| Explainable machine learning for scientific insights and discoveries R Roscher, B Bohn, MF Duarte, J Garcke Ieee Access 8, 42200-42216, 2020 | 491 | 2020 |
| Informed Machine Learning–A taxonomy and survey of integrating prior knowledge into learning systems L Von Rueden, S Mayer, K Beckh, B Georgiev, S Giesselbach, R Heese, ... IEEE Transactions on Knowledge and Data Engineering 35 (1), 614-633, 2021 | 331* | 2021 |
| Sparse grids in a nutshell J Garcke Sparse grids and applications, 57-80, 2013 | 179* | 2013 |
| Data mining with sparse grids J Garcke, M Griebel, M Thess Computing 67, 225-253, 2001 | 179 | 2001 |
| Multivariate regression and machine learning with sums of separable functions G Beylkin, J Garcke, MJ Mohlenkamp SIAM Journal on Scientific Computing 31 (3), 1840-1857, 2009 | 128 | 2009 |
| An adaptive sparse grid semi-Lagrangian scheme for first order Hamilton-Jacobi Bellman equations O Bokanowski, J Garcke, M Griebel, I Klompmaker Journal of Scientific Computing 55, 575-605, 2013 | 123 | 2013 |
| The combination technique and some generalisations M Hegland, J Garcke, V Challis Linear Algebra and its Applications 420 (2-3), 249-275, 2007 | 110 | 2007 |
| On the computation of the eigenproblems of hydrogen and helium in strong magnetic and electric fields with the sparse grid combination technique J Garcke, M Griebel Journal of Computational Physics 165 (2), 694-716, 2000 | 83 | 2000 |
| Analysis of car crash simulation data with nonlinear machine learning methods B Bohn, J Garcke, R Iza-Teran, A Paprotny, B Peherstorfer, ... Procedia Computer Science 18, 621-630, 2013 | 72 | 2013 |
| Sparse Grids and Applications-Munich 2012 J Garcke, D Pflüger Springer Berlin Heidelberg, 2014 | 69 | 2014 |
| Importance weighted inductive transfer learning for regression J Garcke, T Vanck Machine Learning and Knowledge Discovery in Databases: European Conference …, 2014 | 64 | 2014 |
| Classification with sparse grids using simplicial basis functions J Garcke, M Griebel Intelligent data analysis 6 (6), 483-502, 2002 | 61 | 2002 |
| Maschinelles Lernen durch Funktionsrekonstruktion mit verallgemeinerten dünnen Gittern J Garcke Universitäts-und Landesbibliothek Bonn, 2004 | 59 | 2004 |
| Combining machine learning and simulation to a hybrid modelling approach: Current and future directions L von Rueden, S Mayer, R Sifa, C Bauckhage, J Garcke Advances in Intelligent Data Analysis XVIII: 18th International Symposium on …, 2020 | 54 | 2020 |
| Suboptimal feedback control of PDEs by solving HJB equations on adaptive sparse grids J Garcke, A Kröner Journal of Scientific Computing 70, 1-28, 2017 | 52 | 2017 |
| Regression with the optimised combination technique J Garcke Proceedings of the 23rd international conference on Machine learning, 321-328, 2006 | 48 | 2006 |
| A dimension adaptive sparse grid combination technique for machine learning J Garcke Anziam Journal 48, C725-C740, 2006 | 47 | 2006 |
| Approximating Gaussian Processes with H^2-Matrices S Börm, J Garcke European Conference on Machine Learning, 42-53, 2007 | 42* | 2007 |
| Fitting multidimensional data using gradient penalties and the sparse grid combination technique J Garcke, M Hegland | 41 | 2009 |
| On the numerical solution of the chemical master equation with sums of rank one tensors M Hegland, J Garcke ANZIAM Journal 52, C628-C643, 2010 | 34 | 2010 |
