Data-driven uncertainty quantification in macroscopic traffic flow models

DOI:	https://doi.org/10.1007/s10444-022-09989-5
URL:	https://link.springer.com/article/10.1007/s10444-0...
Additional URL:	https://www.researchgate.net/publication/365297384...
Document Type:	Article
Year of publication:	2022
The title of a journal, publication series:	Advances in Computational Mathematics : AICM
Volume:	48
Issue number:	6, Article 75
Page range:	1-26
Place of publication:	Dordrecht
Publishing house:	Kluwer Akad. Publ.
ISSN:	1019-7168
Publication language:	English
Institution:	School of Business Informatics and Mathematics > Scientific Computing (Göttlich 2011-)
Subject:	510 Mathematics

Dieser Eintrag ist Teil der Universitätsbibliographie.

BASE: Würth, Alexandra ; Binois, Mickaël ; Goatin, Paola ; Göttlich, Simone

Google Scholar: Würth, Alexandra ; Binois, Mickaël ; Goatin, Paola ; Göttlich, Simone

ORCID: Würth, Alexandra ; Binois, Mickaël ; Goatin, Paola ; Göttlich, Simone

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