< Terug naar vorige pagina
Publicatie
Comparison of Bayesian and Interval Uncertainty Quantification: Application to the AIRMOD Test Structure
Boekbijdrage - Boekhoofdstuk Conferentiebijdrage
This paper concerns the comparison of two inverse methods for the quantification of uncertain model parameters, based on experimentally obtained measurement data of the model’s responses. Specifically, Bayesian inference is compared to a novel method for the quantification of multivariate interval uncertainty. The comparison is made by applying both methods to the AIRMOD measurement data set, and comparing their results critically in terms of obtained information and compu- tational expense. Since computational cost of the application of both methods to high-dimensional problems and realistic numerical models can become intractable, an Artificial Neural Network surrogate is used for both methods. The application of this ANN proves to limit the computational cost to a large extent, even taking the generation of the training dataset into account. Concerning the comparison of both methods, it is found that the results of the Bayesian identification provide less over- conservative bounds on the uncertainty in the responses of the AIRMOD model.
Boek: 2017 SSCI Proceedings
Pagina's: 1684 - 1691
ISBN:9781538627259
Jaar van publicatie:2017
BOF-keylabel:ja
IOF-keylabel:ja
Authors from:Higher Education