This image showsDirk Pflüger

Dirk Pflüger

Prof. Dr. rer. nat.

Head of Institute
IPVS
Scientific Computing

Contact

+49 711 685 88447
 +49 711 685 70413

Universitätsstraße 38
70569 Stuttgart
Deutschland
Room: 2.105

Office Hours

on appointment

  1. Thüring, T., Strack, A., Pflüger, D.: Comparing the Performance of Heterogeneous Conjugate Gradient and Cholesky Solvers on Various Hardware Using SYCL, https://arxiv.org/abs/2605.13209, (2026). https://doi.org/10.1145/3811257.3811260.
  2. Baslan, N., Kerschl, A., Schmidt, J., Pflüger, D.: Safety and Reliability : Validation for Automated Driving Functions through Scenario-Based Testing. In: 2025 IEEE Intelligent Vehicles Symposium (IV). pp. 1558–1563. IEEE (2025). https://doi.org/10.1109/IV64158.2025.11097692.
  3. Pollinger, T., Hurler, M., Craen, A.v., Obersteiner, M., Pflüger, D.: DisCoTec : Distributed higher-dimensional HPC simulations with the sparse grid combination technique. The journal of open source software. 10, 7018 (2025). https://doi.org/10.21105/joss.07018.
  4. Helmann, M., Strack, A., Pflüger, D.: Replication Data for: GPRat: Gaussian Process Regression with Asynchronous Tasks, (2025). https://doi.org/10.18419/darus-4743.
  5. Strack, A., Taylor, C., Pflüger, D.: Parallel FFTW on RISC-V: A Comparative Study Including OpenMP, MPI, and HPX. In: Neuwirth, S., Paul, A.K., Weinzierl, T., and Carson, E.C. (eds.) High Performance Computing. pp. 586–597. Springer (2025). https://doi.org/10.1007/978-3-032-07612-0_45.
  6. Strack, A., Pflüger, D.: A HPX Communication Benchmark : Distributed FFT Using Collectives. In: Caino-Lores, S., Zeinalipour, D., Doudali, T.D., Singh, D.E., Garzón, G.E.M., Sousa, L.S., Andrade, D., Cucinotta, T., D’Ambrosio, D., Diehl, P., Dolz, M.F., Jukan, A., Montella, R., Nardelli, M., Garcia-Gasulla, M., and Neuwirth, S. (eds.) Euro-Par 2024: parallel processing workshops. pp. 271–274. Springer (2025). https://doi.org/10.1007/978-3-031-90203-1_25.
  7. Strack, A., Taylor, C., Pflüger, D.: Replication Data for: Parallel FFTW on RISC-V: A Comparative Study including OpenMP, MPI, and HPX, (2025). https://doi.org/10.18419/darus-5056.
  8. Helmann, M., Strack, A., Pflüger, D.: GPRat : Gaussian Process Regression with Asynchronous Tasks. In: Diehl, P., Cao, Q., Herault, T., and Bosilca, G. (eds.) Asynchronous Many-Task Systems and Applications : Third International Workshop, WAMTA 2025, St. Louis, MO, USA, February 19-21, 2025, Proceedings. pp. 83–94. Springer (2025). https://doi.org/10.1007/978-3-031-97196-9_7.
  9. Xu, K., Schwachhofer, D., Blocklove, J., Polian, I., Domanski, P., Pflüger, D., Garg, S., Karri, R., Sinanoglu, O., Knechtel, J., Zhao, Z., Schlichtmann, U., Li, B.: Large Language Models (LLMs) for Electronic Design Automation (EDA) : Special Session Paper. In: 2025 IEEE 38th International System-on-Chip Conference (SOCC). pp. 1–6. IEEE (2025). https://doi.org/10.1109/socc66126.2025.11235352.
  10. Pollinger, T., Van Craen, A., Offenhäuser, P., Pflüger, D.: Realizing Joint Extreme-Scale Simulations on Multiple Supercomputers—Two Superfacility Case Studies. In: SC24 : International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE (2024). https://doi.org/10.1109/SC41406.2024.00104.
