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1 Ergebnisse
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Physics-informed deep learning to quantify anomalies for re..:
Uhrich, Benjamin
;
Pfeifer, Nils
;
Schäfer, Martin
..
Applied Intelligence. 54 (2024) 6 - p. 4736-4755 , 2024
Link:
https://doi.org/10.1007/s10489-024-05402-4
RT Journal T1
Physics-informed deep learning to quantify anomalies for real-time fault mitigation in 3D printing
UL https://suche.suub.uni-bremen.de/peid=cr-10.1007_s10489-024-05402-4&Exemplar=1&LAN=DE A1 Uhrich, Benjamin A1 Pfeifer, Nils A1 Schäfer, Martin A1 Theile, Oliver A1 Rahm, Erhard PB Springer Science and Business Media LLC YR 2024 SN 0924-669X SN 1573-7497 JF Applied Intelligence VO 54 IS 6 SP 4736 OP 4755 LK http://dx.doi.org/https://doi.org/10.1007/s10489-024-05402-4 DO https://doi.org/10.1007/s10489-024-05402-4 SF ELIB - SuUB Bremen
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