Uncertainty Quantification An Accelerated Course with Advanced Applications in Computational Engineering /

• Main Author: Soize, Christian (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Series: Interdisciplinary Applied Mathematics, 47
• Subjects: Mathematics.; Computer mathematics.; Probabilities.; Applied mathematics.; Engineering mathematics.; Computational Science and Engineering.; Appl.Mathematics/Computational Methods of Engineering.; Probability Theory and Stochastic Processes.

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 wi...

Table of Contents:

• 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.