  11. Schwachhofer, D., Domanski, P., Becker, S., Wagner, S., Sauer, M., Pflüger, D., Polian, I.: Training Large Language Models for System-Level Test Program Generation Targeting Non-functional Properties. In: 2024 IEEE European Test Symposium (ETS). IEEE (2024). https://doi.org/10.1109/ETS61313.2024.10567741.
  12. Craen, A.v., Breyer, M., Pflüger, D.: Introduction to Parallel and Distributed Programming using N-Body Simulations. In: SC24-W : Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis. pp. 347–354. IEEE (2024). https://doi.org/10.1109/SCW63240.2024.00052.
  13. Breyer, M., Van Craen, A., Domanski, P., Pflüger, D.: hws: A Tool for Monitoring Hardware Metrics Across Diverse Vendors: A Case Study on Hyperparameter Optimization Algorithms. In: SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis. pp. 1815–1825. IEEE (2024).
  14. Bantel, L., Domanski, P., Pflüger, D.: High-Fidelity Simulation of a Cartpole for Sim-to-Real Deep Reinforcement Learning. In: 2024 4th Interdisciplinary Conference on Electrics and Computer (INTCEC). IEEE (2024). https://doi.org/10.1109/INTCEC61833.2024.10602859.
  15. Strack, A., Taylor, C., Diehl, P., Pflüger, D.: Experiences Porting Shared and Distributed Applications to Asynchronous Tasks: A Multidimensional FFT Case-study, (2024). https://doi.org/10.18419/darus-4094.
  16. Diehl, P., Daiß, G., Huck, K., Marcello, D., Shiber, S., Kaiser, H., Pflüger, D.: Simulating stellar merger using HPX/Kokkos on A64FX on Supercomputer Fugaku. The journal of supercomputing. 80, 16947–16978 (2024). https://doi.org/10.1007/s11227-024-06113-w.
  17. Schwachhofer, D., Domanski, P., Becker, S., Wagner, S., Sauer, M., Pflüger, D., Polian, I.: Large Language Model-based Optimization for System-Level Test Program Generation. In: 2024 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT). IEEE (2024). https://doi.org/10.1109/DFT63277.2024.10753556.
  18. Schwachhofer, D., Domanski, P., Becker, S., Wagner, S., Sauer, M., Pflüger, D., Polian, I.: Large Language Model-Based Optimization for System-Level Test Program Generation. In: 2024 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT). pp. 1–6. IEEE (2024).
  19. Shiber, S., De Marco, O., Motl, P.M., Munson, B., Marcello, D.C., Frank, J., Diehl, P., Clayton, G.C., Skinner, B.N., Kaiser, H., Daiß, G., Pflüger, D., Staff, J.E.: Hydrodynamic simulations of white dwarf-white dwarf mergers and the origin of R Coronae Borealis stars. Monthly notices of the Royal Astronomical Society. 535, 1914–1943 (2024). https://doi.org/10.1093/mnras/stae2343.
  20. Flemisch, B., Hermann, S., Herschel, M., Pflüger, D., Pleiss, J., Range, J., Roy, S., Takamoto, M., Uekermann, B.: Research Data Management in Simulation Science : Infrastructure, Tools, and Applications. Datenbank-Spektrum. 24, 97–105 (2024). https://doi.org/10.1007/s13222-024-00475-4.
  21. Domanski, P., Ray, A., Lafata, K., Firouzi, F., Chakrabarty, K., Pflüger, D.: Advancing blood glucose prediction with neural architecture search and deep reinforcement learning for type 1 diabetics. Biocybernetics and biomedical engineering. 44, 481–500 (2024). https://doi.org/10.1016/j.bbe.2024.07.006.
  22. Breyer, M., Van Craen, A., Pflüger, D.: Evaluation of SYCL’s Different Data Parallel Kernels. In: IWOCL ’24 : Proceedings of the 12th International Workshop on OpenCL and SYCL. p. 10. Association For Computing Machinery (2024). https://doi.org/10.1145/3648115.3648130.
  23. Breyer, M., Van Craen, A., Pflüger, D.: Short Paper: Evaluation of stdpar Compilers on a Kernel Matrix Assembly and a BLAS Level 3 SYMM Kernel. In: 2024 23rd International Symposium on Parallel and Distributed Computing (ISPDC). IEEE (2024). https://doi.org/10.1109/ISPDC62236.2024.10705404.
