G.I. Schuëller1, C. Soize2, R.G. Ghanem3, W.K. Liu4
1University of Innsbruck/AT, 2Universite Paris-Est Marne-la-Vallee/FR, 3University of Southern California/US, 4Northwestern University/US
Recent developments in computational engineering science and mechanics, as well as sensing resources provide us with the ability to infer about the physical phenomena with increasingly detailed resolutions and to better develop computational models accounting for uncertainties. In other words, model un- certainties play also a major role in uncertainty quantification. In addition, when accurate experiments are available, it is necessary to characterize the interplay between experimentally observed cause and effect.
In many problems of interest and urgency, a non-deterministic framework allows uncertainty modeling to be the best approach in permitting the description of the uncertainties of the parameters, modeling errors and inadequacies, as well as numerical approximation errors. These uncertainties conspire, with interpretation and analysis tools, to affect the predictive power of accumulated knowledge.
This minisyposium will bring together current research efforts attempting to characterize and manage uncertainties in various stages of the prediction process. In particular, research results in the following areas will be highlighted: