TY  - BOOK
AU  - Soize,Christian
ED  - SpringerLink (Online service)
TI  - Uncertainty Quantification: An Accelerated Course with Advanced Applications in Computational Engineering 
T2  - Interdisciplinary Applied Mathematics,
SN  - 9783319543390
AV  - QA71-90 
U1  - 004 23
PY  - 2017///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Computer science
KW  - Engineering mathematics
KW  - Distribution (Probability theory
KW  - Computational Science and Engineering
KW  - Mathematical and Computational Engineering
KW  - Probability Theory and Stochastic Processes
N1  - 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
N2  - 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
UR  - https://doi.org/10.1007/978-3-319-54339-0
ER  -