Research Group of Prof. Dr. Jochen Garcke

Address:
Institut für Numerische Simulation
Friedrich-Hirzebruch-Allee 7
53115 Bonn
Phone: +49 228 73-69838
Office: FHA7 3.035
E-Mail: ed tod nnob-inu tod sni ta ekcraga tod b@foo tod de

Fraunhofer SCAI

Siehe auch die Seiten der Abteilung Numerische datenbasierte Vorhersage bei Fraunhofer SCAI, sowie über die Zusammenarbeit zwischen INS und Fraunhofer SCAI.

Informationen für Studierende

Bei Interesse an anwendungsorientierten Aufgabenstellungen und angewandter Mathematik sind regelmäßig Praktika, SHK-Stellen sowie Bachelor- und Masterarbeiten in meiner Arbeitsgruppe zu vergeben. Falls Sie sich konkreter interessieren und bewerben möchten, bräuchte ich (mindestens) einen aktuellen Notenauszug, einen kurzen Lebenslauf und den Stand Ihrer Programmiererfahrung.

Typische Themen von Abschlussarbeiten sind in der Numerik von hochdimensionalen Problemen oder im Maschinellen Lernen. In Zusammenarbeit mit dem Fraunhofer SCAI können im Rahmen einer Abschlussarbeit oft auch Anwendungaspekte, wie z.B. virtuelle Produktentwicklung, Sensordatenanalyse, oder Energienetze (auch im Kontext von erneuerbaren Energien), mit betrachtet werden.

Teaching

Summer semester 2024

Summer semester 2023

See teaching activities of the whole group.

Publications

Books

  1. Algorithmic Mathematics in Machine Learning. B. Bohn, J. Garcke, and M. Griebel. Data Science. SIAM, Philadelphia, PA, USA, 2024. BibTeX DOI

