Uncertainty Quantification

Lehrstuhl für Technische Mechanik

FAU Contact:
Oberleiter, Thomas
Pivovarov, Dmytro
Söhngen, Benjamin


Usually the parameters of a system are not know exactly, and one has to distinguish between aleatoric and epistemic uncertainties.

Aleatoric uncertainties are random and natural fluctuations, which can be described by stochastic distribution functions. These can be accounted for in simulations by the stochastic finite element method or Monte Carlo techniques.

However, epistemic uncertainties stem from a lack of knowledge, like a not yet fixed design parameter at the start of a development cycle. These can be modelled by methods of interval and fuzzy arithmetic.

Since both Monte Carlo methods and fuzzy arithmetic necessitate a large number of system evaluations, efficient model reduction methods are a central topic of research.

Related Project(s)

(FOR 2271: Prozessorientiertes Toleranzmanagement mit virtuellen Absicherungsmethoden):
Fuzzy-arithmetical modeling of processes with uncertain prarameters
Prof. Dr.-Ing. Kai Willner
(01/01/2016 - 28/02/2019)
(SPP 1886: Polymorphic uncertainty modelling for the numerical design of structures):
A hybrid Sampling-Stochastic-Finite-Element-Method for polymorphic, microstructural uncertainties in heterogeneous materials
Prof. Dr.-Ing. Paul Steinmann; Prof. Dr.-Ing. Kai Willner
(01/01/2016 - 31/03/2020)
(TRR 73: Umformtechnische Herstellung von komplexen Funktionsbauteilen mit Nebenformelementen aus Feinblechen - Blechmassivumformung):
C3: Parameter and shape optimization in finite elastoplasticity
Prof. Dr.-Ing. Paul Steinmann; Prof. Dr.-Ing. Kai Willner
(01/01/2009 - 31/12/2016)

Last updated on 2018-16-11 at 16:06