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Leveraging the capabilities of physics-informed neural networks for channel optimization in PEM fuel cells

; ; ;

In
Applied energy 409, Seiten/Artikel-Nr.:127478

ImpressumAmsterdam [u.a.] : Elsevier Science

ISSN0306-2619

Online
DOI: 10.1016/j.apenergy.2026.127478


Einrichtungen

  1. Lehrstuhl für Thermodynamik mobiler Energiewandlungssysteme und Institut für Thermodynamik (412310)


Thematische Einordnung (Klassifikation)
DDC: 620


Dokumenttyp
Journal Article

Format
online, print

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-105029451356

Interne Identnummern
RWTH-2026-01765
Datensatz-ID: 1028625

Lizenzstatus der Zeitschrift

 GO

Related:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Software  ;  ;  ;
Python Model PINN Fuel Cell v1.0.1
Zenodo () [10.5281/ZENODO.17143139]  GO BibTeX | EndNote: XML, Text | RIS

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Preprint  ;  ;  ;
Leveraging the Capabilities of Physics-Informed Neural Networks for Channel Optimization in PEM Fuel Cells
[10.2139/ssrn.5500804]  GO BibTeX | EndNote: XML, Text | RIS

Medline ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 10 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
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Document types > Articles > Journal Articles
Faculty of Mechanical Engineering (Fac.4)
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412310
 Record created 2026-02-12, last modified 2026-03-02
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