Uncertainty Quantification in Engineering

Dr. Nora LÜTHEN and Prof. Dr. Bruno SUDRET

Spring semester

• Thursday 16:00-18:00 (note: actual lecture time 15:45-17:30)
  
• HIL E 3
  
• DownloadTime Table (PDF, 74 KB)vertical_align_bottom

Abstract

Uncertainty quantification aims at studying the impact of aleatory- (e.g. natural variability) or epistemic uncertainty onto computational models used in science and engineering. The course introduces the basic concepts of uncertainty quantification: probabilistic modelling of data, uncertainty propagation techniques (polynomial chaos expansions) and sensitivity analysis.

Objectives

After this course students will be able to properly define an uncertainty quantification problem, select the appropriate computational methods and interpret the results in meaningful statements for field scientists, engineers and decision makers. Although the course is primarily intended to civil, mechanical and electrical engineers, it is suitable to any master student 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.

protected pageLink to the course materiallock (Restricted Access to ETH members)

Lecture notes are available upon request  (send an email to ).