Speaker

Matthias Faes became a full Professor in Reliability Engineering at TUDortmund at the age of30, since February 2022. Before, he was a post-doctoral fellow ofthe Research Foundation Flanders (FWO) workingat the Department ofMechanical Engineering ofKU Leuven, and wasalso affliated to the Institute for Risk and Reliability at the UniversityofHannover as Humboldt Fellow, He graduated summa cum laude asMaster ofScienee in Engineering Technology in 2013 and obtained hisPhD in Engineering Technology from KU Leuven in 2017. Since then,he is working on advanced methodologies for non-probabilisticuncertainty quantification under scarce data and information, including inverse and data-drivenmethods, stochastic felds and interval techniques, He is a Laureate ofthe 2017 PhD award ofthe Belgian National Committee for Applied and Theoretical Mechanics, winner ofthe 2017ECCOMAS European PhD award for best PhD thesis in 2017 on computational methods inapplied sciences and engineering in Europe, winner ofthe 2019 ISIPTA - JAR YoungResearcher Award for outstanding contributions to research on imprecise probabilities and the2023 EASD Junior Research Prize for his contribution to the development ofmethodologiesfor structural dynamies, among other awards, He is editor at Mechanical Systems and SignalProcessing and Associate Managing Editor ofthe ASCE-ASME Journal ofRisk and Uncertaintyin Engineering system parts A and B, among other journals. Matthias Faes is author ofmorethan 85 journal papers and more than 80 conference contributions and he has a Google ScholarH-index of25(2200+ citations)since 2016.

Abstract

Uncertainties are especially commonly encountered in the context ofstructural dynamics, wherefor instance the effect natural phenomena such as earthquakes or wind loads on structures hasto be considered. Indeed, due to the sheer complexity ofthe underlying physics, thecorresponding dynamical loads that act on the system often cannot be described in a crisp wayStochastic processes provide a rigorous framework to deal with the uncertainties and space/time correlations ofuncertain loads by resorting to the well-documented framework ofprobability theory, However, in practice, the analyst is often confronted with limited.incomplete or conficting sources ofdata (i.e., epistemic uncertainty). In this case, theapplication of a pure probabilistic framework to take this additional level ofuncertainty intoaccount is questionable since in this case, there is simply not enough information to constructan objective probabilistic uncertainty model.In this research seminar, I will talk about how to deal with this challenging problem ofmodelling uncertainties in space and/or time under limited data. More precisely, l will showhow to define and model uncertainties using interval models, as well as a class ofnoveltechniques to propagate such interval-valued uncertainties.