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Research Group of Prof. Dr. Jochen Garcke

Contact Information

Address:
Institut für Numerische Simulation
Endenicher Allee 19b
53115 Bonn
Phone: +49 228 73-69838
Office: EA19b 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.

Teaching

Summer semester 2019

Winter semester 2018/19

See teaching activities of the whole group.

Publications

Edited volumes

  1. 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
  2. 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
  3. 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
  4. Sparse Grids and Applications, volume 88 of Lecture Notes in Computational Science and Engineering, Springer, 2013. J. Garcke and M. Griebel, editors. BibTeX

Journal papers

  1. Simplex stochastic collocation for piecewise smooth functions with kinks. B. Fuchs and J. Garcke. International Journal for Uncertainty Quantification, 2019. Accepted. Earlier version available as INS Preprint No. 1901. BibTeX PDF arXiv
  2. 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
  3. 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 Link
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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 Link
  10. 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
  11. 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
  12. 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
  13. Classification with sparse grids using simplicial basis functions. J. Garcke and M. Griebel. Intelligent Data Analysis, 6(6):483–502, 2002. BibTeX PDF PostScript Publisher
  14. Data mining with sparse grids. J. Garcke, M. Griebel, and M. Thess. Computing, 67(3):225–253, 2001. BibTeX PostScript DOI
  15. 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 PDF PostScript DOI

Refereed Proceedings in Machine Learning/Data Mining

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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 PDF PostScript DOI

Submissions, Proceedings, Book Contributions, Others

  1. 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
  2. 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
  3. 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
  4. Explainable Machine Learning for Scientific Insights and Discoveries. R. Roscher, B. Bohn, M. F. Duarte, and J. Garcke. 2019. BibTeX arXiv
  5. Informed Machine Learning - Towards a Taxonomy of Explicit Integration of Knowledge into Machine Learning. L. von Rueden, S. Mayer, J. Garcke, C. Bauckhage, and J. Schuecker. mar 2019. BibTeX arXiv
  6. 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
  7. 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
  8. Event-triggered learning for resource-efficient networked control. F. Solowjow, D. Baumann, J. Garcke, and S. Trimpe. In 2018 American Control Conference. 2018. BibTeX PDF
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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 Supplementary Material PDF
  20. 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 Extended Version With Python Code PDF
  21. 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
  22. Time series forecasting using sparse grids. J. Garcke, T. Gerstner, and M. Griebel. submitted, 2013. BibTeX PDF
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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 PDF PostScript

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
  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