Journal papers

  1. In-situ estimation of time-averaging uncertainties in turbulent flow simulations. S. Rezaeiravesh, C. Gscheidle, A. Peplinski, J. Garcke, and P. Schlatter. Computer Methods in Applied Mechanics and Engineering, 433:117511, 2025. BibTeX PDF DOI arXiv
  2. On minimizing the training set fill distance in machine learning regression. P. Climaco and J. Garcke. Journal of Data-centric Machine Learning Research, 2024. BibTeX PDF arXiv DMLR
  3. Alignment of highly resolved time-dependent experimental and simulated crash test data. J. Garcke, S. Hahner, and R. Iza-Teran. International Journal of Crashworthiness, 29(1):1–15, 2024. also available as INS Preprint No. 2208. BibTeX PDF DOI
  4. A modified SimRank++ approach for searching crash simulation data. A. Pakiman, J. Garcke, and A. Schumacher. Applied Intelligence, 2024. accepted. BibTeX PDF
  5. ptwt - The PyTorch wavelet toolbox. M. Wolter, F. Blanke, J. Garcke, and C. T. Hoyt. Journal of Machine Learning Research, 25(80):1–7, 2024. BibTeX PDF JMLR
  6. Multi-resolution Dynamic Mode Decomposition for damage detection in wind turbine gearboxes. P. Climaco, J. Garcke, and R. Iza-Teran. Data-Centric Engineering, 4:e1, 2023. preprint available as INS Preprint 2103. BibTeX DOI arXiv
  7. Method for automated detection of outliers in crash simulations. D. Kracker, R. Dhanasekaran, A. Schumacher, and J. Garcke. International Journal of Crashworthiness, 28(1):96–107, 2023. BibTeX PDF DOI
  8. The elements of flexibility for task-performing systems. S. Mayer, L. F. D. P. Sotto, and J. Garcke. IEEE Access, 11:8029–8056, 2023. BibTeX DOI arXiv
  9. Knowledge discovery assistants for crash simulations with graph algorithms and energy absorption features. A. Pakiman, J. Garcke, and A. Schumacher. Applied Intelligence, 53:19217–19236, 2023. earlier version under title "Crash Simulation Exploration with Energy Absorption Features and Graph Algorithms" available as V1, also available as INS Preprint No. 2207. BibTeX PDF V1 DOI
  10. Causal deep learning models for studying the earth system. T. Tesch, S. Kollet, and J. Garcke. Geoscientific Model Development, 16(8):2149–2166, 2023. BibTeX DOI
  11. 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, B. Kirsch, J. Pfrommer, A. Pick, R. Ramamurthy, M. Walczak, J. Garcke, C. Bauckhage, and J. Schuecker. IEEE Transactions on Knowledge and Data Engineering, 35(1):614–633, 2023. BibTeX PDF DOI arXiv
  12. Wavelet-packets for deepfake image analysis and detection. M. Wolter, F. Blanke, R. Heese, and J. Garcke. Machine Learning, 111:4295–4327, 2022. BibTeX DOI arXiv
  13. Variant approach for identifying spurious relations that deep learning models learn. T. Tesch, S. Kollet, and J. Garcke. Frontiers in Water, 3:114, 2021. BibTeX PDF DOI
  14. Simplex stochastic collocation for piecewise smooth functions with kinks. B. Fuchs and J. Garcke. International Journal for Uncertainty Quantification, 10(1):1–24, 2020. BibTeX PDF DOI arXiv
  15. Explainable Machine Learning for Scientific Insights and Discoveries. R. Roscher, B. Bohn, M. F. Duarte, and J. Garcke. IEEE Access, 8(1):42200–42216, 2020. BibTeX DOI arXiv
  16. A geometrical method for low-dimensional representations of simulations. R. Iza-Teran and J. Garcke. SIAM/ASA Journal on Uncertainty Quantification, 7(2):472–496, 2019. BibTeX PDF DOI arXiv
  17. Suboptimal Feedback Control of PDEs by Solving HJB Equations on Adaptive Sparse Grids. J. Garcke and A. Kröner. Journal of Scientific Computing, 70(1):1–28, 2017. also available as INS Preprint No. 1518. BibTeX PDF DOI Link
  18. Advancing a Gateway Infrastructure for Wind Turbine Data Analysis. A. Aguilera, R. Grunzke, D. Habich, J. Luong, D. Schollbach, U. Markwardt, and J. Garcke. Journal of Grid Computing, 14(4):499–514, 2016. BibTeX PDF DOI
  19. A sparse grid based method for generative dimensionality reduction of high-dimensional data. B. Bohn, J. Garcke, and M. Griebel. Journal of Computational Physics, 309():1 – 17, 2016. earlier version available as INS Preprint No. 1514. BibTeX PDF DOI
  20. An efficient geosciences workflow on multi-core processors and GPUs: a case study for aerosol optical depth retrieval from MODIS satellite data. J. Liu, D. Feld, Y. Xue, J. Garcke, T. Soddemann, and P. Pan. International Journal of Digital Earth, 9(8):748–765, 2016. BibTeX DOI
  21. Multi-core processors and graphics processing unit accelerators for parallel retrieval of aerosol optical depth from satellite data: implementation, performance and energy efficiency. J. Liu, D. Feld, Y. Xue, J. Garcke, and T. Soddemann. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5):2306–2317, 2015. BibTeX DOI
  22. An adaptive sparse grid semi-Lagrangian scheme for first order Hamilton-Jacobi Bellman equations. O. Bokanowski, J. Garcke, M. Griebel, and I. Klompmaker. Journal of Scientific Computing, 55(3):575–605, 2013. also available as INS Preprint No. 1207. BibTeX PDF DOI
  23. Multivariate regression and machine learning with sums of separable functions. G. Beylkin, J. Garcke, and M. J. Mohlenkamp. SIAM Journal on Scientific Computing, 31(3):1840–1857, 2009. BibTeX PDF DOI Link
  24. Fitting multidimensional data using gradient penalties and the sparse grid combination technique. J. Garcke and M. Hegland. Computing, 84(1-2):1–25, April 2009. BibTeX PDF DOI
  25. The combination technique and some generalisations. M. Hegland, J. Garcke, and V. Challis. Linear Algebra and its Applications, 420(2–3):249–275, 2007. BibTeX PDF DOI
  26. Parallelisation of sparse grids for large scale data analysis. J. Garcke, M. Hegland, and O. Nielsen. ANZIAM Journal, 48(1):11–22, 2006. BibTeX PDF DOI
  27. Classification with sparse grids using simplicial basis functions. J. Garcke and M. Griebel. Intelligent Data Analysis, 6(6):483–502, 2002. BibTeX PostScript PDF Publisher
  28. Data mining with sparse grids. J. Garcke, M. Griebel, and M. Thess. Computing, 67(3):225–253, 2001. BibTeX PostScript DOI
  29. On the computation of the eigenproblems of hydrogen and helium in strong magnetic and electric fields with the sparse grid combination technique. J. Garcke and M. Griebel. Journal of Computational Physics, 165(2):694–716, 2000. BibTeX PostScript PDF DOI

