Organizers
Non-deterministic analysis is attracting increasing research interest in several engineering fields as a consequence of the growing diffusion of new materials, advanced technologies, and complex systems which require more careful decision-making. In this context, a key issue is the selection of the most suitable mathematical representation of uncertainties. In many cases, limited data can be acquired from direct measurements to capture the inherent variability of the input parameters. Several approaches are currently emerging to perform uncertainty treatment under limited data, ranging from purely interval and fuzzy approaches to polymorphic concepts. This mini-symposium aims to collect the most recent theoretical and computational developments in the application of these approaches to engineering problems. Researchers focusing on efficient Uncertainty Quantification, ranging from uncertainty propagation methodologies, inverse identification and quantification techniques to optimization, as well as advanced procedures for reliability and sensitivity analysis in the presence of limited data are invited to submit an abstract to this mini-symposium. Recent implementations and developments of Artificial Intelligence and Machine Learning for dealing with uncertainty are also welcome.
- Alba Sofi, University “Mediterranea” of Reggio Calabria, Italy, alba.sofi@unirc.it
- David Moens, KU Leuven, Belgium, david.moens@kuleuven.be
- Edoardo Patelli, University of Strathclyde, Scotland, edoardo.patelli@strath.ac.uk
- Matthias Faes, Dortmund University, Germany, matthias.faes@tu-dortmund.de
- Michael Hanss, University of Stuttgart, Germany, michael.hanss@itm.uni-stuttgart.de
Non-deterministic analysis is attracting increasing research interest in several engineering fields as a consequence of the growing diffusion of new materials, advanced technologies, and complex systems which require more careful decision-making. In this context, a key issue is the selection of the most suitable mathematical representation of uncertainties. In many cases, limited data can be acquired from direct measurements to capture the inherent variability of the input parameters. Several approaches are currently emerging to perform uncertainty treatment under limited data, ranging from purely interval and fuzzy approaches to polymorphic concepts. This mini-symposium aims to collect the most recent theoretical and computational developments in the application of these approaches to engineering problems. Researchers focusing on efficient Uncertainty Quantification, ranging from uncertainty propagation methodologies, inverse identification and quantification techniques to optimization, as well as advanced procedures for reliability and sensitivity analysis in the presence of limited data are invited to submit an abstract to this mini-symposium. Recent implementations and developments of Artificial Intelligence and Machine Learning for dealing with uncertainty are also welcome.
Sponsoring committee
Committee on Probability and Statistics in the Physical Sciences of the Bernoulli Society (http://www2.aueb.gr/bs-cpsps/)
