From 2 November 2020, the autumn semester 2020 will take place online. Exceptions: Courses that can only be carried out with on-site presence.
Please note the information provided by the lecturers via e-mail.

Petros Koumoutsakos: Catalogue data in Autumn Semester 2016

Name Prof. Dr. Petros Koumoutsakos
FieldComputational Science
Address
Professur f. Computational Science
ETH Zürich, CLT F 12
Clausiusstrasse 33
8092 Zürich
SWITZERLAND
Telephone+41 44 632 52 58
E-mailpetros@ethz.ch
URLhttp://www.cse-lab.ethz.ch/index.php?&option=com_content&view=article&id=100&catid=38
DepartmentMechanical and Process Engineering
RelationshipFull Professor

NumberTitleECTSHoursLecturers
151-0104-00LUncertainty Quantification for Engineering & Life Sciences Restricted registration - show details
Does not take place this semester.
Number of participants limited to 60.
4 credits3GP. Koumoutsakos
AbstractQuantification 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.
ObjectiveThe 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.
ContentTopics that will be covered include: Uncertainty quantification under
parametric and non-parametric modelling uncertainty, Bayesian inference with model class assessment, Markov Chain Monte Carlo simulation, prior and posterior reliability analysis.
Lecture notesThe 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.
Literature1. Data Analysis: A Bayesian Tutorial by Devinderjit Sivia
2. Probability Theory: The Logic of Science by E. T. Jaynes
3. Class Notes
Prerequisites / NoticeFundamentals of Probability, Fundamentals of Computational Modeling
151-1053-00LThermo- and Fluid Dynamics Information 0 credits2KP. Jenny, R. S. Abhari, K. Boulouchos, P. Koumoutsakos, C. Müller, H. G. Park, D. Poulikakos, H.‑M. Prasser, T. Rösgen, A. Steinfeld
AbstractCurrent advanced research activities in the areas of thermo- and fluid dynamics are presented and discussed, mostly by external speakers.
ObjectiveKnowledge of advanced research in the areas of thermo- and fluid dynamics