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"Physics-informed deep learning to quantify anomalies for real-time fault ..."

Benjamin Uhrich et al. (2024)

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DOI: 10.1007/S10489-024-05402-4

access: open

type: Journal Article

metadata version: 2024-06-08

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  - journals/apin/UhrichPSTR24
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  - https://dblp.org/rec/journals/apin/UhrichPSTR24
Benjamin Uhrich, Nils Pfeifer, Martin Schäfer, Oliver Theile, Erhard Rahm:
Physics-informed deep learning to quantify anomalies for real-time fault mitigation in 3D printing. Appl. Intell. 54(6): 4736-4755 (2024)

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