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170502 ||| eng |
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|a 9783319543390
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| 100 |
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|a Soize, Christian
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| 245 |
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|a Uncertainty Quantification
|h Elektronische Ressource
|b An Accelerated Course with Advanced Applications in Computational Engineering
|c by Christian Soize
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| 250 |
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|a 1st ed. 2017
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2017, 2017
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| 300 |
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|a XXII, 329 p. 110 illus., 86 illus. in color
|b online resource
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| 505 |
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|a 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
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| 653 |
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|a Engineering mathematics
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| 653 |
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|a Mathematics / Data processing
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| 653 |
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|a Probability Theory
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| 653 |
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|a Computational Science and Engineering
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| 653 |
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|a Engineering / Data processing
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| 653 |
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|a Mathematical and Computational Engineering Applications
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| 653 |
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|a Probabilities
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a Interdisciplinary Applied Mathematics
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| 028 |
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|a 10.1007/978-3-319-54339-0
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-319-54339-0?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 003.3
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| 520 |
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|a 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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