Uncertainty Quantification : An Accelerated Course with Advanced Applications in Computational Engineering /
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...
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| Main Author: | |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2017.
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| Series: | Interdisciplinary applied mathematics ;
47. |
| Subjects: | |
| Online Access: | Connect to this title online |
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.