Recent Trends in the Modeling and Quantification of Non-probabilistic Uncertainty

• Published in: Archives of computational methods in engineering Vol. 27; no. 3; pp. 633 - 671
• Main Authors: Faes, Matthias, Moens, David
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.07.2020; Springer Nature B.V
• Subjects: Bayesian analysis; Engineering; Fuzzy logic; Mathematical and Computational Engineering; Modelling; Original Paper; Uncertainty
• ISSN: 1134-3060; 1886-1784
• DOI: 10.1007/s11831-019-09327-x

This paper gives an overview of recent advances in the field of non-probabilistic uncertainty quantification. Both techniques for the forward propagation and inverse quantification of interval and fuzzy uncertainty are discussed. Also the modeling of spatial uncertainty in an interval and fuzzy context is discussed. An in depth discussion of a recently introduced method for the inverse quantification of spatial interval uncertainty is provided and its performance is illustrated using a case studies taken from literature. It is shown that the method enables an accurate quantification of spatial uncertainty under very low data availability and with a very limited amount of assumptions on the underlying uncertainty. Finally, also a conceptual comparison with the class of Bayesian methods for uncertainty quantification is provided.