guest ::
A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains : comparison with finite element method
; ; ; ;
2022
Online
DOI: 10.48550/arXiv.2206.13103
URL: https://publications.rwth-aachen.de/record/850542/files/850542.pdf
Einrichtungen
Inhaltliche Beschreibung (Schlagwörter)
Computational Engineering, Finance, and Science (cs.CE) (Genormte SW) ; FOS: Computer and information sciences (Genormte SW) ; Machine Learning (cs.LG) (Genormte SW)
Restricted: 
PDF
External link: 
Fulltext by arXiv.org
Dokumenttyp
Preprint
Format
online
Sprache
English
Externe Identnummern
arXiv: arXiv:2206.13103
Interne Identnummern
RWTH-2022-07558
Datensatz-ID: 850542
Beteiligte Länder
Germany
Related:
Journal Article
A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: Comparison with finite element method
Computer methods in applied mechanics and engineering 401(Part B), 115616 (2022) [10.1016/j.cma.2022.115616]
BibTeX |
EndNote:
XML,
Text |
RIS
; Free to read
|
The record appears in these collections: |