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

Synopsis

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.

Copyright:

2025

Book Details

Book Quality:

Publisher Quality

ISBN-13:

9781040193228

Related ISBNs:

9781040193204, 9781003478119, 9781032740560

Publisher:

CRC Press

Date of Addition:

12/11/24

Copyrighted By:

Dan Gabriel Cacuci, Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have at

Adult content:

No

Language:

English

Has Image Descriptions:

No

Categories:

Nonfiction, Reference, Mathematics and Statistics

Submitted By:

Bookshare Staff

Usage Restrictions:

This is a copyrighted book.