%0 Journal Article %1 cheng2019multilevel %A Cheng, Kai %A Lu, Zhenzhou %A Zhen, Ying %D 2019 %I Elsevier %J Computer methods in applied mechanics and engineering %K %P 360-377 %R 10.1016/j.cma.2019.02.021 %T Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression %V 349

@article{cheng2019multilevel, added-at = {2023-09-01T10:39:04.000+0200}, author = {Cheng, Kai and Lu, Zhenzhou and Zhen, Ying}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/29d8b02fb0afe2c95836ca6f94f6b4493/unibiblio-4}, doi = {10.1016/j.cma.2019.02.021}, interhash = {da7454c787d737d28be893a4f278ce4c}, intrahash = {9d8b02fb0afe2c95836ca6f94f6b4493}, issn = {{0045-7825} and {0374-2830}}, journal = {Computer methods in applied mechanics and engineering}, keywords = {}, language = {eng}, pages = {360-377}, publisher = {Elsevier}, timestamp = {2023-09-01T08:39:04.000+0200}, title = {Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression}, volume = 349, year = 2019 }