Data mining with sparse grids using simplicial basis functions. - Jochen Garcke; Michael Griebel (KDD, 2001)

On the Parallelization of the Sparse Grid Approach for Data Mining. - Jochen Garcke; Michael Griebel (LSSC, 2001)

Data Mining with Sparse Grids. - Jochen Garcke; Michael Griebel; M. Thess (Computing, 2001)

Classification with sparse grids using simplicial basis functions. - Jochen Garcke; Michael Griebel (Intell. Data Anal., 2002)

Parallelisation of Sparse Grids for Large Scale Data Analysis. - Jochen Garcke; Markus Hegland; Ole Møller Nielsen (International Conference on Computational Science, 2003)

Maschinelles Lernen durch Funktionsrekonstruktion mit verallgemeinerten dünnen Gittern. - Jochen Garcke (2004)

Fitting Multidimensional Data Using Gradient Penalties and Combination Techniques. - Jochen Garcke; Markus Hegland (HPSC, 2006)

Regression with the optimised combination technique. - Jochen Garcke (ICML, 2006)

Approximating Gaussian Processes with H2-Matrices. - Steffen Börm; Jochen Garcke (ECML, 2007)

A Dimension Adaptive Combination Technique Using Localised Adaptation Criteria. - Jochen Garcke (HPSC, 2009)

Fitting multidimensional data using gradient penalties and the sparse grid combination technique. - Jochen Garcke; Markus Hegland (Computing, 2009)

Multivariate Regression and Machine Learning with Sums of Separable Functions. - Gregory Beylkin; Jochen Garcke; Martin J. Mohlenkamp (SIAM J. Sci. Comput., 2009)

Classification with Sums of Separable Functions. - Jochen Garcke (ECML/PKDD (1), 2010)

On a Connection between Maximum Variance Unfolding, Shortest Path Problems and IsoMap. - Alexander Paprotny; Jochen Garcke (AISTATS, 2012)

Using Hyperbolic Cross Approximation to measure and compensate Covariate Shift. - Thomas Vanck; Jochen Garcke (ACML, 2013)

Analysis of Car Crash Simulation Data with Nonlinear Machine Learning Methods. - Bastian Bohn; Jochen Garcke; Rodrigo Iza-Teran; Alexander Paprotny; Benjamin Peherstorfer; Ulf Schepsmeier; Clemens-August Thole (ICCS, 2013)

An Adaptive Sparse Grid Semi-Lagrangian Scheme for First Order Hamilton-Jacobi Bellman Equations. - Olivier Bokanowski; Jochen Garcke; Michael Griebel; Irene Klompmaker (J. Sci. Comput., 2013)

Data Analytics for Simulation Repositories in Industry. - Rodrigo Iza-Teran; Jochen Garcke (GI-Jahrestagung, 2014)

Importance Weighted Inductive Transfer Learning for Regression. - Jochen Garcke; Thomas Vanck (ECML/PKDD (1), 2014)

VAVID - Handling Big Data. - Jochen Garcke (ERCIM News, 2015)

Multicore Processors and Graphics Processing Unit Accelerators for Parallel Retrieval of Aerosol Optical Depth From Satellite Data: Implementation, Performance, and Energy Efficiency. - Jia Liu 021; Dustin Feld; Yong Xue; Jochen Garcke; Thomas Soddemann (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2015)

An efficient geosciences workflow on multi-core processors and GPUs: a case study for aerosol optical depth retrieval from MODIS satellite data. - Jia Liu 021; Dustin Feld; Yong Xue; Jochen Garcke; Thomas Soddemann; Peiyuan Pan (Int. J. Digit. Earth, 2016)

Advancing a Gateway Infrastructure for Wind Turbine Data Analysis. - Alvaro Aguilera; Richard Grunzke; Dirk Habich; Johannes Luong; Dirk Schollbach; Ulf Markwardt; Jochen Garcke (J. Grid Comput., 2016)

A sparse grid based method for generative dimensionality reduction of high-dimensional data. - Bastian Bohn; Jochen Garcke; Michael Griebel (J. Comput. Phys., 2016)

Energy-Efficiency and Performance Comparison of Aerosol Optical Depth Retrieval on Distributed Embedded SoC Architectures. - Dustin Feld; Jochen Garcke; Jia Liu 021; Eric Schricker; Thomas Soddemann; Yong Xue (Scientific Computing and Algorithms in Industrial Simulations, 2017)

Dimensionality Reduction for the Analysis of Time Series Data from Wind Turbines. - Jochen Garcke; Rodrigo Iza-Teran; Marvin Marks; Mandar Pathare; Dirk Schollbach; Martin Stettner (Scientific Computing and Algorithms in Industrial Simulations, 2017)

ModelCompare. - Jochen Garcke; Mandar Pathare; Nikhil Prabakaran (Scientific Computing and Algorithms in Industrial Simulations, 2017)

Suboptimal Feedback Control of PDEs by Solving HJB Equations on Adaptive Sparse Grids. - Jochen Garcke; Axel Kröner (J. Sci. Comput., 2017)

Event-Triggered Learning for Resource-Efficient Networked Control. - Friedrich Solowjow; Dominik Baumann; Jochen Garcke; Sebastian Trimpe (ACC, 2018)

Informed Machine Learning - Towards a Taxonomy of Explicit Integration of Knowledge into Machine Learning. - Laura von Rüden; Sebastian Mayer; Jochen Garcke; Christian Bauckhage; Jannis Schücker (CoRR, 2019)

A Geometrical Method for Low-Dimensional Representations of Simulations. - Rodrigo Iza-Teran; Jochen Garcke (SIAM/ASA J. Uncertain. Quantification, 2019)

