Uncertainty Quantification and Data Analysis in Applied Sciences

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.

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 (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.

Resources