Refereed Proceedings in Machine Learning/Artificial Intelligence

  1. Generalizing diversity with the signature transform. A. Feiden, B. Paparella, and J. Garcke. In GECCO 2024 Companion Proceedings. 2024. BibTeX PDF DOI
  2. Unsupervised representation learning for diverse deformable shape collections. S. Hahner, S. Attaiki, J. Garcke, and M. Ovsjanikov. In 2024 International Conference on 3D Vision (3DV), 1594–1604. IEEE Computer Society, 2024. BibTeX DOI arXiv
  3. Learning crowd behaviors in navigation with attention-based spatial-temporal graphs. Y. Zhou and J. Garcke. In 2024 International Conference on Robotics and Automation (ICRA), 5485 – 5491. 2024. BibTeX DOI arXiv
  4. Overcoming deceptive rewards with quality-diversity. A. Feiden and J. Garcke. In GECCO 2023 Companion Proceedings. 2023. BibTeX PDF DOI
  5. Graph extraction for assisting crash simulation data analysis. A. Pakiman, J. Garcke, and A. Schumacher. In M. Ojeda-Aciego, K. Sauerwald, and R. Jäschke, editors, Proceedings of International Conference on Conceptual Structures: Graph-Based Representation and Reasoning, 171–185. Springer, 2023. BibTeX PDF DOI
  6. How does knowledge injection help in informed machine learning? L. von Rueden, J. Garcke, and C. Bauckhage. In 2023 International Joint Conference on Neural Networks (IJCNN), 1–8. 2023. BibTeX PDF DOI
  7. Foresight social-aware reinforcement learning for robot navigation. Y. Zhou, S. Li, and J. Garcke. In 35th Chinese Control and Decison Conference. 2023. BibTeX PDF
  8. Mesh convolutional autoencoder for semi-regular meshes of different sizes. S. Hahner and J. Garcke. In IEEE Winter Conference on Applications of Computer Vision, WACV 2022. IEEE, 2022. BibTeX DOI arXiv Proceedings
  9. Transfer learning using spectral convolutional autoencoders on semi-regular surface meshes. S. Hahner, F. Kerkhoff, and J. Garcke. In B. Rieck and R. Pascanu, editors, Proceedings of the First Learning on Graphs Conference (LoG 2022), volume 198 of Proceedings of Machine Learning Research, 18:1–18:19. PMLR, 09–12 Dec 2022. BibTeX arXiv Proceedings
  10. Graph modeling in computer assisted automotive development. A. Pakiman and J. Garcke. In 2022 IEEE International Conference on Knowledge Graph (ICKG), 203–210. 2022. BibTeX DOI arXiv
  11. The pole balancing problem from the viewpoint of system flexibility. L. F. D. P. Sotto, S. Mayer, and J. Garcke. In GECCO 2022 Companion Proceedings. 2022. BibTeX PDF DOI
  12. Canonical convolutional neural networks. L. Veeramacheneni, M. Wolter, R. Klein, and J. Garcke. In International Joint Conference on Neural Networks (IJCNN). 2022. BibTeX PDF DOI arXiv
  13. Adaptive wavelet pooling for convolutional neural networks. M. Wolter and J. Garcke. In A. Banerjee and K. Fukumizu, editors, Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, volume 130 of Proceedings of Machine Learning Research, 1936–1944. PMLR, 13–15 Apr 2021. BibTeX PDF Proceedings
  14. Analysis and prediction of deforming 3d shapes using oriented bounding boxes and LSTM autoencoders. S. Hahner, R. Iza-Teran, and J. Garcke. In Proceedings of ICANN 2020. 2020. BibTeX DOI arXiv
  15. A compact spectral descriptor for shape deformations. S. Sible, R. Iza-Teran, J. Garcke, N. Aulig, and P. Wollstadt. In Proceedings of ECAI 2020. 2020. BibTeX PDF arXiv
  16. Combining machine learning and simulation to a hybrid modelling approach: current and future directions. L. von Rueden, S. Mayer, R. Sifa, C. Bauckhage, and J. Garcke. In M. R. Berthold, A. Feelders, and G. Krempl, editors, Advances in Intelligent Data Analysis XVIII. Springer, 2020. BibTeX PDF DOI
  17. Event-triggered learning for resource-efficient networked control. F. Solowjow, D. Baumann, J. Garcke, and S. Trimpe. In 2018 American Control Conference, 6506–6512. 2018. BibTeX DOI PDF
  18. Importance Weighted Inductive Transfer Learning for Regression. J. Garcke and T. Vanck. In T. Calders, F. Esposito, E. Hüllermeier, and R. Meo, editors, Proceedings of ECMLPKDD 2014, Nancy, volume 8724 of Lecture Notes in Computer Science, 466–481. Springer Berlin Heidelberg, 2014. BibTeX PDF DOI
  19. Using Hyperbolic Cross Approximation to measure and compensate Covariate Shift. T. Vanck and J. Garcke. In Proceedings of ACML 2013, Canberra, 435–450. 2013. BibTeX PDF Link
  20. On a connection between maximum variance unfolding, shortest path problems and isomap. A. Paprotny and J. Garcke. In 15th International Conference on Artificial Intelligence and Statistics (AISTATS 2012), 859–867. La Palma, Canary Islands, Spain, April 21–23 2012. BibTeX PDF Link
  21. Classification with sums of separable functions. J. Garcke. In J. Balcázar, F. Bonchi, A. Gionis, and M. Sebag, editors, ECML PKDD 2010, Part I, volume 6321 of LNAI, 458–473. 2010. BibTeX PDF
  22. Approximating gaussian processes with H2{H^2}-matrices. S. Börm and J. Garcke. In J. N. Kok, J. Koronacki, R. L. de Mantaras, S. Matwin, D. Mladen, and A. Skowron, editors, Proceedings of 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007. ECML 2007, volume 4701, 42–53. 2007. BibTeX PDF DOI
  23. Regression with the optimised combination technique. J. Garcke. In W. Cohen and A. Moore, editors, Proceedings of the 23rd ICML '06, 321–328. New York, NY, USA, 2006. ACM Press. BibTeX PDF DOI
  24. Data mining with sparse grids using simplicial basis functions. J. Garcke and M. Griebel. In F. Provost and R. Srikant, editors, Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, 87–96. 2001. BibTeX PostScript PDF DOI

Submissions, Proceedings, Book Contributions, Others

  1. On the interplay of subset selection and informed graph neural networks. N. Breustedt, P. Climaco, J. Garcke, J. Hamaekers, G. Kutyniok, D. A. Lorenz, R. Oerder, and C. V. Shukla. In Informed Machine Learning. 2023. BibTeX PDF arXiv
  2. Informed pre-training of neural networks using prototypes from prior knowledge. L. von Rueden, S. Houben, K. Cvejoski, J. Garcke, C. Bauckhage, and N. Piatkowski. preprint, 2023. BibTeX
  3. Identifying similarities and exceptions in deformations and mesh functions – Comparing many simulation results automatically. J. Garcke, R. Iza-Teran, M. Pathare, D. Steffes-lai, and P. Schwanitz. In SIMVEC 2022, pages 277–288. VDI Verlag, 2022. BibTeX PDF DOI
  4. Explorative in-situ analysis of turbulent flow data based on a data-driven approach. C. Gscheidle and J. Garcke. In 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS), 1–10. CIMNE, 2022. BibTeX PDF DOI
  5. Shape-feature approach for locally accurate prediction of CFD simulation results. D. Steffes-lai, R. Iza-Teran, C. Gscheidle, and J. Garcke. In NAFEMS Seminar, Machine Learning und Artificial Intelligence in der Strömungsmechanik und der Strukturanalyse. 2022. BibTeX PDF
  6. Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell. P. Burggraef, J. Wagner, B. Heinbach, F. Steinberg, A. Perez, L. Schmallenbach, J. Garcke, D. Steffes-lai, and M. Wolter. In Procedia CIRPS. 2021. BibTeX DOI
  7. Learning product properties with small datasets in forming simulations. R. Iza-Teran, L. Morand, D. Helm, and J. Garcke. In International Conference and Workshop on Numerical Simulation of 3D Sheet Metal Forming Processes (NUMISHEET 2021). 2021. BibTeX PDF
  8. Decision support by interpretable machine learning in acoustic emission based cutting tool wear prediction. A. Schmetz, C. Vahl, Z. Zhen, D. Reibert, S. Mayer, D. Zontar, J. Garcke, and C. Brecher. In IEEE International Conference on Industrial Engineering and Engineering Management (IEEM2021). 2021. BibTeX DOI
  9. Towards a Framework for Automatic Event Detection for Car Crash Simulations. D. Steffes-lai, M. Pathare, and J. Garcke. In NAFEMS World Congress 2021. 2021. BibTeX PDF
  10. What identifies a whale by its fluke? On the benefit of interpretable machine learning for whale identification. J. Kierdorf, J. Garcke, J. Behley, T. Cheeseman, and R. Roscher. In ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Science. 2020. BibTeX DOI PDF
  11. Automatic analysis of crash simulations with dimensionality reduction algorithms such as PCA and t-SNE. D. Kracker, J. Garcke, and A. Schuhmacher. In 16th International LS-DYNA Users Conference. 2020. accepted. BibTeX PDF
  12. Explain it to me - Facing Remote Sensing Challenges in the Bio- and Geosciences with Explainable Machine Learning. R. Roscher, B. Bohn, M. F. Duarte, and J. Garcke. In ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Science. 2020. BibTeX DOI PDF
  13. Cognitive Systems and Robotics. C. Bauckhage, T. Bauernhansl, J. Beyerer, and J. Garcke. In R. Neugebauer, editor, Digital Transformation, pages 231–251. Springer Berlin Heidelberg, Berlin, Heidelberg, 2019. BibTeX DOI
  14. Analysis of Turbulent Flow Data Based on a Spectral Basis Representation. J. Garcke, C. Gscheidle, R. Iza-Teran, and L. Berger. In NAFEMS World Congress 2019, Quebec City. 2019. BibTeX PDF
  15. Finite differences on sparse grids for continuous time heterogeneous agent models. J. Garcke and S. Ruttscheidt. Available as INS Preprint No. 1906., 2019. BibTeX PDF
  16. A Knowledge-Based Surrogate Modeling Approach For Cup Drawing With Limited Data. L. Morand, D. Helm, J. Garcke, and R. Iza-Teran. In International Deep-Drawing Research Group Conference 2019. 2019. BibTeX PDF
