Research Group of Prof. Dr. J. Garcke
Institute for Numerical Simulation
maximize

Publications of Prof. Dr. Jochen Garcke:

Edited volumes:

[1] J. Garcke and D. Pflüger, editors. Sparse Grids and Applications - Stuttgart 2014, volume 109 of Lecture Notes in Computational Science and Engineering. Springer, 2016.
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[2] J. Garcke and D. Pflüger, editors. Sparse Grids and Applications - Munich 2012, volume 97 of Lecture Notes in Computational Science and Engineering. Springer, 2014.
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[3] J. Garcke and M. Griebel, editors. Sparse Grids and Applications, volume 88 of Lecture Notes in Computational Science and Engineering. Springer, 2013.
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Journal Papers:

[1] J. Garcke and A. Kröner. Suboptimal Feedback Control of PDEs by Solving HJB Equations on Adaptive Sparse Grids. Journal of Scientific Computing, 70(1):1-28, 2017. also available as INS Preprint No. 1518.
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[2] A. Aguilera, R. Grunzke, D. Habich, J. Luong, D. Schollbach, U. Markwardt, and J. Garcke. Advancing a Gateway Infrastructure for Wind Turbine Data Analysis. Journal of Grid Computing, 14(4):499-514, 2016.
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[3] B. Bohn, J. Garcke, and M. Griebel. A sparse grid based method for generative dimensionality reduction of high-dimensional data. Journal of Computational Physics, 309:1 - 17, 2016. earlier version available as INS Preprint No. 1514.
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[4] J. Liu, D. Feld, Y. Xue, J. Garcke, T. Soddemann, and P. Pan. An efficient geosciences workflow on multi-core processors and GPUs: a case study for aerosol optical depth retrieval from MODIS satellite data. International Journal of Digital Earth, 9(8):748-765, 2016.
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[5] R. Iza-Teran and J. Garcke. Operator based multi-scale analysis of simulation bundles. 2015. Submitted, also available as INS Preprint No. 1524.
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[6] J. Liu, D. Feld, Y. Xue, J. Garcke, and T. Soddemann. Multi-core processors and graphics processing unit accelerators for parallel retrieval of aerosol optical depth from satellite data: Implementation, performance and energy efficiency. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5):2306-2317, 2015.
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[7] O. Bokanowski, J. Garcke, M. Griebel, and I. Klompmaker. An adaptive sparse grid semi-Lagrangian scheme for first order Hamilton-Jacobi Bellman equations. Journal of Scientific Computing, 55(3):575-605, 2013. also available as INS Preprint No. 1207.
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[8] J. Garcke and M. Hegland. Fitting multidimensional data using gradient penalties and the sparse grid combination technique. Computing, 84(1-2):1-25, April 2009.
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[9] G. Beylkin, J. Garcke, and M. J. Mohlenkamp. Multivariate regression and machine learning with sums of separable functions. SIAM Journal on Scientific Computing, 31(3):1840-1857, 2009.
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[10] M. Hegland, J. Garcke, and V. Challis. The combination technique and some generalisations. Linear Algebra and its Applications, 420(2-3):249-275, 2007.
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[11] J. Garcke, M. Hegland, and O. Nielsen. Parallelisation of sparse grids for large scale data analysis. ANZIAM Journal, 48(1):11-22, 2006.
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[12] J. Garcke and M. Griebel. Classification with sparse grids using simplicial basis functions. Intelligent Data Analysis, 6(6):483-502, 2002.
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[13] J. Garcke, M. Griebel, and M. Thess. Data mining with sparse grids. Computing, 67(3):225-253, 2001.
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[14] J. Garcke and M. Griebel. On the computation of the eigenproblems of hydrogen and helium in strong magnetic and electric fields with the sparse grid combination technique. Journal of Computational Physics, 165(2):694-716, 2000.
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Refereed Proceedings in Machine Learning/Data Mining:

