Dirk Pflüger
Cited by
Cited by
Spatially adaptive sparse grids for high-dimensional problems
DM Pflüger
Technische Universität München, 2010
Modeling and Simulation: An Application-Oriented Introduction
HJ Bungartz, S Zimmer, M Buchholz, D Pflüger
Optimization 53, 2014
Modellbildung und Simulation: eine anwendungsorientierte Einführung
HJ Bungartz, S Zimmer, M Buchholz, D Pflüger
Springer-Verlag, 2009
Spatially adaptive sparse grids for high-dimensional data-driven problems
D Pflüger, B Peherstorfer, HJ Bungartz
Journal of Complexity 26 (5), 508-522, 2010
PDEBench: An extensive benchmark for scientific machine learning
M Takamoto, T Praditia, R Leiteritz, D MacKinlay, F Alesiani, D Pflüger, ...
Advances in Neural Information Processing Systems 35, 1596-1611, 2022
Polynomial chaos expansions for dependent random variables
JD Jakeman, F Franzelin, A Narayan, M Eldred, D Pflüger
Computer Methods in Applied Mechanics and Engineering 351, 643-666, 2019
Density estimation with adaptive sparse grids for large data sets
B Peherstorfer, D Pflüger, HJ Bungartz
Proceedings of the 2014 SIAM International Conference on Data Mining, 443-451, 2014
Spatially adaptive refinement
D Pflüger
Sparse grids and applications, 243-262, 2012
Option pricing with a direct adaptive sparse grid approach
HJ Bungartz, A Heinecke, D Pflüger, S Schraufstetter
Journal of Computational and Applied Mathematics 236 (15), 3741-3750, 2012
Extending a Highly Parallel Data Mining Algorithm to the Intel ® Many Integrated Core Architecture
A Heinecke, M Klemm, D Pflüger, A Bode, HJ Bungartz
Euro-Par 2011: Parallel Processing Workshops: CCPI, CGWS, HeteroPar, HiBB …, 2012
Compact data structure and scalable algorithms for the sparse grid technique
A Murarasu, J Weidendorfer, G Buse, D Butnaru, D Pflüger
ACM SIGPLAN Notices 46 (8), 25-34, 2011
Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario
M Köppel, F Franzelin, I Kröker, S Oladyshkin, G Santin, D Wittwar, ...
Computational Geosciences 23, 339-354, 2019
Multi-and many-core data mining with adaptive sparse grids
A Heinecke, D Pflüger
Proceedings of the 8th ACM International Conference on Computing Frontiers, 1-10, 2011
Hierarchical gradient-based optimization with B-splines on sparse grids
J Valentin, D Pflüger
Sparse Grids and Applications-Stuttgart 2014, 315-336, 2016
Evaluation of pool-based testing approaches to enable population-wide screening for COVID-19
T de Wolff, D Pflüger, M Rehme, J Heuer, MI Bittner
PLoS One 15 (12), e0243692, 2020
Load balancing for massively parallel computations with the sparse grid combination technique
M Heene, C Kowitz, D Pflüger
Parallel Computing: Accelerating Computational Science and Engineering (CSE …, 2014
Emerging architectures enable to boost massively parallel data mining using adaptive sparse grids
A Heinecke, D Pflüger
International Journal of Parallel Programming 41 (3), 357-399, 2013
From piz daint to the stars: simulation of stellar mergers using high-level abstractions
G Daiß, P Amini, J Biddiscombe, P Diehl, J Frank, K Huck, H Kaiser, ...
Proceedings of the International Conference for High Performance Computing …, 2019
Hybrid parallel solutions of the Black-Scholes PDE with the truncated combination technique
J Benk, D Pflüger
2012 International Conference on High Performance Computing & Simulation …, 2012
Harnessing billions of tasks for a scalable portable hydrodynamic simulation of the merger of two stars
T Heller, BA Lelbach, KA Huck, J Biddiscombe, P Grubel, AE Koniges, ...
The International Journal of High Performance Computing Applications 33 (4 …, 2019
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