Advances in High-Order Predictive Modeling - Methodologies and Illustrative Problem

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 2^nd-BERRU-PM methodology, an acronym that designates the -second-order best-estimate with reduced uncertainties (2^nd-BERRU) predictive modeling (PM).- The 2^nd-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 2^nd-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 or