  17. Kognitive Systeme und Robotik. C. Bauckhage, T. Bauernhansl, J. Beyerer, and J. Garcke. In R. Neugebauer, editor, Digitalisierung: Schlüsseltechnologien für Wirtschaft und Gesellschaft, pages 239–260. Springer, Berlin, Heidelberg, 2018. BibTeX DOI
  18. Efficient higher order time discretization schemes for Hamilton-Jacobi-Bellman equations based on diagonally implicit symplectic Runge-Kutta methods. J. Garcke and I. Kalmykov. In D. Kalise, K. Kunisch, and Z. Rao, editors, Hamilton-Jacobi-Bellman Equations: Numerical Methods and Applications in Optimal Control, pages 97–128. De Gruyter, 2018. BibTeX PDF DOI
  19. Energy-Efficiency and Performance Comparison of Aerosol Optical Depth Retrieval on Distributed Embedded SoC Architectures. D. Feld, J. Garcke, J. Liu, E. Schricker, T. Soddemann, and Y. Xue. In M. Griebel, A. Schüller, and M. A. Schweitzer, editors, Scientific Computing and Algorithms in Industrial Simulations: Projects and Products of Fraunhofer SCAI, pages 341–358. Springer International Publishing, Cham, 2017. BibTeX PDF DOI
  20. Machine learning approaches for data from car crashes and numerical car crash simulations. J. Garcke and R. Iza-Teran. In NAFEMS 2017, Stockholm. 2017. BibTeX PDF
  21. Dimensionality Reduction for the Analysis of Time Series Data from Wind Turbines. J. Garcke, R. Iza-Teran, M. Marks, M. Pathare, D. Schollbach, and M. Stettner. In M. Griebel, A. Schüller, and M. A. Schweitzer, editors, Scientific Computing and Algorithms in Industrial Simulations: Projects and Products of Fraunhofer SCAI, pages 317–339. Springer International Publishing, Cham, 2017. BibTeX PDF DOI
  22. ModelCompare. J. Garcke, M. Pathare, and N. Prabakaran. In M. Griebel, A. Schüller, and M. A. Schweitzer, editors, Scientific Computing and Algorithms in Industrial Simulations: Projects and Products of Fraunhofer SCAI, pages 199–205. Springer International Publishing, Cham, 2017. BibTeX PDF DOI
  23. Datenanalysemethoden zur Auswertung von Simulationsergebnissen im Crash und deren Abgleich mit dem Experiment. J. Garcke, R. Iza-Teran, and N. Prabakaran. In VDI-Tagung SIMVEC 2016. 2016. BibTeX PDF
  24. Towards an industry data gateway: an integrated platform for the analysis of wind turbine data. A. Aguilera, R. Grunzke, U. Markwardt, D. Habich, D. Schollbach, and J. Garcke. In 7th International Workshop on Science Gateways (IWSG), 62–66. June 2015. BibTeX PDF DOI
  25. Machine learning approaches for repositories of numerical simulation results. J. Garcke and R. Iza-Teran. In 10th European LS-DYNA Conference 2015. 2015. BibTeX PDF
  26. Maschinelle Lernverfahren zur effizienten und interaktiven Auswertung großer Mengen von CAE-Modellvarianten. J. Garcke and R. Iza-Teran. In VDI-Tagung SIMVEC 2014. 2014. BibTeX PDF
  27. Adaptive sparse grids in reinforcement learning. J. Garcke and I. Klompmaker. In S. Dahlke, W. Dahmen, M. Griebel, W. Hackbusch, K. Ritter, R. Schneider, C. Schwab, and H. Yserentant, editors, Extraction of Quantifiable Information from Complex Systems, volume 102 of Lecture Notes in Computational Science and Engineering, pages 179–194. Springer, 2014. BibTeX PDF DOI
  28. Data analytics for simulation repositories in industry. R. Iza-Teran and J. Garcke. In E. Plödereder, L. Grunske, E. Schneider, and D. Ull, editors, GI-Tagungsband der Informatik 2014, 161–167. 2014. BibTeX PDF
  29. Analysis of Car Crash Simulation Data with Nonlinear Machine Learning Methods. B. Bohn, J. Garcke, R. Iza-Teran, A. Paprotny, B. Peherstorfer, U. Schepsmeier, and C.-A. Thole. In Procedia Computer Science, Proceedings of the ICCS 2013, Barcelona, volume 18, 621–630. 2013. BibTeX PDF Supplementary Material DOI
