%0 Conference Paper %1 wenzel2022structured %A Wenzel, Tizian %A Kurz, Marius %A Beck, Andrea %A Santin, Gabriele %A Haasdonk, Bernard %B Large-Scale Scientific Computing %C Cham %D 2022 %E Lirkov, Ivan %E Margenov, Svetozar %I Springer International Publishing %K EXC2075 PN1 PN1-1 PN6 PN6-1 selected %P 410--418 %T Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows %X Standard kernel methods for machine learning usually struggle when dealing with large datasets. We review a recently introduced Structured Deep Kernel Network (SDKN) approach that is capable of dealing with high-dimensional and huge datasets - and enjoys typical standard machine learning approximation properties. %@ 978-3-030-97549-4

@inproceedings{wenzel2022structured, abstract = {Standard kernel methods for machine learning usually struggle when dealing with large datasets. We review a recently introduced Structured Deep Kernel Network (SDKN) approach that is capable of dealing with high-dimensional and huge datasets - and enjoys typical standard machine learning approximation properties.}, added-at = {2024-06-21T15:02:15.000+0200}, address = {Cham}, author = {Wenzel, Tizian and Kurz, Marius and Beck, Andrea and Santin, Gabriele and Haasdonk, Bernard}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2c80b6189570dba6e0cb5642f2cd74550/testusersimtech}, booktitle = {Large-Scale Scientific Computing}, editor = {Lirkov, Ivan and Margenov, Svetozar}, interhash = {4d5fe46232a8071066356f28243f3701}, intrahash = {c80b6189570dba6e0cb5642f2cd74550}, isbn = {978-3-030-97549-4}, keywords = {EXC2075 PN1 PN1-1 PN6 PN6-1 selected}, pages = {410--418}, publisher = {Springer International Publishing}, timestamp = {2024-06-21T15:02:15.000+0200}, title = {Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows}, year = 2022 }