organised by Prof. A.A. Taflanidis
University of Notre Dame, IN, USA
and Prof. C. Papadimitriou
University of Thessaly, Greece
The knowledge about engineering systems and their environment, representing potential future excitations, is never complete. Uncertainties always exist and for an efficient analysis and design should be explicitly taken into account. This can be established by assigning probability models to the system and excitation properties leading to a stochastic system model description. It further requires development of appropriate computational approaches for propagating the effect of these uncertainties to the system output, when calculating its performance for analysis or design purposes, and for updating these probability models, when monitoring data become available. These approaches are in general computationally intensive and various soft computing methodologies have been established for efficiently investigating the behavior of stochastic system models. This mini-symposium will focus on soft computing methods for applications related to all fundamental tasks of system theory under uncertainty; (i) stochastic analysis; (ii) stochastic optimization; and (iii) Bayesian identification and model updating. Topics include, but are not limited to, response surface methodologies for stochastic analysis, neural networks for damage detection, genetic algorithms for stochastic optimization, Bayesian networks, and fuzzy logic approaches to uncertainty quantification.
Please note that papers that are found to fall outside the scope of this session may be considered for other sessions.
To submit abstracts for this special session, please email the organisers directly at (a.taflanidis AT nd.edu , costasp AT mie.uth.gr) or alternatively submit your abstract directly to the Conference Editor using the abstract submission portal. If you use the portal, please do not forget to mention that your abstract is part of special session CSC11-S02.