Stefano Marelli: Katalogdaten im Frühjahrssemester 2020
|Name||Herr Dr. Stefano Marelli|
Risiko, Sich., Ungew. im Bauing.w.
ETH Zürich, HIL E 23.1
|Telefon||+41 44 633 06 70|
|Departement||Bau, Umwelt und Geomatik|
|101-0178-01L||Uncertainty Quantification in Engineering||3 KP||2G||S. Marelli|
|Kurzbeschreibung||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.|
|Lernziel||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.|
|Inhalt||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).|
|Skript||Detailed slides are provided for each lecture. A printed script gathering all the lecture slides may be bought at the beginning of the semester.|
|Voraussetzungen / Besonderes||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 |
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 KP||4G||E. Chatzi, P. Koumoutsakos, S. Marelli, V. Ntertimanis, K. Papadimitriou|
|Kurzbeschreibung||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.|
|Lernziel||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.
|Inhalt||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 (18 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 (18 hours)
Lecturers: Prof. Dr. Costas Papadimitriou, Dr. Georgios Arampatzis, Prof. Dr. Petros Koumoutsakos
Track 3: Data-driven Identification and Simulation of Dynamic Systems, offered the by the Chair of Structural Mechanics, ETH Zurich (18 hours)
Lecturers: Prof. Dr. Eleni Chatzi, Dr. Vasilis Dertimanis
The lectures will be complemented via a comprehensive series of interactive Tutorials will take place.
|Skript||The course script is composed by the lecture slides, which will be continuously updated throughout the duration of the course on the CSZ website.|
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.
|Voraussetzungen / Besonderes||Introductory course on probability theory|
Fair command on Matlab