%0 Journal Article %1 mandler2023feature %A Mandler, Hannes %A Weigand, Bernhard %D 2023 %I Pergamon %J Computers & fluids %K %P 105993 %T Feature importance in neural networks as a means of interpretation for data-driven turbulence models %V 265

@article{mandler2023feature, added-at = {2024-01-23T10:57:34.000+0100}, affiliation = {Mandler, H (Corresponding Author), Univ Stuttgart, Inst Aerosp Thermodynam, Pfaffenwaldring 31, D-70569 Stuttgart, Germany. Mandler, Hannes; Weigand, Bernhard, Univ Stuttgart, Inst Aerosp Thermodynam, Pfaffenwaldring 31, D-70569 Stuttgart, Germany.}, author = {Mandler, Hannes and Weigand, Bernhard}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/212b35b884fa43b759ca1eaeeac8012a4/unibiblio}, interhash = {9b26d70addf342754939409943e1e7cf}, intrahash = {12b35b884fa43b759ca1eaeeac8012a4}, issn = {0045-7930}, journal = {Computers & fluids}, keywords = {}, language = {eng}, pages = 105993, publisher = {Pergamon}, research-areas = {Computer Science; Mechanics}, timestamp = {2024-01-23T09:57:34.000+0100}, title = {Feature importance in neural networks as a means of interpretation for data-driven turbulence models}, unique-id = {WOS:001052698600001}, volume = 265, year = 2023 }