%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 ians myown peerreviewed pn6 pn6-1 %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 = {2022-03-28T15:07:11.000+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/243a04ad795d27a498a944a5fa0492d71/tizianwenzel}, booktitle = {Large-Scale Scientific Computing}, editor = {Lirkov, Ivan and Margenov, Svetozar}, interhash = {4d5fe46232a8071066356f28243f3701}, intrahash = {43a04ad795d27a498a944a5fa0492d71}, isbn = {978-3-030-97549-4}, keywords = {exc2075 ians myown peerreviewed pn6 pn6-1}, pages = {410--418}, publisher = {Springer International Publishing}, timestamp = {2022-04-22T12:48:38.000+0200}, title = {Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows}, year = 2022 }