Dirk Pflüger
Member from 2015
to 2020
subject: Informatics
Contact
Universität Stuttgart
Institut für Parallele und Verteilte Systeme
Universitätsstr. 38
70569 Stuttgart
Tel.: (0711) 685-884 47
Fax: (0711) 685-704 13
dirk.pflueger@ipvs.uni-stuttgart.de
Research Areas
High-dimensional problems and simulations
Data-driven problems and machine learning
Sparse Grids and hierarchical methods
Parallelization and High-Performance Computing
Scientific Computing
Curriculum Vitae
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Since 2018
Professor for Scientific Computing, Institute for Parallel and Distributed Systems, University of Stuttgart
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2013
Stand-in Professor for Simulation of Large Systems, IPVS, University of Stuttgart
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2012
Junior Professor for Simulation Software Engineering, Institute for Parallel and Distributed Systems and Cluster of Excellence Simulation Technology, University of Stuttgart
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2011
Research stay at the CMA, Australian National University, Canberra
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2010-2012
Postdoc, Chair of Scientific Computing, Technische Universität München
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2010
Heinz-Schwärtzel-Prize of the TU München, the LMU and the Universität der Bundeswehr München for the best thesis in foundations of computer science
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2005-2010
Research Assistant at the Chair of Scientific Computing, Technische Universität München
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2003
Master Information Technology, The University of Sydney, Australia
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1999-2006
Diplomstudiengang Informatik with minor music theory, University of Stuttgart
Activities
Publications
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Modelling and Simulation: An Application-Oriented Introduction
H.-J. Bungartz, S. Zimmer, M. Buchholz, D. Pflüger. Springer, 2014
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Emerging architectures enable to boost massively parallel data mining using adaptive sparse grids
A. Heinecke, D. Pflüger. International Journal of Parallel Programming, Springer, 2012
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Spatially adaptive sparse grids for high-dimensional data-driven problems
D. Pflüger, B. Peherstorfer, H.-J. Bungartz. Journal of Complexity, 2010
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Spatially Adaptive Sparse Grids for High-Dimensional Problems
Verlag Dr. Hut, 2010
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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, D. Marcello, D. Pfander, D. Pflüger. Int. Conf. for High-Performance Computing (Supercomputing), 2019
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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, A. Barth, B. Haasdonk, W. Nowak, D. Pflüger, C. Rohde. Computational Geosciences 23 (2)
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Gradient-based optimization with B-splines on sparse grids for solving forward-dynamics simulations of three-dimensional, continuum-mechanical musculoskeletal system models.
J. Valentin, M. Sprenger, D. Pflüger, O. Röhrle International journal for numerical methods in biomedical engineering, 2018
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The scalability-efficiency/maintainability-portability trade-off in simulation software engineering: Examples and a preliminary systematic literature review.
D. Pflüger, M. Mehl, J. Valentin, F. Lindner, D. Pfander, S. Wagner, D. Graziotin, Y. Wang. SE-HPCCSE, IEEE, 2016
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Non-intrusive uncertainty quantification with sparse grids for multivariate peridynamic simulations.
F. Franzelin, P. Diehl, D. Pflüger. Meshfree Methods for Partial Differential Equations VII, 2015
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EXAHD: an exa-scalable two-level sparse grid approach for higher-dimensional problems in plasma physics and beyond.
D. Pflüger, H.-J. Bungartz, M. Griebel, F. Jenko, T. Dannert, C. Kowitz, A.P. Hinojosa, P. Zaspel. European Conference on Parallel Processing, Springer, 2014
