%0 Journal Article %1 bohringer2023strategy %A Böhringer, Pauline %A Sommer, Daniel %A Haase, Thomas %A Barteczko, Martin %A Sprave, Joachim %A Stoll, Markus %A Karadogan, Celalettin %A Koch, David %A Middendorf, Peter %A Liewald, Mathias %D 2023 %I Elsevier %J Computer methods in applied mechanics and engineering %K %P 115894 %R 10.1016/j.cma.2023.115894 %T A strategy to train machine learning material models for finite element simulations on data acquirable from physical experiments %V 406

@article{bohringer2023strategy, added-at = {2023-09-19T16:08:03.000+0200}, affiliation = {Sommer, D (Corresponding Author), Univ Stuttgart, Inst Aircraft Design, Stuttgart, Germany. Boehringer, Pauline; Sprave, Joachim, Mercedes Benz AG, Res & Dev, Sindelfingen, Germany. Sommer, Daniel; Barteczko, Martin; Middendorf, Peter, Univ Stuttgart, Inst Aircraft Design, Stuttgart, Germany. Haase, Thomas, Ernst Mach Inst, Fraunhofer Inst High Speed Dynam, Freiburg, Germany. Stoll, Markus, Renumics GmbH, Karlsruhe, Germany. Karadogan, Celalettin; Liewald, Mathias, Univ Stuttgart, Inst Met Forming Technol, Stuttgart, Germany. Koch, David, Dynamore GmbH, Stuttgart, Germany.}, author = {Böhringer, Pauline and Sommer, Daniel and Haase, Thomas and Barteczko, Martin and Sprave, Joachim and Stoll, Markus and Karadogan, Celalettin and Koch, David and Middendorf, Peter and Liewald, Mathias}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/243aaf587f525e1629b3a0a4af1142668/unibiblio}, doi = {10.1016/j.cma.2023.115894}, interhash = {8bde96727e5eb34e53c1b49edf301286}, intrahash = {43aaf587f525e1629b3a0a4af1142668}, issn = {{0045-7825} and {1879-2138}}, journal = {Computer methods in applied mechanics and engineering}, keywords = {}, language = {eng}, pages = 115894, publisher = {Elsevier}, research-areas = {Engineering; Mathematics; Mechanics}, timestamp = {2023-09-19T14:08:03.000+0200}, title = {A strategy to train machine learning material models for finite element simulations on data acquirable from physical experiments}, unique-id = {WOS:000922492200001}, volume = 406, year = 2023 }