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Leveraging the capabilities of physics-informed neural networks for channel optimization in PEM fuel cells
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In
Applied energy 409, Seiten/Artikel-Nr.:127478
2026
Online
DOI: 10.1016/j.apenergy.2026.127478
DOI: 10.18154/RWTH-2026-01765
URL: https://publications.rwth-aachen.de/record/1028625/files/1028625.pdf
Einrichtungen
Projekte
Thematische Einordnung (Klassifikation)
DDC: 620
OpenAccess: 
PDF
Dokumenttyp
Journal Article
Format
online, print
Sprache
English
Anmerkung
Peer reviewed article
Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-105029451356
WOS Core Collection: WOS:001686912900001
Interne Identnummern
RWTH-2026-01765
Datensatz-ID: 1028625
Beteiligte Länder
Germany
Related:
Software
Python Model PINN Fuel Cell v1.0.1
Zenodo (2025) [10.5281/ZENODO.17143139]
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RIS
Preprint
Leveraging the Capabilities of Physics-Informed Neural Networks for Channel Optimization in PEM Fuel Cells
[10.2139/ssrn.5500804]
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; 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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The record appears in these collections: |
| Typ | Amount | VAT | Currency | Share | Status | Cost centre |
| Hybrid-OA | 2652.00 | 185.64 | EUR | 96.82 % | (DEAL) | 021000-412310 |
| Other | 87.00 | 6.09 | EUR | 3.18 % | (DEAL) | 021000-412310 |
| Sum | 2739.00 | 191.73 | EUR | |||
| Total | 2930.73 |
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