Publications

How to Cite SG⁺⁺

If you use SG⁺⁺ for your own publication, please link http://sgpp.sparsegrids.org and cite the following book:

  • D. Pflüger, Spatially Adaptive Sparse Grids for High-Dimensional Problems, Verlag Dr. Hut, 2010.

All Publications with SG⁺⁺

2019

  • N. Boike, E. Mozikov, M. Staub, and M. Tompert, “Peg-in-hole mit flexiblen Objekten,” project thesis, University of Stuttgart, 2019.
  • [DOI] J. Valentin, “B-Splines for Sparse Grids: Algorithms and Application to Higher-Dimensional Optimization,” PhD thesis, University of Stuttgart, 2019.
  • [DOI] P. Wundrack, “Verteilte Dünngitter-Regression mit SG⁺⁺ und HPX,” master’s thesis, University of Stuttgart, 2019.

2018

  • F. Franzelin, “Data-Driven Uncertainty Quantification for Large-Scale Simulations,” PhD thesis, University of Stuttgart, 2018.
  • [DOI] S. Friz, “Generalized B-Splines auf Dünnen Gittern,” master’s thesis, University of Stuttgart, 2018.
  • [DOI] M. Heene, A. P. Hinojosa, M. Obersteiner, H. Bungartz, and D. Pflüger, “EXAHD: An Exa-Scalable Two-Level Sparse Grid Approach for Higher-Dimensional Problems in Plasma Physics and Beyond,” in High Performance Computing in Science and Engineering ’17, Springer, 2018, p. 513–529.
  • [DOI] M. Heene, “A Massively Parallel Combination Technique for the Solution of High-Dimensional PDEs,” PhD thesis, University of Stuttgart, 2018.
  • [DOI] J. Valentin and D. Pflüger, “Fundamental Splines on Sparse Grids and Their Application to Gradient-Based Optimization,” in Sparse Grids and Applications – Miami 2016, 2018, p. 229–251.
  • [DOI] J. Valentin, M. Sprenger, D. Pflüger, and O. Röhrle, “Gradient-Based Optimization with B-Splines on Sparse Grids for Solving Forward-Dynamics Simulations of Three-Dimensional, Continuum-Mechanical Musculoskeletal System Models,” International Journal for Numerical Methods in Biomedical Engineering, vol. 34, iss. 5, p. 1–21, 2018.

2017

  • [DOI] F. Diez, “Gradientenbasierte Approximation mit B-Splines auf dünnen Gittern,” bachelor’s thesis, University of Stuttgart, 2017.
  • [URL] J. Drodofsky, “Große lineare Gleichungssysteme für die B-Spline Interpolation auf Dünnen Gittern,” bachelor’s thesis, University of Stuttgart, 2017.
  • J. Gemander, R. Hartung, and M. Koch, “3D printing and visualisation in the topology optimization with sparse grids,” project thesis, University of Stuttgart, 2017.
  • M. Luz, “Subspace-optimales Data Mining auf räumlich adaptiven dünnen Gittern,” master’s thesis, University of Stuttgart, 2017.
  • [DOI] M. Obersteiner, A. Parra Hinojosa, M. Heene, H. Bungartz, and D. Pflüger, “A Highly Scalable, Algorithm-Based Fault-Tolerant Solver for Gyrokinetic Plasma Simulations,” in Proceedings of the 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, 2017.

