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Autor(en): 
  • Christian Soize
  • Uncertainty Quantification: An Accelerated Course with Advanced Applications in Computational Engineering 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 5 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Mai 2017  
    Genre:  EDV / Informatik 
     
    Applied mathematics / B / Computational Science and Engineering / Computer mathematics / Engineering mathematics / Mathematical and Computational Engineering / Mathematical and Computational Engineering Applications / Mathematics and Statistics
    ISBN:  9783319543383 
    EAN-Code: 
    9783319543383 
    Verlag:  Springer EN 
    Einband:  Gebunden  
    Sprache:  English  
    Serie:  #47 - Interdisciplinary Applied Mathematics  
    Dimensionen:  H 235 mm / B 155 mm / D 24 mm 
    Gewicht:  6506 gr 
    Seiten:  329 
    Illustration:  XXII, 329 p. 110 illus., 86 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This book presents the fundamental notions and advanced mathematical tools in the stochastic modeling of uncertainties and their quantification for large-scale computational models in sciences and engineering. In particular, it focuses in parametric uncertainties, and non-parametric uncertainties with applications from the structural dynamics and vibroacoustics of complex mechanical systems, from micromechanics and multiscale mechanics of heterogeneous materials. 
    Resulting from a course developed by the author, the book begins with a description of the fundamental mathematical tools of probability and statistics that are directly useful for uncertainty quantification. It proceeds with a well carried out description of some basic and advanced methods for constructing stochastic models of uncertainties, paying particular attention to the problem of calibrating and identifying a stochastic model of uncertainty when experimental data is available. <
    This book is intended to be a graduate-level textbook for students as well as professionals interested in the theory, computation, and applications of risk and prediction in science and engineering fields.

      



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