Búsqueda de LIBROS DEL AUTOR: dan gabriel cacuci

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  • ADVANCES IN HIGH-ORDER PREDICTIVE MODELING
    ebook
    ADVANCES IN HIGH-ORDER PREDICTIVE MODELING
    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 fundamenta...

    $1,480.00

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  • THE SECOND-ORDER ADJOINT SENSITIVITY ANALYSIS METHODOLOGY
    ebook
    THE SECOND-ORDER ADJOINT SENSITIVITY ANALYSIS METHODOLOGY
    DAN GABRIEL CACUCI
    The Second-Order Adjoint Sensitivity Analysis Methodology generalizes the First-Order Theory presented in the author’s previous books published by CRC Press. This breakthrough has many applications in sensitivity and uncertainty analysis, optimization, data assimilation, model calibration, and reducing uncertainties in model predictions. The book has many illustrative examples ...

    $3,600.00

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  • COMPUTATIONAL METHODS FOR DATA EVALUATION AND ASSIMILATION
    ebook
    COMPUTATIONAL METHODS FOR DATA EVALUATION AND ASSIMILATION
    DAN GABRIEL CACUCI / IONEL MICHAEL NAVON / MIHAELA IONESCU-BUJOR
    Data evaluation and data combination require the use of a wide range of probability theory concepts and tools, from deductive statistics mainly concerning frequencies and sample tallies to inductive inference for assimilating non-frequency data and a priori knowledge. Computational Methods for Data Evaluation and Assimilation presents interdiscipli ...

    $1,800.00

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  • ADVANCES IN HIGH-ORDER SENSITIVITY ANALYSIS
    ebook
    ADVANCES IN HIGH-ORDER SENSITIVITY ANALYSIS
    DAN GABRIEL CACUCI
    The high-order sensitivities of model responses with respect to model parameters are notoriously difficult to compute for large-scale models involving many parameters. The neglect of higher-order response sensitivities leads to substantial errors in predicting the moments (expectation, variance, skewness, kurtosis, and higher-order) of the model response’s distribution in the p...

    $1,940.00

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