  24. Breyer, M., Craen, A.v., Domanski, P., Pflüger, D.: hws : A Tool for Monitoring Hardware Metrics Across Diverse Vendors : A Case Study on Hyperparameter Optimization Algorithms. In: SC24-W : Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis. pp. 1815–1825. IEEE (2024). https://doi.org/10.1109/SCW63240.2024.00228.
  25. Daiß, G., Diehl, P., Kaiser, H., Pflüger, D.: Stellar Mergers with HPX-Kokkos and SYCL : Methods of using an Asynchronous Many-Task Runtime System with SYCL. In: Proceedings of the 2023 International Workshop on OpenCL. p. 8. Association for Computing Machinery (2023). https://doi.org/10.1145/3585341.3585354.
  26. Pollinger, T., Craen, A.v., Niethammer, C., Breyer, M., Pflüger, D.: Leveraging the Compute Power of Two HPC Systems for Higher-Dimensional Grid-Based Simulations with the Widely-Distributed Sparse Grid Combination Technique. In: SC ’23 : Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. p. 84. Association for Computing Machinery (2023). https://doi.org/10.1145/3581784.3607036.
  27. Thüring, T., Breyer, M., Pflüger, D.: Comparing a Naive and a Tree-Based N-Body Algorithm using Different Standard SYCL Implementations on Various Hardware. In: SC ’23 : Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. pp. 1906–1917. Association for Computing Machinery (2023). https://doi.org/10.1145/3624062.3624604.
  28. Dresler, M., Buddeberg, E., Endesfelder, U., Haaker, J., Hof, C., Kretschmer, R., Pflüger, D., Schmidt, F.: Effective or predatory funding? Evaluating the hidden costs of grant applications. Immunology & cell biology. 101, 104–111 (2023). https://doi.org/10.1111/imcb.12592.
  29. Pollinger, T., Rentrop, J., Pflüger, D., Kormann, K.: A stable and mass-conserving sparse grid combination technique with biorthogonal hierarchical basis functions for kinetic simulations. Journal of computational physics. 491, 112338 (2023). https://doi.org/10.1016/j.jcp.2023.112338.
  30. Diehl, P., Daiß, G., Huck, K., Marcello, D., Shiber, S., Kaiser, H., Pflüger, D.: Simulating Stellar Merger using HPX/Kokkos on A64FX on Supercomputer Fugaku. In: 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). pp. 682–691. IEEE (2023). https://doi.org/10.1109/IPDPSW59300.2023.00116.
  31. Rehme, M.F., John, D.N., Schick, M., Pflüger, D.: Uncertainty Quantification for parameter estimation of an industrial electric motor using hierarchical Bayesian inversion. Mechatronics. 92, 102989 (2023). https://doi.org/10.1016/j.mechatronics.2023.102989.
  32. Strack, A., Pflüger, D.: Scalability of Gaussian Processes Using Asynchronous Tasks : A Comparison Between HPX and PETSc. In: Diehl, P., Thoman, P., Kaiser, H., and Kale, L. (eds.) Asynchronous Many-Task Systems and Applications : First International Workshop, WAMTA 2023, Baton Rouge, LA, USA, February 15-17, 2023, Proceedings. pp. 52–64. Springer (2023). https://doi.org/10.1007/978-3-031-32316-4_5.
  33. Domanski, P., Pflüger, D., Latty, R.: Learn to Tune : Robust Performance Tuning in Post-Silicon Validation. In: 2023 IEEE European Test Symposium (ETS). IEEE (2023). https://doi.org/10.1109/ETS56758.2023.10174123.
  34. Domanski, P., Ray, A., Firouzi, F., Lafata, K., Chakrabarty, K., Pflüger, D.: Blood Glucose Prediction for Type-1 Diabetics using Deep Reinforcement Learning. In: Chang, C.K., Chang, R.N., Fan, J., Fox, G.C., Jin, Z., Pravadelli, G., and Shahriar, H. (eds.) 2023 IEEE International Conference on Digital Health (ICDH). pp. 339–347. IEEE (2023). https://doi.org/10.1109/ICDH60066.2023.00042.
