Bruno Sudret: Catalogue data in Spring Semester 2019

Name	Prof. Dr. Bruno Sudret
Name variants	Bruno Sudret B. Sudret
Field	Risk, Safety and Uncertainty Quantification in Civil Engineering
Address	Risiko, Sich., Ungew. im Bauing.w. ETH Zürich, HIL E 22.3 Stefano-Franscini-Platz 5 8093 Zürich SWITZERLAND
Telephone	+41 44 633 04 44
E-mail	sudret@ethz.ch
URL	http://www.rsuq.ethz.ch
Department	Civil, Environmental and Geomatic Engineering
Relationship	Full Professor

Number	Title	ECTS	Hours	Lecturers
101-0178-01L	Uncertainty Quantification in Engineering	3 credits	2G	B. Sudret, S. Marelli
Abstract	Uncertainty quantification aims at studying the impact of aleatory and epistemic uncertainty onto computational models used in science and engineering. The course introduces the basic concepts of uncertainty quantification: probabilistic modelling of data (copula theory), uncertainty propagation techniques (Monte Carlo simulation, polynomial chaos expansions), and sensitivity analysis.
Learning objective	After this course students will be able to properly pose an uncertainty quantification problem, select the appropriate computational methods and interpret the results in meaningful statements for field scientists, engineers and decision makers. The course is suitable for any master/Ph.D. student in engineering or natural sciences, physics, mathematics, computer science with a basic knowledge in probability theory.
Content	The course introduces uncertainty quantification through a set of practical case studies that come from civil, mechanical, nuclear and electrical engineering, from which a general framework is introduced. The course in then divided into three blocks: probabilistic modelling (introduction to copula theory), uncertainty propagation (Monte Carlo simulation and polynomial chaos expansions) and sensitivity analysis (correlation measures, Sobol' indices). Each block contains lectures and tutorials using Matlab and the in-house software UQLab (www.uqlab.com).
Lecture notes	Detailed slides are provided for each lecture. A printed script gathering all the lecture slides may be bought at the beginning of the semester.
Prerequisites / Notice	A basic background in probability theory and statistics (bachelor level) is required. A summary of useful notions will be handed out at the beginning of the course. A good knowledge of Matlab is required to participate in the tutorials and for the mini-project.
101-0190-08L	Uncertainty Quantification and Data Analysis in Applied Sciences Does not take place this semester. The course should be open to doctoral students from within ETH and UZH who work in the field of Computational Science. External graduate students and other auditors will be allowed by permission of the instructors.	3 credits	4G	E. Chatzi, P. Koumoutsakos, B. Sudret
Abstract	The course presents fundamental concepts and advanced methodologies for handling and interpreting data in relation with models. It elaborates on methods and tools for identifying, quantifying and propagating uncertainty through models of systems with applications in various fields of Engineering and Applied science.
Learning objective	The course is offered as part of the Computational Science Zurich (CSZ) (http://www.zhcs.ch/) graduate program, a joint initiative between ETH Zürich and University of Zürich. This CSZ Block Course aims at providing a graduate level introduction into probabilistic modeling and identification of engineering systems. Along with fundamentals of probabilistic and dynamic system analysis, advanced methods and tools will be introduced for surrogate and reduced order models, sensitivity and failure analysis, parallel processing, uncertainty quantification and propagation, system identification, nonlinear and non-stationary system analysis.
Content	The topics to be covered are in three broad categories, with a detailed outline available online (see Learning Materials). Track 1: Uncertainty Quantification and Rare Event Estimation in Engineering, offered by the Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich (20 hours) Lecturers: Prof. Dr. Bruno Sudret, Dr. Stefano Marelli Track 2: Bayesian Inference and Uncertainty Propagation, offered the by the System Dynamics Laboratory, University of Thessaly, and the Chair of Computational Science, ETH Zurich (20 hours) Lecturers: Prof. Dr. Costas Papadimitriou, Dr. Panagiotis Hadjidoukas, Prof. Dr. Petros Koumoutsakos Track 3: Data-driven Identification and Simulation of Dynamic Systems, offered the by the Chair of Structural Mechanics, ETH Zurich (20 hours) Lecturers: Prof. Dr. Eleni Chatzi, Dr. Vasilis Dertimanis. The lectures will be complemented via a comprehensive series of interactive Tutorials will take place.
Lecture notes	The course script is composed by the lecture slides, which will be continuously updated throughout the duration of the course on the CSZ website.
Literature	Suggested Reading: Track 2 : E.T. Jaynes: Probability Theory: The logic of Science Track 3: T. Söderström and P. Stoica: System Identification, Prentice Hall International, Link see Learning Materials. Xiu, D. (2010) Numerical methods for stochastic computations - A spectral method approach, Princeton University press. Smith, R. (2014) Uncertainty Quantification: Theory, Implementation and Applications SIAM Computational Science and Engineering, Lemaire, M. (2009) Structural reliability, Wiley. Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. & Tarantola, S. (2008) Global Sensitivity Analysis - The Primer, Wiley.
Prerequisites / Notice	Introductory course on probability theory Fair command on Matlab
101-1187-00L	Colloquium "Structural Engineering"	0 credits	2K	B. Stojadinovic, E. Chatzi, M. Fontana, A. Frangi, W. Kaufmann, B. Sudret, T. Vogel
Abstract	Professors from national and international universities, technical experts from private industry as well as research associates of the Institute of Structural Engineering (IBK) are invited to present recent research results and specific projects. The colloquium is addressed to students, academics as well as practicing engineers.
Learning objective	Become acquainted with recent research results in structural engineering.
364-1058-00L	Risk Center Seminar Series Number of participants limited to 50.	0 credits	2S	A. Bommier, D. Basin, D. N. Bresch, L.‑E. Cederman, P. Cheridito, H. Gersbach, H. R. Heinimann, M. Larsson, G. Sansavini, F. Schweitzer, D. Sornette, B. Stojadinovic, B. Sudret, U. A. Weidmann, S. Wiemer, M. Zeilinger, R. Zenklusen
Abstract	This course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling and governing complex socio-economic systems, and managing risks and crises. Students and other guests are welcome.
Learning objective	Participants should learn to get an overview of the state of the art in the field, to present it in a well understandable way to an interdisciplinary scientific audience, to develop novel mathematical models and approaches for open problems, to analyze them with computers or other means, and to defend their results in response to critical questions. In essence, participants should improve their scientific skills and learn to work scientifically on an internationally competitive level.
Content	This course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling complex socio-economic systems and crises. For details of the program see the webpage of the seminar. Students and other guests are welcome.
Lecture notes	There is no script, but the sessions will be recorded and be made available. Transparencies of the presentations may be put on the course webpage.
Literature	Literature will be provided by the speakers in their respective presentations.
Prerequisites / Notice	Participants should have relatively good scientific, in particular mathematical skills and some experience of how scientific work is performed.