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

di Dan Gabriel Cacuci (Autore)

Taylor & Francis Ltd, 2024

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.

• Autore: Dan Gabriel Cacuci
• Editore: Taylor & Francis Ltd
• Collana: Advances in Applied Mathematics
• Anno: 2024
• Rilegatura: Hardback
• Pagine: 288 p.
• Dimensioni: 234 x 156 mm
• Peso: 720 gr.
• EAN: 9781032740560

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