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Uncertainty Quantification in Hierarchical Vehicular Flow Models
;
2021
Online
DOI: 10.48550/ARXIV.2108.07589
Einrichtungen
- Lehr- und Forschungsgebiet Mathematik (114620)
- Fachgruppe Mathematik (110000)
- Internet of Production (080067)
- Institut für Geometrie und Praktische Mathematik (111400)
Projekte
- WS-A.III - Functional Perspective (X080067-WS-A.III) (X080067-WS-A.III)
- DFG project 390621612 - EXC 2023: Internet of Production (IoP) (390621612) (390621612)
Inhaltliche Beschreibung (Schlagwörter)
traffic flow, BGK models, stochastic Galerkin, Aw-Rascle-Zhang model, kinetic equations (frei)
External link: 
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Dokumenttyp
Preprint
Format
online
Sprache
English
Externe Identnummern
arXiv: arXiv:2108.07589
Interne Identnummern
RWTH-2022-01833
Datensatz-ID: 841576
Beteiligte Länder
Germany
Related:
Journal Article
Uncertainty quantification in hierarchical vehicular flow models
Kinetic and related models : KRM 15(2), 239-256 (2022) [10.3934/krm.2022006]
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Record created 2022-02-16, last modified 2025-12-23
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