Advances in High-Order Predictive Modeling: Methodologies and Illustrative Problems

By Dan Gabriel Cacuci

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.

• Format: Hardcover
• Language: English
• ISBN: 1032740566
• ISBN13: 9781032740560
• Release Date: December 2024
• Publisher: CRC Press
• Length: 288 Pages
• Weight: 1.33 lbs.
• Dimensions: 0.7" x 6.1" x 9.2"