  35. Breyer, M., Van Craen, A., Pflüger, D.: Performance Evolution of Different SYCL Implementations based on the Parallel Least Squares Support Vector Machine Library. In: IWOCL ’23: Proceedings of the 2023 International Workshop on OpenCL. pp. 1–12. Association for Computing Machinery (2023). https://doi.org/10.1145/3585341.3585369.
  36. Davis, K., Leiteritz, R., Pflüger, D., Schulte, M.: Deep learning based surrogate modeling for thermal plume prediction of groundwater heat pumps, (2023). https://doi.org/10.48550/arXiv.2302.08199.
  37. Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D., Niepert, M.: PDEBench: An Extensive Benchmark for Scientific Machine Learning. In: 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks (2022).
  38. Daiß, G., Diehl, P., Marcello, D., Kheirkhahan, A., Kaiser, H., Pflüger, D.: From Task-Based GPU Work Aggregation to Stellar Mergers : Turning Fine-Grained CPU Tasks into Portable GPU Kernels. In: 2022 IEEE/ACM International Workshop on Performance, Portability and Productivity in HPC (P3HPC). pp. 89–99. IEEE (2022). https://doi.org/10.1109/P3HPC56579.2022.00014.
  39. Daiß, G., Singanaboina, S.Y., Diehl, P., Kaiser, H., Pflüger, D.: From Merging Frameworks to Merging Stars : Experiences using HPX, Kokkos and SIMD Types. In: 2022 IEEE/ACM 7th International Workshop on Extreme Scale Programming Models and Middleware (ESPM2). pp. 10–19. IEEE (2022). https://doi.org/10.1109/ESPM256814.2022.00007.
  40. Schober, P., Valentin, J., Pflüger, D.: Solving High-Dimensional Dynamic Portfolio Choice Models with Hierarchical B-Splines on Sparse Grids. Computational economics. 59, 185–224 (2022). https://doi.org/10.1007/s10614-020-10061-x.
  41. Amrouch, H., Anders, J., Becker, S., Betka, M., Bleher, G., Domanski, P., Elhamawy, N., Ertl, T., Gatzastras, A., Genßler, P.R., Hasler, S., Heinrich, M., Hoorn, A.v., Jafarzadeh, H., Kallfass, I., Klemme, F., Koch, S., Küsters, R., Lalama Jimenez, A.J., Latty, R., Liao, Y., Lylina, N., Najafi Haghi, Z.P., Pflüger, D., Polian, I., Rivoir, J., Sauer, M., Schwachhofer, D., Templin, S., Volmer, C., Wagner, S., Weiskopf, D., Wunderlich, H.-J., Yang, B., Zimmermann, M.: Intelligent Methods for Test and Reliability. In: 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). pp. 969–974. IEEE (2022). https://doi.org/10.23919/DATE54114.2022.9774526.
  42. Dresler, M., Buddeberg, E., Endesfelder, U., Haaker, J., Hof, C., Kretschmer, R., Pflüger, D., Schmidt, F.: Why many funding schemes harm rather than support research. Nature human behaviour. 6, 607–608 (2022). https://doi.org/10.1038/s41562-021-01286-3.
  43. Domanski, P., Pflüger, D., Rivoir, J., Latty, R.: Self-Learning Tuning for Post-Silicon Validation, (2022). https://doi.org/10.48550/arXiv.2111.08995.
  44. Leiteritz, R., Davis, K., Schulte, M., Pflüger, D.: A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps, (2022). https://doi.org/10.48550/arXiv.2203.14961.
  45. Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D., Niepert, M.: PDEBench Pretrained Models : Pretrained models for “PDEBench: An Extensive Benchmark for Scientific Machine Learning”, (2022). https://doi.org/10.18419/darus-2987.
  46. Schober, P., Valentin, J., Pflüger, D.: Correction to: Solving High-Dimensional Dynamic Portfolio Choice Models with Hierarchical B-Splines on Sparse Grids. Computational economics. 59, (2022). https://doi.org/10.1007/s10614-020-10061-x.
