Probability, Random Processes, and Statistics; Markov Chains; Sampling and Monte Carlo Methods; Parameter Estimation; Uncertainty Propagation in Models; Stochastic Spectral Methods; Surrogate Models and Advanced Topics.
For further information see the academic catalog: IAM768 - Methods and Applications of Uncertainty Quantification
Course Objectives: Students are expected to gain, besides theoretical concepts, programming skills that are related to Uncertainty Quantification and related applications.
Course Learning Outcomes: By the end of this course, students should be equipped with fundamental methods of Uncertainty Quantification, and related concepts from Scientific Computing, Finance and Statistics, and Physics and Engineering.
Instructional Methods: Classical Lectures and Presentation of Students.
Course Website / Course Management System: odtuclass.iam.metu.edu.tr
Weekly Outline / Tentative Course Schedule:
- Introduction and Preliminaries
- Motivating Applications and Prototypical Models
- Probability, Random Processes, and Statistics; Markov Chains
- Sampling and Monte Carlo Methods
- Computing Expectations/Integrals, Moments; Moment Approximations using Limit Theorems
- Monte Carlo Methods, variance reduction techniques; importance sampling
- Parameter Estimation
- Frequentist Techniques: Linear Regression, Nonlinear Parameter Estimation, Optimisation and Algorithms (related content from least squares, regularization, etc.)
- Bayesian Techniques: Markov Chain Monte Carlo, Metropolis-Hasting Algorithms, and Sequential Monte Carlo and Particle Filter; Delayed Rejection Adaptive Metropolis (DRAM), DiffeRential Evolution Adaptive Metropolis (DREAM)
- Stochastic Spectral Methods
- Orthogonal Polynomials, Piecewise Polynomial Approximation, Interpolation, Projection, (Gaussian) Quadrature Rules; Finite Elements (and, possibly, Finite Differences), Galerkin (Finite Element) Methods, (Polynomial) Spectral Methods
- Spectral Expansion and Stochastic Spectral Methods: Karhunen-Loève Expansion, (generalised) Polynomial Chaos Expansion (gPC); Stochastic Galerkin Methods, Collocation, and Discrete Projection
- Surrogate Models and Advanced Topics
Required Textbook/s & Readings:
- Ralph C. Smith, Uncertainty Quantification: Theory, Implementation, and Applications, SIAM, 2014
- O. P. Le Maître, O. M. Knio, Spectral Methods for Uncertainty Quantification: with applications to Computational Fluid Dynamics, Springer, 2010
- Mats G. Larson, Fredrik Bengzon, The Finite Element Method: Theory, Implementation, and Applications, Springer, 2013
- Steven M. Kay, Intuitive Probability and Random Processes using MATLAB, Springer, 2006
- Paul Glasserman, Monte Carlo Methods in Financial Engineering, Springer, 2003