Efficient Quantification of Model Uncertainties When De-boarding a Train

Florian Künzner; Tobias Neckel; Hans-Joachim Bungartz; Felix Dietrich; Gerta Köster

doi:10.17815/CD.2020.104

Efficient Quantification of Model Uncertainties When De-boarding a Train

Authors

Florian Künzner Department of Informatics, Technical University of Munich, Munich, Germany
Tobias Neckel Department of Informatics, Technical University of Munich, Munich, Germany
Hans-Joachim Bungartz Department of Informatics, Technical University of Munich, Munich, Germany
Felix Dietrich Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, USA
Gerta Köster Munich University of Applied Sciences, Munich, Germany

DOI:

https://doi.org/10.17815/CD.2020.104

Keywords:

pedestrian dynamics, uncertainty quantification, surrogate models

Abstract

It is difficult to provide live simulation systems for decision support. Time is limited and uncertainty quantification requires many simulation runs. We combine a surrogate model with the stochastic collocation method to overcome time and storage restrictions and show a proof of concept for a de-boarding scenario of a train.

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Published

12.08.2020

How to Cite

Künzner, F., Neckel, T., Bungartz, H.-J., Dietrich, F., & Köster, G. (2020). Efficient Quantification of Model Uncertainties When De-boarding a Train. Collective Dynamics, 5, 477–479. https://doi.org/10.17815/CD.2020.104

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Issue

Vol. 5 (2020)

Section

Proceedings of Pedestrian and Evacuation Dynamics 2018

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