101-0178-01L  Uncertainty Quantification in Engineering

SemesterSpring Semester 2021
LecturersS. Marelli, B. Sudret
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
101-0178-01 GUncertainty Quantification in Engineering2 hrs
Thu16-18HPV G 5 »
S. Marelli, B. Sudret

Catalogue data

AbstractUncertainty 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.
ObjectiveAfter 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.
ContentThe 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 notesDetailed slides are provided for each lecture. A printed script gathering all the lecture slides may be bought at the beginning of the semester.
Prerequisites / NoticeA 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.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits3 credits
ExaminersS. Marelli, B. Sudret
Typeend-of-semester examination
Language of examinationEnglish
RepetitionA repetition date will be offered in the first two weeks of the semester immediately consecutive.
Additional information on mode of examinationFinal grade: 80% on final exam (on 8th of June 2021), compulsory continuous performance assessment task during semester (20% on mini-project) need not be passed on its own.

Conditions for the exam:
-2 hour written exam
-all lecture notes (printed / manuscript) allowed
-a standard simple calculator is needed (see DBAUG list provided before the exam)
-Computers, laptops, phones, tablets, advanced programmable calculators NOT allowed.

Learning materials

 
Main linkUncertainty quantification in engineering
Additional linksUQLab
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Civil Engineering MasterDigitalisation Specific CoursesWInformation
Civil Engineering MasterMajor in Structural EngineeringWInformation
Computational Biology and Bioinformatics MasterTheoryWInformation
Doctoral Department of Civil, Environmental and Geomatic EngineeringAdditional CoursesWInformation
Doctoral Department of Mechanical and Process EngineeringDoctoral and Post-Doctoral CoursesWInformation
Doctoral Department of PhysicsDoctoral and Post-Doctoral CoursesWInformation
Electrical Engineering and Information Technology MasterRecommended SubjectsWInformation
Electrical Engineering and Information Technology MasterSpecialization CoursesWInformation
Integrated Building Systems MasterSpecialised CoursesWInformation
Physics MasterGeneral ElectivesWInformation
Computational Science and Engineering MasterElectivesWInformation