Uncertainty Quantification : An Accelerated Course with Advanced Applications in Computational Engineering / by Christian Soize

By:

Soize, Christian [aut]

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Interdisciplinary Applied Mathematics ; 47 | SpringerLink BücherPublisher: Cham : Springer, 2017Description: Online-Ressource (XXII, 329 p. 110 illus., 86 illus. in color, online resource)ISBN:

9783319543390

Subject(s):

Additional physical formats: 9783319543383 | Druckausg.: 978-3-319-54338-3 | Erscheint auch als: Uncertainty quantification. Druck-Ausgabe Cham : Springer, 2017. xxii, 329 SeitenDDC classification:

MSC: MSC: *60-01 | 62-01 | 65-01 | 65C50 | 60E15 | 62P30 | 62F15 | 62M20 | 60J22RVK: RVK: SK 835LOC classification:

QA71-90

DOI: DOI: 10.1007/978-3-319-54339-0Online resources:

Summary: 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 MediaSummary: 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 fieldsPPN: PPN: 1658316339Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SXMS | ZDB-2-SMA

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