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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.

Informationen für Studierende

In den verschiedenen Abteilungen von Fraunhofer SCAI sind regelmäßig Praktika, SHK-Stellen (z.B. für Programmiertätigkeiten, Experimente, IT-Unterstützung, Literaturrecherche,…) sowie Bachelor- und Masterarbeiten zu vergeben. Praktikumsmöglichkeiten im Bacheler und Master gibt es insbesondere in den Geschäftsfeldern High Performance Analytics, High Performance Computing, Multiphysics, Numerische datenbasierte Vorhersage, Schnelle Löser und Virtual Material Design.

Bei Interesse an anwendungsorientierten Aufgabenstellungen und angewandter Mathematik kann ich weitere Informationen geben und Kontakt zu den verschiedenen Arbeitsgruppen am Fraunhofer SCAI vermitteln. Falls Sie sich konkreter interessieren und bewerben möchten, bräuchte ich (mindestens) einen aktuellen Notenauszug, einen kurzen Lebenslauf und den Stand Ihrer Programmiererfahrung.

Aktuelle Stellenausschreibungen

  • Typischerweise sind SHK-Stellen in verschiedenen Abteilungen am Fraunhofer SCAI zu besetzen.

Teaching

Winter semester 2018/19

Summer semester 2018

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

Journal papers

  1. A geometrical method for low-dimensional representations of simulations. R. Iza-Teran and J. Garcke. 2018. revised. BibTeX PDF
  2. 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 Publisher Link
  3. 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 Publisher
  4. 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 Publisher
  5. 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 Publisher
  6. 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 Publisher
  7. 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 Publisher
  8. 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 Publisher Link
  9. 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 Publisher
  10. 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 Publisher
  11. 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 Publisher
  12. 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
  13. Data mining with sparse grids. J. Garcke, M. Griebel, and M. Thess. Computing, 67(3):225–253, 2001. BibTeX PostScript Publisher
  14. 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 Publisher

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 Publisher
  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 Publisher
  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 Publisher
  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 Publisher

Submissions, Proceedings, Book Contributions, Others

  1. 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 Publisher
  2. 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 Publisher
  3. 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
  4. 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 Publisher
  5. 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
  6. 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 Publisher
  7. 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 Publisher
  8. 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
  9. 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 Publisher
  10. 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
  11. 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
  12. 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 Publisher
  13. 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
  14. 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 Publisher
  15. 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 Publisher
  16. 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 Publisher
  17. Time series forecasting using sparse grids. J. Garcke, T. Gerstner, and M. Griebel. submitted, 2013. BibTeX PDF
  18. 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
  19. 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 Publisher
  20. 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
  21. 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 Publisher
  22. 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 Publisher
  23. An optimised sparse grid combination technique for eigenproblems. J. Garcke. In Proceedings of ICIAM 2007, volume 7 of PAMM, 1022301–1022302. 2008. BibTeX PDF Publisher
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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. 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