  47. Craen, A.v., Breyer, M., Pflüger, D.: PLSSVM : Parallel Least Squares Support Vector Machine. Software impacts. 14, 100343 (2022). https://doi.org/10.1016/j.simpa.2022.100343.
  48. Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D., Niepert, M.: PDEBENCH : an extensive benchmark for scientific machine learning. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., and Oh, A. (eds.) NIPS ’22 : Proceedings of the 36th International Conference on Neural Information Processing Systems. pp. 1596–1611. Curran Associates Inc (2022). https://doi.org/10.5555/3600270.3600387.
  49. Craen, A.v., Breyer, M., Pflüger, D.: PLSSVM : A (multi-)GPGPU-accelerated Least Squares Support Vector Machine. In: 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). pp. 818–827. IEEE (2022). https://doi.org/10.1109/IPDPSW55747.2022.00138.
  50. Leiteritz, R., Buchfink, P., Haasdonk, B., Pflüger, D.: Surrogate-data-enriched Physics-Aware Neural Networks. In: Proceedings of the Northern Lights Deep Learning Workshop 2022. pp. 1–8. Septentrio Academic Publishing (2022). https://doi.org/10.7557/18.6268.
  51. Amrouch, H., Chakrabarty, K., Pflüger, D., Polian, I., Sauer, M., Reorda, M.S.: Machine Learning for Test, Diagnosis, Post-Silicon Validation and Yield Optimization. In: Fransi, S. and Taouil, M. (eds.) 2022 IEEE European Test Symposium (ETS). IEEE (2022). https://doi.org/10.1109/ETS54262.2022.9810416.
  52. Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D., Niepert, M.: PDEBench Datasets : Data for “PDEBench: An Extensive Benchmark for Scientific Machine Learning”, (2022). https://doi.org/10.18419/darus-2986.
  53. Rehme, M.F., Franzelin, F., Pflüger, D.: B-splines on sparse grids for surrogates in uncertainty quantification. Reliability engineering & system safety. 209, 107430 (2021). https://doi.org/10.1016/j.ress.2021.107430.
  54. Holzmüller, D., Pflüger, D.: Fast Sparse Grid Operations Using the Unidirectional Principle : A Generalized and Unified Framework. In: Bungartz, H.-J., Garcke, J., and Pflüger, D. (eds.) Sparse Grids and Applications - Munich 2018. pp. 69–100. Springer (2021). https://doi.org/10.1007/978-3-030-81362-8_4.
  55. Heene, M., Pflüger, D.: 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. pp. 339–348. IOS Press (2021). https://doi.org/10.3233/978-1-61499-621-7-339.
  56. Rehme, M.F., Zimmer, S., Pflüger, D.: Hierarchical Extended B-splines for Approximations on Sparse Grids. In: Bungartz, H.-J., Garcke, J., and Pflüger, D. (eds.) Sparse Grids and Applications : Munich 2018. pp. 187–203. Springer (2021). https://doi.org/10.1007/978-3-030-81362-8_8.
  57. Domanski, P., Pflüger, D., Latty, R., Rivoir, J.: ORSA : Outlier Robust Stacked Aggregation for Best- and Worst-Case Approximations of Ensemble Systems. In: Wani, M.A. (ed.) 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). pp. 1357–1364. IEEE (2021). https://doi.org/10.1109/ICMLA52953.2021.00220.
  58. Diehl, P., Daiß, G., Marcello, D., Huck, K., Shiber, S., Kaiser, H., Frank, J., Clayton, G.C., Pflüger, D.: Octo-Tiger’s New Hydro Module and Performance Using HPX+CUDA on ORNL’s Summit. In: Mohr, B. and Liu, Z. (eds.) 2021 IEEE International Conference on Cluster Computing (CLUSTER). pp. 204–214. IEEE (2021). https://doi.org/10.1109/Cluster48925.2021.00059.
  59. Diehl, P., Marcello, D., Amini, P., Kaiser, H., Shiber, S., Clayton, G.C., Frank, J., Daiß, G., Pflüger, D., Eder, D., Koniges, A., Huck, K.: Performance Measurements Within Asynchronous Task-Based Runtime Systems : A Double White Dwarf Merger as an Application. Computing in science & engineering. 23, 73–81 (2021). https://doi.org/10.1109/MCSE.2021.3073626.
