MS 02

Data-driven inverse methods for uncertainty quantification

Organizers

Matthias Faes, matthias.faesnull@kuleuven.be

KU Leuven, Department of Mechanical Engineering, Technology campus De Nayer, St.-Katelijne-Waver, Belgium

Sifeng Bi, sifeng.binull@bit.edu.cn

School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China

Matteo Broggi, brogginull@irz.uni-hannover.de

Institute for Risk and Reliability, Leibniz University Hannover, Germany

Edoardo Patelli, edoardo.patellinull@strath.ac.uk

Department of Civil and Environmental Engineering, Strathclyde University, Glasgow, UK

Abstract

Recent developments in non-deterministic modelling approaches introduced a very broad spectrum of highly advanced numerical methods for reliability analysis and uncertainty quantification, including probabilistic, interval, fuzzy or imprecise methods. The application of these techniques however requires the analyst to specify the relevant uncertain model parameters with a high degree of accuracy. Since direct measurement of such model quantities is often not feasible, or in practice too expensive, data-driven inverse techniques are commonly applied.

This mini-symposium aims to gather experts researchers, academics and practising engineers concerned with data-driven inverse methods for uncertainty quantification to present their recent findings, methodological developments and innovative applications. Papers discussing advances in techniques from both frequentist and Bayesian interpretations of probability theory, as well as interval and possibilistic methods and concepts based on imprecise probabilities are invited. Next to these more traditional uncertainty quantification methods, also contributions related to machine learning are particularly welcomed.