  30. Sparse grids in a nutshell. J. Garcke. In J. Garcke and M. Griebel, editors, Sparse grids and applications, volume 88 of Lecture Notes in Computational Science and Engineering, pages 57–80. Springer, 2013. BibTeX PDF Extended Version With Python Code DOI
  31. Intraday foreign exchange rate forecasting using sparse grids. J. Garcke, T. Gerstner, and M. Griebel. In J. Garcke and M. Griebel, editors, Sparse grids and applications, volume 88 of Lecture Notes in Computational Science and Engineering, pages 81–105. Springer, 2013. BibTeX PDF DOI
  32. Time series forecasting using sparse grids. J. Garcke, T. Gerstner, and M. Griebel. Technical Report, Fraunhofer SCAI, 2013. BibTeX PDF Link
  33. A framework for simulation process management and data mining. C. Schöne, R. Iza-Teran, and J. Garcke. In 1st International Simulation Data and Process Management Conference, Salzburg, Jun 9-12. 2013. BibTeX PDF
  34. A dimension adaptive combination technique using localised adaptation criteria. J. Garcke. In H. G. Bock, X. P. Hoang, R. Rannacher, and J. P. Schlöder, editors, Modeling, Simulation and Optimization of Complex Processes, pages 115–125. Springer Berlin Heidelberg, 2012. BibTeX PDF DOI
  35. On the numerical solution of the chemical master equation with sums of rank one tensors. M. Hegland and J. Garcke. In W. McLean and A. J. Roberts, editors, Proceedings of the 15th Biennial Computational Techniques and Applications Conference, CTAC-2010, volume 52 of ANZIAM J., C628–C643. aug 2011. BibTeX PDF Publisher
  36. Data mining for the category management in the retail market. J. Garcke, M. Griebel, and M. Thess. In M. Grötschel, K. Lucas, and V. Mehrmann, editors, Production Factor Mathematics, pages 81–92. Springer Berlin Heidelberg, 2010. BibTeX DOI
  37. Data-Mining für die Angebotsoptimierung im Handel. J. Garcke, M. Griebel, and M. Thess. In M. Grötschel, K. Lucas, and V. Mehrmann, editors, Produktionsfaktor Mathematik, acatech diskutiert, pages 111–123. Springer, 2008. BibTeX PDF DOI
  38. An optimised sparse grid combination technique for eigenproblems. J. Garcke. In Proceedings of ICIAM 2007, volume 7 of PAMM, 1022301–1022302. 2008. BibTeX PDF DOI
  39. Fitting multidimensional data using gradient penalties and combination techniques. J. Garcke and M. Hegland. In H.G. Bock, E. Kostina, X.P. Hoang, and R. Rannacher, editors, Proceedings of HPSC 2006, Hanoi, Vietnam, 235–248. 2008. BibTeX PDF
  40. A dimension adaptive sparse grid combination technique for machine learning. J. Garcke. In W. Read, J. W. Larson, and A. J. Roberts, editors, Proceedings of the 13th Biennial Computational Techniques and Applications Conference, CTAC-2006, volume 48 of ANZIAM J., C725–C740. 2007. BibTeX PDF Publisher
  41. Semi-supervised learning with sparse grids. J. Garcke and M. Griebel. In M.-R. Amini, O. Chapelle, and R. Ghani, editors, Proceedings of ICML, Workshop on Learning with Partially Classified Training Data, 19–28. 2005. BibTeX PDF
  42. Parallelisation of sparse grids for large scale data analysis. J. Garcke, M. Hegland, and O. Nielsen. In P. Sloot, D. Abramson, A. Bogdanov, J. Dongarra, A. Zomaya, and Y. Gorbachev, editors, Proceedings of the International Conference on Computational Science 2003 (ICCS 2003) Melbourne, Australia, volume 2659 of Lecture Notes in Computer Science, 683–692. Springer, 2003. BibTeX PDF
  43. On the parallelization of the sparse grid approach for data mining. J. Garcke and M. Griebel. In S. Margenov, J. Wasniewski, and P. Yalamov, editors, Large-Scale Scientific Computations, Third International Conference, LSSC 2001, Sozopol, Bulgaria, volume 2179 of Lecture Notes in Computer Science, 22–32. Springer, 2001. BibTeX PostScript PDF

