151-0104-00L Uncertainty Quantification for Engineering & Life Sciences
Semester | Spring Semester 2014 |
Lecturers | P. Koumoutsakos, K. Papadimitriou |
Periodicity | yearly recurring course |
Language of instruction | English |
Abstract | Quantification of uncertainties in computational models pertaining to applications in engineering and life sciences. Exploitation of massively available data to develop computational models with quantifiable predictive capabilities. Applications of Uncertainty Quantification and Propagation to problems in mechanics, control, systems and cell biology. |
Objective | The course will teach fundamental concept of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences. Emphasis will be placed on practical and computational aspects of UQ+P including the implementation of relevant algorithms in multicore architectures. |
Content | Topics that will be covered include: Reliability analysis, parametric and non-parametric estimation, Bayesian inference, Markov Chain Monte Carlo |
Lecture notes | The class will be largely based on the book: Data Analysis: A Bayesian Tutorial by Devinderjit Sivia as well as on class notes and related literature that will be distributed in class. |
Literature | 1. Data Analysis: A Bayesian Tutorial by Devinderjit Sivia 2. Probability Theory: The Logic of Science by E. T. Jaynes 3. Class Notes |
Prerequisites / Notice | Fundamentals of Probability, Fundamentals of Computational Modeling |