Advances in High-Order Predictive Modeling Methodologies and Illustrative Problems

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...

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Bibliographic Details
Main Author: Cacuci, Dan Gabriel (Author)
Corporate Author: Taylor & Francis
Format: eBook
Language:English
Published: Boca Raton, FL : CRC Press, [2025]
Series:Advances in Applied Mathematics
Subjects:
Online Access:Connect to the full text of this electronic book
Description
Summary: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.
Physical Description:1 online resource (xiv, 288 pages).
ISBN:9781003478119
1003478115
9781040193228
1040193226
9781040193204
104019320X