| Explainable machine learning for scientific insights and discoveries R Roscher, B Bohn, MF Duarte, J Garcke Ieee Access 8, 42200-42216, 2020 | 302 | 2020 |
| Informed Machine Learning--A Taxonomy and Survey of Integrating Knowledge into Learning Systems L von Rueden, S Mayer, K Beckh, B Georgiev, S Giesselbach, R Heese, ... arXiv preprint arXiv:1903.12394, 2019 | 186* | 2019 |
| Data mining with sparse grids J Garcke, M Griebel, M Thess Computing 67 (3), 225-253, 2001 | 176 | 2001 |
| Sparse grids in a nutshell J Garcke Sparse grids and applications, 57-80, 2012 | 164* | 2012 |
| 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 | 117 | 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 (3), 575-605, 2013 | 108 | 2013 |
| The combination technique and some generalisations M Hegland, J Garcke, V Challis Linear Algebra and its Applications 420 (2-3), 249-275, 2007 | 108 | 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 | 85 | 2000 |
| Sparse grids and applications J Garcke, M Griebel Springer Science & Business Media, 2012 | 66 | 2012 |
| 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 | 63 | 2013 |
| Importance weighted inductive transfer learning for regression J Garcke, T Vanck Joint European conference on machine learning and knowledge discovery in …, 2014 | 57 | 2014 |
| Maschinelles Lernen durch Funktionsrekonstruktion mit verallgemeinerten dünnen Gittern J Garcke Universitäts-und Landesbibliothek Bonn, 2004 | 57 | 2004 |
| Classification with sparse grids using simplicial basis functions J Garcke, M Griebel Intelligent data analysis 6 (6), 483-502, 2002 | 50 | 2002 |
| Regression with the optimised combination technique J Garcke Proceedings of the 23rd international conference on Machine learning, 321-328, 2006 | 47 | 2006 |
| A dimension adaptive sparse grid combination technique for machine learning J Garcke Anziam Journal 48, C725-C740, 2006 | 45 | 2006 |
| Suboptimal feedback control of PDEs by solving HJB equations on adaptive sparse grids J Garcke, A Kröner Journal of Scientific Computing 70 (1), 1-28, 2017 | 42 | 2017 |
| Fitting multidimensional data using gradient penalties and the sparse grid combination technique J Garcke, M Hegland Computing 84 (1), 1-25, 2009 | 40 | 2009 |
| Approximating Gaussian Processes with H^2-Matrices S Börm, J Garcke European Conference on Machine Learning, 42-53, 2007 | 35 | 2007 |
| 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 International Symposium on Intelligent Data Analysis, 548-560, 2020 | 34 | 2020 |
| 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 | 31 | 2010 |
