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A Machine Learning Enabled Image‐data‐driven End‐to‐end Mechanical Field Predictor For Dual‐Phase Steel
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In
Special Issue: 92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM) / Issue Edited by: Ch. Böhm, K. Mang, B. Markert, S. Reese, M. Schmidtchen, J. Waimann, M. Kaliske
In
Proceedings in applied mathematics and mechanics : PAMM 22(1), Seiten/Artikel-Nr.:e202200110
2023
Online
DOI: 10.18154/RWTH-2023-03269
DOI: 10.1002/pamm.202200110
URL: https://publications.rwth-aachen.de/record/954586/files/954586.pdf
Einrichtungen
- Lehrstuhl für Werkstoffphysik und Institut für Metallkunde und Materialphysik (523110)
- Fachgruppe für Materialwissenschaft und Werkstofftechnik (520000)
Thematische Einordnung (Klassifikation)
DDC: 510
OpenAccess: 
PDF
Dokumenttyp
Journal Article/Contribution to a conference proceedings
Format
online
Sprache
English
Anmerkung
Peer reviewed article
Interne Identnummern
RWTH-2023-03269
Datensatz-ID: 954586
Beteiligte Länder
Germany
Related:
Dissertation / PhD Thesis
Investigation of damage sites in dual phase microstructures by artificial intelligence
Aachen : RWTH Aachen University 1 Online-Ressource : Illustrationen (2024) [10.18154/RWTH-2024-06967] = Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2024, Kumulative Dissertation
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Proceedings
Special Issue: 92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM)
92. Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2022, AachenAachen, Germany, 15 Aug 2022 - 19 Aug 2022
Proceedings in applied mathematics and mechanics : PAMM 22(1), (2023)
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