
  • Utgivningsdatum: 2024-12-11
  • Förlag: Chapman & Hall/CRC