[1] J. Garcke and T. Vanck. Importance Weighted Inductive Transfer Learning for Regression. In T. Calders, F. Esposito, E. Hüllermeier, and R. Meo, editors, Proceedings of ECMLPKDD 2014, Nancy, volume 8724 of Lecture Notes in Computer Science, pages 466-481. Springer Berlin Heidelberg, 2014.
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[2] T. Vanck and J. Garcke. Using Hyperbolic Cross Approximation to measure and compensate Covariate Shift. In Proceedings of ACML 2013, Canberra, page 435–450, 2013.
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[3] A. Paprotny and J. Garcke. On a connection between maximum variance unfolding, shortest path problems and isomap. In 15th International Conference on Artificial Intelligence and Statistics (AISTATS 2012), pages 859-867, La Palma, Canary Islands, Spain, April 21-23 2012.
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[4] J. Garcke. Classification with sums of separable functions. In J. Balcázar, F. Bonchi, A. Gionis, and M. Sebag, editors, ECML PKDD 2010, Part I, volume 6321 of LNAI, pages 458-473, 2010.
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[5] S. Börm and J. Garcke. Approximating gaussian processes with H2-matrices. 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, pages 42-53, 2007.
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[6] J. Garcke. Regression with the optimised combination technique. In W. Cohen and A. Moore, editors, Proceedings of the 23rd ICML '06, pages 321-328, New York, NY, USA, 2006. ACM Press.
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[7] J. Garcke and M. Griebel. Data mining with sparse grids using simplicial basis functions. In F. Provost and R. Srikant, editors, Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, pages 87-96, 2001.
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Submissions, Proceedings, Book Contributions, Others:

