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%0 Journal Article
%1 Uhrich2024
%A Uhrich, Benjamin
%A Pfeifer, Nils
%A Schäfer, Martin
%A Theile, Oliver
%A Rahm, Erhard
%D 2024
%I Springer
%J Applied Intelligence
%K topic_physchemistry area_bigdata ep imported
%N 6
%P 4736--4755
%R 10.1007/S10489-024-05402-4
%T Physics-informed deep learning to quantify anomalies for real-time fault mitigation in 3D printing
%V 54
@article{Uhrich2024,
added-at = {2024-11-28T13:27:37.000+0100},
author = {Uhrich, Benjamin and Pfeifer, Nils and Sch{\"a}fer, Martin and Theile, Oliver and Rahm, Erhard},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/281b1bf132ebf40360faba0d8925a59f1/scadsfct},
doi = {10.1007/S10489-024-05402-4},
interhash = {dfe02ea2f57817c19958786f88d13e42},
intrahash = {81b1bf132ebf40360faba0d8925a59f1},
journal = {Applied Intelligence},
keywords = {topic_physchemistry area_bigdata ep imported},
number = 6,
owner = {ericpeukert},
pages = {4736--4755},
publisher = {Springer},
timestamp = {2024-11-28T17:41:37.000+0100},
title = {Physics-informed deep learning to quantify anomalies for real-time fault mitigation in 3D printing},
volume = 54,
year = 2024
}