A Compact Spectral Descriptor for Shape Deformations. - Skylar Sible; Rodrigo Iza-Teran; Jochen Garcke; Nikola Aulig; Patricia Wollstadt (ECAI, 2020)

Analysis and Prediction of Deforming 3D Shapes Using Oriented Bounding Boxes and LSTM Autoencoders. - Sara Hahner; Rodrigo Iza-Teran; Jochen Garcke (ICANN (1), 2020)

Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions. - Laura von Rüden; Sebastian Mayer; Rafet Sifa; Christian Bauckhage; Jochen Garcke (IDA, 2020)

Explainable Machine Learning for Scientific Insights and Discoveries. - Ribana Roscher; Bastian Bohn; Marco F. Duarte; Jochen Garcke (IEEE Access, 2020)

Adaptive wavelet pooling for convolutional neural networks. - Moritz Wolter; Jochen Garcke (AISTATS, 2021)

Decision Support by Interpretable Machine Learning in Acoustic Emission Based Cutting Tool Wear Prediction. - Arno Schmetz; Christopher Vahl; Zhen Zhen; Daniel Reibert; Sebastian Mayer; Daniel Zontar; Jochen Garcke; Christian Brecher (IEEM, 2021)

R-SARL: Crowd-aware Navigation Based Deep Reinforcement Learning for Nonholonomic Robot in Complex Environments. - Yanying Zhou; Shijie Li; Jochen Garcke (CoRR, 2021)

Wavelet-Packet Powered Deepfake Image Detection. - Moritz Wolter; Felix Blanke; Charles Tapley Hoyt; Jochen Garcke (CoRR, 2021)

The pole balancing problem from the viewpoint of system flexibility. - Léo Françoso Dal Piccol Sotto; Sebastian Mayer; Jochen Garcke (GECCO Companion, 2022)

Graph Modeling in Computer Assisted Automotive Development. - Anahita Pakiman; Jochen Garcke (ICKG, 2022)

Canonical convolutional neural networks. - Lokesh Veeramacheneni; Moritz Wolter; Reinhard Klein; Jochen Garcke (IJCNN, 2022)

Transfer Learning Using Spectral Convolutional Autoencoders on Semi-Regular Surface Meshes. - Sara Hahner; Felix Kerkhoff; Jochen Garcke (LoG, 2022)

Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes. - Sara Hahner; Jochen Garcke (WACV, 2022)

Wavelet-packets for deepfake image analysis and detection. - Moritz Wolter; Felix Blanke; Raoul Heese; Jochen Garcke (Mach. Learn., 2022)

Overcoming Deceptive Rewards with Quality-Diversity. - Arno Feiden; Jochen Garcke (GECCO Companion, 2023)

Graph Extraction for Assisting Crash Simulation Data Analysis. - Anahita Pakiman; Jochen Garcke; Axel Schumacher (ICCS, 2023)

How Does Knowledge Injection Help in Informed Machine Learning? - Laura von Rüden; Jochen Garcke; Christian Bauckhage (IJCNN, 2023)

The Elements of Flexibility for Task-Performing Systems. - Sebastian Mayer; Léo Françoso Dal Piccol Sotto; Jochen Garcke (IEEE Access, 2023)

Evolutionary Solution Adaption for Multi-Objective Metal Cutting Process Optimization. - Léo Françoso Dal Piccol Sotto; Sebastian Mayer; Hemanth Janarthanam; Alexander Butz; Jochen Garcke (CoRR, 2023)

On the Interplay of Subset Selection and Informed Graph Neural Networks. - Niklas Breustedt; Paolo Climaco; Jochen Garcke; Jan Hamaekers; Gitta Kutyniok; Dirk A. Lorenz; Rick Oerder; Chirag Varun Shukla (CoRR, 2023)

Investigating minimizing the training set fill distance in machine learning regression. - Paolo Climaco; Jochen Garcke (CoRR, 2023)

Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems. - Laura von Rüden; Sebastian Mayer; Katharina Beckh; Bogdan Georgiev; Sven Giesselbach; Raoul Heese; Birgit Kirsch; Julius Pfrommer; Annika Pick; Rajkumar Ramamurthy; Michal Walczak; Jochen Garcke; et al. (IEEE Trans. Knowl. Data Eng., 2023)

Explainable Machine Learning. - Jochen Garcke; Ribana Roscher (Mach. Learn. Knowl. Extr., 2023)

Knowledge discovery assistants for crash simulations with graph algorithms and energy absorption features. - Anahita Pakiman; Jochen Garcke; Axel Schumacher (Appl. Intell., 2023)

Unsupervised Representation Learning for Diverse Deformable Shape Collections. - Sara Hahner; Souhaib Attaiki; Jochen Garcke; Maks Ovsjanikov (3DV, 2024)

Generalizing Diversity with the Signature Transform. - Arno Feiden; Biagio Paparella; Jochen Garcke (GECCO Companion, 2024)

Learning Crowd Behaviors in Navigation with Attention-based Spatial-Temporal Graphs. - Yanying Zhou; Jochen Garcke (ICRA, 2024)

ptwt - The PyTorch Wavelet Toolbox. - Moritz Wolter; Felix Blanke; Jochen Garcke; Charles Tapley Hoyt (J. Mach. Learn. Res., 2024)

Diversity in Reinforcement Learning Through the Occupancy Measure. - Arno Feiden; Jochen Garcke (GECCO, 2025)

A modified SimRank++ approach for searching crash simulation data. - Anahita Pakiman; Jochen Garcke; Axel Schumacher (Appl. Intell., 2025)