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...
Κύριος συγγραφέας: | |
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Συγγραφή απο Οργανισμό/Αρχή: | |
Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
Γλώσσα: | English |
Έκδοση: |
Cham :
Springer International Publishing : Imprint: Springer,
2017.
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Σειρά: | Interdisciplinary Applied Mathematics,
47 |
Θέματα: | |
Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.