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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

Sparse Grids and Applications

6th Workshop on Sparse Grids and Applications

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 2020

Summer semester 2019

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

Journal papers

  1. 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
  2. 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
  3. 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. mar 2020. BibTeX PDF arXiv
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. Data mining with sparse grids. J. Garcke, M. Griebel, and M. Thess. Computing, 67(3):225–253, 2001. BibTeX PostScript DOI
  17. 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/Data Mining

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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. 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 2020). 2020. BibTeX PDF
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. Finite differences on sparse grids for continuous time heterogeneous agent models. J. Garcke and S. Ruttscheidt. Submitted to Proceedings of SGA 2018. Available as INS Preprint No. 1906., 2019. BibTeX PDF
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. Time series forecasting using sparse grids. J. Garcke, T. Gerstner, and M. Griebel. Technical Report, Fraunhofer SCAI, 2013. BibTeX PDF Link
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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

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