Uncertainty Quantification
Produktnummer:
189f359bb730b84348800a965f8bdf884e
Autor: | Soize, Christian |
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Themengebiete: | High Stochastic Dimension MCMC Methods Maximum Entropy Principle Model-parameter Uncertainties Model Uncertainties Non-Gaussian Random Fields Nonparametric Uncertainties Polynomial Chaos Expansion Random Matrices Robust Design |
Veröffentlichungsdatum: | 25.07.2018 |
EAN: | 9783319853727 |
Sprache: | Englisch |
Seitenzahl: | 329 |
Produktart: | Kartoniert / Broschiert |
Verlag: | Springer International Publishing |
Untertitel: | An Accelerated Course with Advanced Applications in Computational Engineering |
Produktinformationen "Uncertainty Quantification"
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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