Gast ::
Physics-Informed Neural Networks for Predicting Particle Properties in Plasma Spraying
; ;
In
ITSC 2024 : international thermal spray conference and exhibition : conference proceedings and poster sessions of the conference in Milan/Italy on April 29-May 1, 2024 / organizers: DVS - German Welding Society, ASM International - Thermal Spray Society (TSS), DVS Media GmbH, Seiten/Artikel-Nr: 452-458
2024
Buch: Conference abstracts (including proceedings on USB stick)
Online
URL: https://publications.rwth-aachen.de/record/986276/files/986276.pdf
Einrichtungen
- Lehrstuhl und Institut für Oberflächentechnik im Maschinenbau (419010)
- Internet of Production (080067)
Projekte
- WS-B2.II - Discontinuous Production (X080067-WS-B2.II) (X080067-WS-B2.II)
- DFG project 390621612 - EXC 2023: Internet of Production (IoP) (390621612) (390621612)
Inhaltliche Beschreibung (Schlagwörter)
Atmospheric plasma spraying (APS) (frei) ; artificial neural networks (ANNs) (frei) ; computational fluid dynamics (CFD) (frei) ; grey box model (frei) ; machine learning (ML) (frei) ; physics-informed neural networks (PINNs) (frei)
Restricted: 
PDF
Dokumenttyp
Contribution to a book/Contribution to a conference proceedings
Format
media combination
Sprache
English
Anmerkung
No peer reviewed article
Interne Identnummern
RWTH-2024-05122
Datensatz-ID: 986276
Beteiligte Länder
Germany
Related:
Journal Article
Physics-Informed Neural Networks for Predicting Particle Properties in Plasma Spraying
Journal of thermal spray technology : JTST 34(2/3), 885-892 (2025) [10.1007/s11666-025-01965-x]
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