Name: Advances in High-Order Predictive Modeling: Methodologies and Illustrative Problems (Advances in Applied Mathematics) (en Inglés)
SKU: 63438478
Price: 116.63 USD
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Author: Dan Gabriel Cacuci

portada Advances in High-Order Predictive Modeling: Methodologies and Illustrative Problems (Advances in Applied Mathematics) (en Inglés)

Advances in High-Order Predictive Modeling: Methodologies and Illustrative Problems (Advances in Applied Mathematics) (en Inglés)

Formato

Libro Físico

Editorial

Crc Pr Inc

Autor

Dan Gabriel Cacuci

Idioma

Inglés

N° páginas

288

Encuadernación

Tapa Dura

ISBN13

9781032740560

N° edición

Advances in High-Order Predictive Modeling: Methodologies and Illustrative Problems (Advances in Applied Mathematics) (en Inglés)

Dan Gabriel Cacuci (Autor) · Crc Pr Inc · Tapa Dura

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Reseña del libro "Advances in High-Order Predictive Modeling: Methodologies and Illustrative Problems (Advances in Applied Mathematics) (en Inglés)"

Continuing the author's 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.