%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 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 = {area_bigdata ep imported}, number = 6, owner = {ericpeukert}, pages = {4736--4755}, publisher = {Springer}, timestamp = {2024-11-28T13:27:37.000+0100}, title = {Physics-informed deep learning to quantify anomalies for real-time fault mitigation in 3D printing}, volume = 54, year = 2024 }