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    Fundamentals of Uncertainty Quantification for  Engineers: Methods and Models

    Fundamentals of Uncertainty Quantification for Engineers by Wang, Yan; Tran, Anh.V.; Mcdowell, David L.;

    Methods and Models

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      • Publisher's listprice EUR 195.00
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        82 719 Ft (78 780 Ft + 5% VAT)
      • Discount 10% (cc. 8 272 Ft off)
      • Discounted price 74 447 Ft (70 902 Ft + 5% VAT)

    82 719 Ft

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    Long description:

    Fundamentals of Uncertainty Quantification for Engineers: Methods and Models provides a comprehensive introduction to uncertainty quantification (UQ) accompanied by a wide variety of applied examples, implementation details, and practical exercises to reinforce the concepts outlined in the book. Sections start with a review of the history of probability theory and recent developments of UQ methods in the domains of applied mathematics and data science. Major concepts of probability axioms, conditional probability, and Bayes’ rule are discussed and examples of probability distributions in parametric data analysis, reliability, risk analysis, and materials informatics are included.

    Random processes, sampling methods, and surrogate modeling techniques including multivariate polynomial regression, Gaussian process regression, multi-fidelity surrogate, support-vector machine, and decision tress are also covered. Methods for model selection, calibration, and validation are introduced next, followed by chapters on sensitivity analysis, stochastic expansion methods, Markov models, and non-probabilistic methods. The book concludes with a chapter describing the methods that can be used to predict UQ in systems, such as Monte Carlo, stochastic expansion, upscaling, Langevin dynamics, and inverse problems, with example applications in multiscale modeling, simulations, and materials design.




    • Introduces all major topics of uncertainty quantification with engineering examples, implementation details, and practical exercises provided in all chapters
    • Features examples from a wide variety of science and engineering disciplines (e.g. aerospace, mechanical, material, manufacturing, multiscale simulation)
    • Discusses materials informatics, sampling methods, surrogate modeling techniques, decision tress, multivariate polynomial regression, and more

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    Table of Contents:

    1. Introduction to Uncertainty Quantification for Engineers
    2. Probability and Statistics in Uncertainty Quantification
    3. Random Processes in Uncertainty Quantification
    4. Sampling Methods in Uncertainty Quantification
    5. Surrogate Modeling in Uncertainty Quantification
    6. Model Selection, Calibration, and Validation in Uncertainty Quantification
    7. Sensitivity Analysis in Uncertainty Quantification
    8. Stochastic Expansion Methods in Uncertainty Quantification
    9. Markov Models
    10. Non-Probabilistic Methods in Uncertainty Quantification
    11. Uncertainty propagation in Uncertainty Quantification

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