Jochen Garcke
Cited by
Cited by
Explainable machine learning for scientific insights and discoveries
R Roscher, B Bohn, MF Duarte, J Garcke
Ieee Access 8, 42200-42216, 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
Sparse grids in a nutshell
J Garcke
Sparse grids and applications, 57-80, 2013
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
Data mining with sparse grids
J Garcke, M Griebel, M Thess
Computing 67, 225-253, 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
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
The combination technique and some generalisations
M Hegland, J Garcke, V Challis
Linear Algebra and its Applications 420 (2-3), 249-275, 2007
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
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
Importance weighted inductive transfer learning for regression
J Garcke, T Vanck
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2014
Sparse grids and applications
J Garcke, M Griebel
Springer Science & Business Media, 2012
Classification with sparse grids using simplicial basis functions
J Garcke, M Griebel
Intelligent data analysis 6 (6), 483-502, 2002
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
Maschinelles Lernen durch Funktionsrekonstruktion mit verallgemeinerten dünnen Gittern
J Garcke
Universitäts-und Landesbibliothek Bonn, 2004
Regression with the optimised combination technique
J Garcke
Proceedings of the 23rd international conference on Machine learning, 321-328, 2006
Approximating Gaussian Processes with H^2-Matrices
S Börm, J Garcke
European Conference on Machine Learning, 42-53, 2007
A dimension adaptive sparse grid combination technique for machine learning
J Garcke
Anziam Journal 48, C725-C740, 2006
Fitting multidimensional data using gradient penalties and the sparse grid combination technique
J Garcke, M Hegland
Computing 84, 1-25, 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
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