2016

  • F. Diez, F. Grotepaß, M. Küchle, and J. Schröder, “High Performance Sparse Grid Data Mining with B-Splines,” project thesis, University of Stuttgart, 2016.
  • [DOI] F. Franzelin and D. Pflüger, “From Data to Uncertainty: An Efficient Integrated Data-Driven Sparse Grid Approach to Propagate Uncertainty,” in Sparse Grids and Applications – Stuttgart 2014, 2016, p. 29–49.
  • [DOI] M. Heene and D. Pflüger, “Scalable Algorithms for the Solution of Higher-Dimensional PDEs,” in Software for Exascale Computing – SPPEXA 2013-2015, H. Bungartz, P. Neumann, and W. E. Nagel, Eds., Springer, 2016, p. 165–186.
  • [DOI] F. Huber, “The Sparse Grid Combination Technique for Quantities of Interest,” bachelor’s thesis, University of Stuttgart, 2016.
  • [DOI] P. Hupp, M. Heene, R. Jacob, and D. Pflüger, “Global Communication Schemes for the Numerical Solution of High-dimensional PDEs,” Parallel Computing, vol. 52, iss. C, p. 78–105, 2016.
  • [DOI] V. Khakhutskyy and M. Hegland, “Spatially-Dimension-Adaptive Sparse Grids for Online Learning,” in Sparse Grids and Applications – Stuttgart 2014, 2016, p. 133–162.
  • [URL] A. Kulischkin, “Die Kombinationstechnik als Zeitintegrator in Parareal,” bachelor’s thesis, University of Stuttgart, 2016.
  • [DOI] D. Pfander, A. Heinecke, and D. Pflüger, “A New Subspace-Based Algorithm for Efficient Spatially Adaptive Sparse Grid Regression, Classification and Multi-evaluation,” in Sparse Grids and Applications – Stuttgart 2014, 2016, p. 221–246.
  • D. Pfander and D. Pflüger, “Achieving Performance-Portable Close-to-Peak Performance by Static Auto-Tuning for Regression on Sparse Grids,” Parallel Computing, 2016.
  • [DOI] D. Pflüger and D. Pfander, “Computational Efficiency vs. Maintainability and Portability. Experiences with the Sparse Grid Code SG⁺⁺,” in Proceedings of the Fourth International Workshop on Software Engineering for HPC in Computational Science and Engineering, 2016, p. 10–18.
  • [URL] J. Schröder, “Optimierung von unsicheren Systemen mit B-Splines auf Dünnen Gittern,” bachelor’s thesis, University of Stuttgart, 2016.
  • [DOI] J. Valentin and D. Pflüger, “Hierarchical Gradient-Based Optimization with B-Splines on Sparse Grids,” in Sparse Grids and Applications – Stuttgart 2014, 2016, p. 315–336.

2015

  • [URL] M. Brunn, “Data-mining on adaptively refined sparse grids with higher order basis functions on GPUs,” bachelor’s thesis, University of Stuttgart, 2015.
  • [DOI] G. Daiß, “Verteiltes Dünngitter Clustering mit großen Datensätzen,” bachelor’s thesis, University of Stuttgart, 2015.
  • [DOI] M. Franke, “Sparse Grid Datamining with Huge Datasets,” bachelor’s thesis, University of Stuttgart, 2015.
  • [DOI] F. Franzelin, P. Diehl, and D. Pflüger, “Non-intrusive Uncertainty Quantification with Sparse Grids for Multivariate Peridynamic Simulations,” in Meshfree Methods for Partial Differential Equations VII, M. Griebel and M. A. Schweitzer, Eds., Springer, 2015, vol. 100, p. 115–143.
  • F. Gajek, F. Kohlgrüber, V. Siegert, and M. Thull, “Fuzzy Control with the Combination Technique,” project thesis, University of Stuttgart, 2015.
  • [DOI] M. Heene and D. Pflüger, “Efficient and scalable distributed-memory hierarchization algorithms for the sparse grid combination technique,” in Parallel Computing: On the Road to Exascale, Proceedings of the International Conference on Parallel Computing, ParCo 2015, 1-4 September 2015, Edinburgh, Scotland, UK, 2015, p. 339–348.
  • [DOI] A. P. Hinojosa, C. Kowitz, M. Heene, D. Pflüger, and H. -J. Bungartz, “Towards a Fault-Tolerant, Scalable Implementation of GENE,” in Recent Trends in Computational Engineering – CE2014: Optimization, Uncertainty, Parallel Algorithms, Coupled and Complex Problems, M. Mehl, M. Bischoff, and M. Schäfer, Eds., Springer, 2015, p. 47–65.
  • R. Leiteritz, “A Highly-Parallel Adaptive Algorithm for Regression Tasks on Spatially Adaptive Sparse Grids,” project thesis, University of Stuttgart, 2015.
  • C. Proissl, “Globale Optimierung mit Dünngittersurrogaten und dem Algorithmus GOSGrid,” project thesis, University of Stuttgart, 2015.
  • [DOI] C. Schreiber, “Dünngitter-Diskretisierungen für Probleme mit variablen Koeffizienten,” bachelor’s thesis, University of Stuttgart, 2015.

