Anonymous
Posted June 29, 2011
I bought this book expected to learn something about the theory of uncertainty quantification and the numerical analysis used to approximate stochastic partial differential equations. This is not such a book!
This is a simple overview (in an unorganized fashion) of classical spectral methods re-cast in a stochastic setting. There isn't really anything novel presented here. I don't see how a graduate student would benefit from this elementary exploration with no
real analytical explanations. Similar to his many papers, the author presents
more of a cookbook for computational scientists who are looking for a recipe to apply to some stochastic application. If you're looking for a rich explanation of this field then this is not the book for you.
More About This Textbook
Overview
"Short and comprehensive, this book is appropriate for novices of polynomial chaos. Many diverse fields are adopting this method, and this book can be used for first-year graduate studies as well as senior undergraduate courses. The book includes important new developments, such as non-Gaussian processes and stochastic collocation methods."—George Karniadakis, Brown University
Editorial Reviews
Mathematical Reviews
[A]s a newbie to this field, by reading this lively written text I was able to gain insight into this really interesting and challenging matter.— Peter Mathé
Mathematical Reviews - Peter Mathe
[A]s a newbie to this field, by reading this lively written text I was able to gain insight into this really interesting and challenging matter.Mathematical Reviews - Peter Mathé
[A]s a newbie to this field, by reading this lively written text I was able to gain insight into this really interesting and challenging matter.From the Publisher
"[A]s a newbie to this field, by reading this lively written text I was able to gain insight into this really interesting and challenging matter."—Peter Math, Mathematical ReviewsProduct Details
Related Subjects
Meet the Author
Dongbin Xiu is associate professor of mathematics at Purdue University.
Table of Contents
Preface xi
Chapter 1 Introduction 1
1.1 Stochastic Modeling and Uncertainty Quantification 1
1.1.1 Burgers' Equation: An Illustrative Example 1
1.1.2 Overview of Techniques 3
1.1.3 Burgers' Equation Revisited 4
1.2 Scope and Audience 5
1.3 A Short Review of the Literature 6
Chapter 2 Basic Concepts of Probability Theory 9
2.1 Random Variables 9
2.2 Probability and Distribution 10
2.2.1 Discrete Distribution 11
2.2.2 Continuous Distribution 12
2.2.3 Expectations and Moments 13
2.2.4 Moment-Generating Function 14
2.2.5 Random Number Generation 15
2.3 Random Vectors 16
2.4 Dependence and Conditional Expectation 18
2.5 Stochastic Processes 20
2.6 Modes of Convergence 22
2.7 Central Limit Theorem 23
Chapter 3 Survey of Orthogonal Polynomials and Approximation Theory 25
3.1 Orthogonal Polynomials 25
3.1.1 Orthogonality Relations 25
3.1.2 Three-Term Recurrence Relation 26
3.1.3 Hypergeometric Series and the Askey Scheme 27
3.1.4 Examples of Orthogonal Polynomials 28
3.2 Fundamental Results of Polynomial Approximation 30
3.3 Polynomial Projection 31
3.3.1 Orthogonal Projection 31
3.3.2 Spectral Convergence 33
3.3.3 Gibbs Phenomenon 35
3.4 Polynomial Interpolation 36
3.4.1 Existence 37
3.4.2 Interpolation Error 38
3.5 Zeros of Orthogonal Polynomials and Quadrature 39
3.6 Discrete Projection 41
Chapter 4 Formulation of Stochastic Systems 44
4.1 Input Parameterization: Random Parameters 44
4.1.1 Gaussian Parameters 45
4.1.2 Non-Gaussian Parameters 46
4.2 Input Parameterization: Random Processes and Dimension Reduction 47
4.2.1 Karhunen-Loeve Expansion 47
4.2.2 Gaussian Processes 50
4.2.3 Non-Gaussian Processes 50
4.3 Formulation of Stochastic Systems 51
4.4 Traditional Numerical Methods 52
4.4.1 Monte Carlo Sampling 53
4.4.2 Moment Equation Approach 54
4.4.3 Perturbation Method 55
Chapter 5 Generalized Polynomial Chaos 57
5.1 Definition in Single Random Variables 57
5.1.1 Strong Approximation 58
5.1.2 Weak Approximation 60
5.2 Definition in Multiple Random Variables 64
5.3 Statistics 67
Chapter 6 Stochastic Galerkin Method 68
6.1 General Procedure 68
6.2 Ordinary Differential Equations 69
6.3 Hyperbolic Equations 71
6.4 Diffusion Equations 74
6.5 Nonlinear Problems 76
Chapter 7 Stochastic Collocation Method 78
7.1 Definition and General Procedure 78
7.2 Interpolation Approach 79
7.2.1 Tensor Product Collocation 81
7.2.2 Sparse Grid Collocation 82
7.3 Discrete Projection: Pseudospectral Approach 83
7.3.1 Structured Nodes: Tensor and Sparse Tensor Constructions 85
7.3.2 Nonstructured Nodes: Cubature 86
7.4 Discussion: Galerkin versus Collocation 87
Chapter 8 Miscellaneous Topics and Applications 89
8.1 Random Domain Problem 89
8.2 Bayesian Inverse Approach for Parameter Estimation 95
8.3 Data Assimilation by the Ensemble Kalman Filter 99
8.3.1 The Kalman Filter and the Ensemble Kalman Filter 100
8.3.2 Error Bound of the EnKF 101
8.3.3 Improved EnKF via gPC Methods 102
Appendix A Some Important Orthogonal Polynomials in the Askey Scheme 105
A.1 Continuous Polynomials 106
A.1.1 Hermite Polynomial Hn (x) and Gaussian Distribution 106
A.1.2 Laguerre Polynomial Ln(α) (x) and Gamma Distribution 106
A.1.3 Jacobi Polynomial Pn(α, β) (x) and Beta Distribution 107
A.2 Discrete Polynomials 108
A.2.1 Charlier Polynomial Cn(x; a) and Poisson Distribution 108
A.2.2 Krawtchouk Polynomial Kn (x; p, N) and Binomial Distribution 108
A.2.3 Meixner Polynomial Mn (x; β, c) and Negative Binomial Distribution 109
A.2.4 Hahn Polynomial Qn (x; α, β, N) and Hypergeometric Distribution 110
Appendix B The Truncated Gaussian Model G(α, β) 113
References 117
Index 127