Introduction to Uncertainty Quantification
Uncertainty quantification is a topic of increasing practical importance at the intersection of applied mathematics, statistics, computation, and numerous application areas in science and engineering. This text provides a framework in which the main objectives of the field of uncertainty quantificat...
| Main Author: | |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2015, 2015
|
| Edition: | 1st ed. 2015 |
| Series: | Texts in Applied Mathematics
|
| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Introduction
- Measure and Probability Theory
- Banach and Hilbert Spaces
- Optimization Theory
- Measures of Information and Uncertainty
- Bayesian Inverse Problems
- Filtering and Data Assimilation
- Orthogonal Polynomials and Applications
- Numerical Integration
- Sensitivity Analysis and Model Reduction
- Spectral Expansions
- Stochastic Galerkin Methods
- Non-Intrusive Methods
- Distributional Uncertainty
- References
- Index