Edited volumes

  1. Sparse Grids and Applications - Munich 2018, volume 144 of Lecture Notes in Computational Science and Engineering. Springer, 2022. H. J. Bungartz, J. Garcke, and D. Pflüger, editors. BibTeX DOI
  2. Sparse Grids and Applications - Miami 2016, volume 123 of Lecture Notes in Computational Science and Engineering, Springer, 2018. J. Garcke, D. Pflüger, C. Webster, and G. Zhang, editors. BibTeX DOI
  3. Sparse Grids and Applications - Stuttgart 2014, volume 109 of Lecture Notes in Computational Science and Engineering, Springer, 2016. J. Garcke and D. Pflüger, editors. BibTeX DOI
  4. Sparse Grids and Applications - Munich 2012, volume 97 of Lecture Notes in Computational Science and Engineering, Springer, 2014. J. Garcke and D. Pflüger, editors. BibTeX DOI
  5. Sparse Grids and Applications, volume 88 of Lecture Notes in Computational Science and Engineering, Springer, 2013. J. Garcke and M. Griebel, editors. BibTeX DOI

Other reports

  1. VAVID - Vergleichende Analyse von ingenieurrelevanten Mess- und Simulationsdaten. J. Garcke, A. Aguilera, M. Büchse, L. Drack, R. Iza-Teran, M. Liebscher, J. Luong, S. Mertler, H. Müllerschön, B. Römer, D. Schollbach, and C. Schöne. Abschlussbericht des BMBF-Projekts, BMBF-Projekt, 2018. BibTeX DOI
  2. SIMDATA-NL - Nichtlineare Charakterisierung und Analyse von FEM-Simulationsergebnissen für Autobauteile und Crash-Tests. M. Griebel, H.-J. Bungartz, C. Czado, J. Garcke, U. Trottenberg, C.-A. Thole, B. Bohn, R. Iza-Teran, A. Paprotny, B. Peherstorfer, and U. Schepsmeier. Abschlussbericht des BMBF-Projekts, BMBF-Projekt, 2014. BibTeX PDF