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Uncertainty Quantification

An Accelerated Course with Advanced Applications in Computational Engineering

Medium: Buch
ISBN: 978-3-319-85372-7
Verlag: Birkhäuser
Erscheinungstermin: 25.07.2018
Lieferfrist: bis zu 10 Tage

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.


Produkteigenschaften


  • Artikelnummer: 9783319853727
  • Medium: Buch
  • ISBN: 978-3-319-85372-7
  • Verlag: Birkhäuser
  • Erscheinungstermin: 25.07.2018
  • Sprache(n): Englisch
  • Auflage: Softcover Nachdruck of the original 1. Auflage 2017
  • Serie: Interdisciplinary Applied Mathematics
  • Produktform: Kartoniert, Paperback
  • Gewicht: 5329 g
  • Seiten: 329
  • Format (B x H x T): 155 x 235 x 20 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

Fundamental Notions in Stochastic Modeling of Uncertainties and their Propagation in Computational Models.- Elements of Probability Theory.- Markov Process and Stochastic Differential Equation.- MCMC Methods for Generating Realizations and for Estimating the Mathematical Expectation of Nonlinear Mappings of Random Vectors.- Fundamental Probabilistic Tools for Stochastic Modeling of Uncertainties.- Brief Overview of Stochastic Solvers for the Propagation of Uncertainties.- Fundamental Tools for Statistical Inverse Problems.- Uncertainty Quantification in Computational Structural Dynamics and Vibroacoustics.- Robust Analysis with Respect to the Uncertainties for Analysis, Updating, Optimization, and Design.- Random Fields and Uncertainty Quantification in Solid Mechanics of Continuum Media.