[1] D. Feld, J. Garcke, J. Liu, E. Schricker, T. Soddemann, and Y. Xue. Energy-efficiency and performance comparison of aerosol optical depth (AOD) retrieval on distributed embedded SoC architectures with Nvidia GPUs. 2017. to appear.
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[2] J. Garcke and R. Iza-Teran. Machine learning approaches for data from car crashes and numerical car crash simulations. In NAFEMS 2017, Stockholm, 2017.
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[3] J. Garcke, R. Iza-Teran, M. Marks, M. Pathare, D. Schollbach, and M. Stettner. Dimensionality reduction for the analysis of time series data from wind turbines. 2017. to appear.
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[4] J. Garcke and I. Kalmykov. Efficient higher order time discretization schemes for Hamilton-Jacobi-Bellman equations based on diagonally implicit symplectic Runge-Kutta methods. 2017. INS Preprint No. 1710.
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[5] J. Garcke, M. Pathare, and N. Prabakaran. ModelCompare. 2017. to appear.
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[6] J. Garcke, R. Iza-Teran, and N. Prabakaran. Datenanalysemethoden zur Auswertung von Simulationsergebnissen im Crash und deren Abgleich mit dem Experiment. In VDI-Tagung SIMVEC 2016, 2016.
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[7] A. Aguilera, R. Grunzke, U. Markwardt, D. Habich, D. Schollbach, and J. Garcke. Towards an industry data gateway: An integrated platform for the analysis of wind turbine data. In 7th International Workshop on Science Gateways (IWSG), pages 62-66, June 2015.
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[8] J. Garcke and R. Iza-Teran. Machine learning approaches for repositories of numerical simulation results. In 10th European LS-DYNA Conference 2015, 2015.
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[9] J. Garcke and R. Iza-Teran. Maschinelle Lernverfahren zur effizienten und interaktiven Auswertung großer Mengen von CAE-Modellvarianten. In VDI-Tagung SIMVEC 2014, 2014.
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[10] J. Garcke and I. Klompmaker. Adaptive sparse grids in reinforcement learning. 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.
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[11] 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. SIMDATA-NL - Nichtlineare Charakterisierung und Analyse von FEM-Simulationsergebnissen für Autobauteile und Crash-Tests. Abschlussbericht des BMBF-Projekts, 2014.
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[12] R. Iza-Teran and J. Garcke. Data analytics for simulation repositories in industry. In E. Plödereder, L. Grunske, E. Schneider, and D. Ull, editors, GI-Tagungsband der Informatik 2014, pages 161-167, 2014.
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[13] B. Bohn, J. Garcke, R. Iza-Teran, A. Paprotny, B. Peherstorfer, U. Schepsmeier, and C.-A. Thole. Analysis of Car Crash Simulation Data with Nonlinear Machine Learning Methods. In Procedia Computer Science, Proceedings of the ICCS 2013, Barcelona, volume 18, pages 621-630, 2013. supplementary material http://garcke.ins.uni-bonn.de/research/pub/simdata_supplementary.pdf 1.
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[14] J. Garcke. Sparse grids in a nutshell. 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. extended version with python code http://garcke.ins.uni-bonn.de/research/pub/sparse_grids_nutshell_code.pdf 1.
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[15] J. Garcke, T. Gerstner, and M. Griebel. Time series forecasting using sparse grids. 2013. submitted.
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[16] J. Garcke, T. Gerstner, and M. Griebel. Intraday foreign exchange rate forecasting using sparse grids. 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. also available as INS Preprint No. 1006.
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[17] C. Schöne, R. Iza-Teran, and J. Garcke. A framework for simulation process management and data mining. In 1st International Simulation Data and Process Management Conference, Salzburg, Jun 9-12, 2013.
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[18] J. Garcke. A dimension adaptive combination technique using localised adaptation criteria. 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.
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[19] M. Hegland and J. Garcke. On the numerical solution of the chemical master equation with sums of rank one tensors. 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., pages C628-C643, Aug. 2011.
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[20] J. Garcke, M. Griebel, and M. Thess. Data mining for the category management in the retail market. In M. Grötschel, K. Lucas, and V. Mehrmann, editors, Production Factor Mathematics, pages 81-92. Springer Berlin Heidelberg, 2010.
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[21] J. Garcke. An optimised sparse grid combination technique for eigenproblems. In Proceedings of ICIAM 2007, volume 7 of PAMM, pages 1022301-1022302, 2008.
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[22] J. Garcke, M. Griebel, and M. Thess. Data-Mining für die Angebotsoptimierung im Handel. In M. Grötschel, K. Lucas, and V. Mehrmann, editors, Produktionsfaktor Mathematik, acatech diskutiert, pages 111-123. Springer, 2008.
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[23] J. Garcke and M. Hegland. Fitting multidimensional data using gradient penalties and combination techniques. In H. Bock, E. Kostina, X. Hoang, and R. Rannacher, editors, Proceedings of HPSC 2006, Hanoi, Vietnam, pages 235-248, 2008.
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[24] J. Garcke. A dimension adaptive sparse grid combination technique for machine learning. 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., pages C725-C740, 2007.
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[25] J. Garcke and M. Griebel. Semi-supervised learning with sparse grids. In M.-R. Amini, O. Chapelle, and R. Ghani, editors, Proceedings of ICML, Workshop on Learning with Partially Classified Training Data, pages 19-28, 2005.
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[26] J. Garcke, M. Hegland, and O. Nielsen. Parallelisation of sparse grids for large scale data analysis. 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, pages 683-692. Springer, 2003.
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[27] J. Garcke and M. Griebel. On the parallelization of the sparse grid approach for data mining. 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, pages 22-32. Springer, 2001.
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Theses:

[1] J. Garcke. Maschinelles Lernen durch Funktionsrekonstruktion mit verallgemeinerten dünnen Gittern. Doktorarbeit, Institut für Numerische Simulation, Universität Bonn, 2004.
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[2] J. Garcke. Berechnung von Eigenwerten der stationären Schrödingergleichung mit der Kombinationstechnik. Diplomarbeit,thesis, Institut für Angewandte Mathematik, Universität Bonn, 1998.
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Other Reports:

[1] 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. SIMDATA-NL - Nichtlineare Charakterisierung und Analyse von FEM-Simulationsergebnissen für Autobauteile und Crash-Tests. Abschlussbericht des BMBF-Projekts, 2014.
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