2014

  • [DOI] G. Buse, D. Pflüger, and R. Jacob, “Efficient Pseudorecursive Evaluation Schemes for Non-Adaptive Sparse Grids,” in Sparse Grids and Applications – Munich 2012, 2014, p. 1–27.
  • [DOI] M. Heene, C. Kowitz, and D. Pflüger, “Load Balancing for Massively Parallel Computations with the Sparse Grid Combination Technique,” in Parallel Computing: Accelerating Computational Science and Engineering (CSE), 2014, p. 574–583.
  • [DOI] P. Hupp, R. Jacob, M. Heene, D. Pflüger, and M. Hegland, “Global Communication Schemes for the Sparse Grid Combination Technique,” in Parallel Computing: Accelerating Computational Science and Engineering (CSE), 2014, pp. 564-573.
  • [DOI] V. Khakhutskyy, D. Pflüger, and M. Hegland, “Scalability and Fault Tolerance of the Alternating Direction Method of Multipliers for Sparse Grids,” Parallel Computing: Accelerating Computational Science and Engineering (CSE), vol. 25, pp. 603-612, 2014.
  • [DOI] M. Lahnert, “Quantifizierung von Unsicherheiten auf adaptiven dünnen Gittern mit stückweise polynomiellen Basisfunktionen,” diploma thesis, University of Stuttgart, 2014.
  • B. Peherstorfer, D. Pflüger, and H. Bungartz, “Density Estimation with Adaptive Sparse Grids for Large Data Sets,” in Proceedings of the 2014 SIAM International Conference on Data Mining, 2014, p. 443–451.
  • [DOI] D. Pflüger, H. Bungartz, M. Griebel, F. Jenko, T. Dannert, M. Heene, C. Kowitz, A. Hinojosa, and P. Zaspel, “EXAHD: An Exa-scalable Two-Level Sparse Grid Approach for Higher-Dimensional Problems in Plasma Physics and Beyond,” in Euro-Par 2014: Parallel Processing Workshops, L. Lopes, J. Žilinskas, A. Costan, R. G. Cascella, G. Kecskemeti, E. Jeannot, M. Cannataro, L. Ricci, S. Benkner, S. Petit, V. Scarano, J. Gracia, S. Hunold, S. Scott, S. Lankes, C. Lengauer, J. Carretero, J. Breitbart, and M. Alexander, Eds., Springer, 2014, vol. 8806, p. 565–576.
  • [DOI] J. Valentin, “Hierarchische Optimierung mit Gradientenverfahren auf Dünngitterfunktionen,” master’s thesis, University of Stuttgart, 2014.

2013

  • A. Heinecke, R. Karlstetter, D. Pflüger, and H. Bungartz, “Data Mining on Vast Datasets as a Cluster System Benchmark,” Concurrency and Computation: Practice and Experience, 2013.
  • [DOI] C. Kowitz, D. Pflüger, F. Jenko, and M. Hegland, “The Combination Technique for the Initial Value Problem in Linear Gyrokinetics,” in Sparse Grids and Applications, Springer, 2013.
  • J. Leibinger, “Quantifizierung von Unsicherheiten mit dünnen Gittern,” diploma thesis, University of Stuttgart, 2013.
  • [DOI] D. Pfander, “Zeitreihenanalyse auf dünnen Gittern,” diploma thesis, University of Stuttgart, 2013.

2012

  • [DOI] A. Heinecke and D. Pflüger, “Emerging Architectures Enable to Boost Massively Parallel Data Mining Using Adaptive Sparse Grids,” International Journal of Parallel Programming, vol. 41, iss. 3, pp. 357-399, 2012.
  • [DOI] B. Peherstorfer, D. Pflüger, and H. Bungartz, “Clustering Based on Density Estimation with Sparse Grids,” in KI 2012: Advances in Artificial Intelligence, B. Glimm and A. Krüger, Eds., Springer, 2012, vol. 7526, p. 131–142.
  • [DOI] D. Pflüger, “Spatially Adaptive Refinement,” in Sparse Grids and Applications, 2012, p. 243–262.

2011

  • F. Franzelin, “Classification with Estimated Densities on Sparse Grids,” master’s thesis, Technical University of Munich, 2011.
  • [DOI] A. Heinecke and D. Pflüger, “Multi- and Many-core Data Mining with Adaptive Sparse Grids,” in Proceedings of the 8th ACM International Conference on Computing Frontiers, 2011, p. 29:1–29:10.

2010

  • [URL] D. Pflüger, Spatially Adaptive Sparse Grids for High-Dimensional Problems, Verlag Dr. Hut, 2010.

2008

  • [URL] H. Bungartz, D. Pflüger, and S. Zimmer, “Adaptive Sparse Grid Techniques for Data Mining,” in Modelling, Simulation and Optimization of Complex Processes 2006, Proc. Int. Conf. HPSC, Hanoi, Vietnam, 2008, p. 121–130.