Dirk Pflüger

Dirk Pflüger

Member from 2015 to 2020
subject: Informatics

Universität Stuttgart
Institut für Parallele und Verteilte Systeme
Universitätsstr. 38
70569 Stuttgart

Tel.: (0711) 685-884 47
Fax: (0711) 685-704 13

Personal web page

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

  • Since 2018

    Professor for Scientific Computing, Institute for Parallel and Distributed Systems, University of Stuttgart

  • 2013

    Stand-in Professor for Simulation of Large Systems, IPVS, University of Stuttgart

  • 2012

    Junior Professor for Simulation Software Engineering, Institute for Parallel and Distributed Systems and Cluster of Excellence Simulation Technology, University of Stuttgart

  • 2011

    Research stay at the CMA, Australian National University, Canberra

  • 2010-2012

    Postdoc, Chair of Scientific Computing, Technische Universität München

  • 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

  • 2005-2010

    Research Assistant at the Chair of Scientific Computing, Technische Universität München

  • 2003

    Master Information Technology, The University of Sydney, Australia

  • 1999-2006

    Diplomstudiengang Informatik with minor music theory, University of Stuttgart


  • Modelling and Simulation: An Application-Oriented Introduction

    H.-J. Bungartz, S. Zimmer, M. Buchholz, D. Pflüger. Springer, 2014

  • Emerging architectures enable to boost massively parallel data mining using adaptive sparse grids

    A. Heinecke, D. Pflüger. International Journal of Parallel Programming, Springer, 2012

  • Spatially adaptive sparse grids for high-dimensional data-driven problems

    D. Pflüger, B. Peherstorfer, H.-J. Bungartz. Journal of Complexity, 2010

  • Spatially Adaptive Sparse Grids for High-Dimensional Problems

    Verlag Dr. Hut, 2010

  • 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

  • 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)

  • 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

  • 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

  • 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

  • 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