  60. Breyer, M., Daiß, G., Pflüger, D.: Performance-Portable Distributed k-Nearest Neighbors using Locality-Sensitive Hashing and SYCL. In: IWOCL′21: International Workshop on OpenCL. pp. 1–12. Association for Computing Machinery (2021). https://doi.org/10.1145/3456669.3456692.
  61. Akkermann, M., Akkaya, U.C., Dermiel, C., Pflüger, D., Dresler, M.: Sound sleep : Lullabies as a test case for the neurobiological effects of music. Behavioral and brain sciences. 44, e96 (2021). https://doi.org/10.1017/S0140525X20001259.
  62. Daiß, G., Simberg, M., Reverdell, A., Biddiscombe, J., Pollinger, T., Kaiser, H., Pflüger, D.: Beyond Fork-Join : Integration of Performance Portable Kokkos Kernels with HPX. In: 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). pp. 377–386. IEEE (2021). https://doi.org/10.1109/IPDPSW52791.2021.00066.
  63. Leiteritz, R., Hurler, M., Pflüger, D.: Learning Free-Surface Flow with Physics-Informed Neural Networks. In: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). pp. 1668–1673. IEEE (2021). https://doi.org/10.1109/ICMLA52953.2021.00266.
  64. Rehme, M.F., Pflüger, D.: Stochastic Collocation with Hierarchical Extended B-Splines on Sparse Grids. In: Fasshauer, G.E., Neamtu, M., and Schumaker, L. (eds.) Approximation Theory XVI. pp. 219–236. Springer (2021). https://doi.org/10.1007/978-3-030-57464-2_12.
  65. Hirschmann, S., Kronenburg, A., Glass, C.W., Pflüger, D.: Load-Balancing for Large-Scale Soot Particle Agglomeration Simulations. In: Foster, I., Joubert, G.R., Kučera, L., and Nagel, W.E. (eds.) Parallel Computing : Technology Trends. pp. 147–156. IOS Press (2020). https://doi.org/10.3233/APC200035.
  66. Valentin, J., Hübner, D., Stingl, M., Pflüger, D.: Gradient-Based Two-Scale Topology Optimization With B-Splines on Sparse Grids. SIAM Journal on Scientific Computing. 42, B1092–B1114 (2020). https://doi.org/10.1137/19M128822X.
  67. Endesfelder, U., Pflüger, D., Wolff, T.d.: #StopPandemicBias : scientists, share your privilege. Nature. 583, 683 (2020). https://doi.org/10.1038/d41586-020-02236-1.
  68. Wolff, T.d., Pflüger, D., Rehme, M., Heuer, J., Bittner, M.-I.: Evaluation of pool-based testing approaches to enable population-wide screening for COVID-19. PLOS ONE. 15, e0243692 (2020). https://doi.org/10.1371/journal.pone.0243692.
  69. Lago, R., Obersteiner, M., Pollinger, T., Rentrop, J., Bungartz, H.-J., Dannert, T., Griebel, M., Jenko, F., Pflüger, D.: EXAHD : A Massively Parallel Fault Tolerant Sparse Grid Approach for High-Dimensional Turbulent Plasma Simulations. In: Bungartz, H.-J., Reiz, S., Uekermann, B., Neumann, P., and Nagel, W.E. (eds.) Software for Exascale Computing : SPPEXA 2016-2019. pp. 301–329. Springer (2020). https://doi.org/10.1007/978-3-030-47956-5_11.
  70. Pollinger, T., Pflüger, D.: Learning-Based Load Balancing for Massively Parallel Simulations of Hot Fusion Plasmas. In: Foster, I., Joubert, G.R., Kučera, L., and Nagel, W.E. (eds.) Parallel Computing : Technology Trends. pp. 137–146. IOS Press (2020). https://doi.org/10.3233/APC200034.
  71. Smiljanic, M., Weeber, R., Pflüger, D., Holm, C., Kronenburg, A.: Developing coarse-grained models for agglomerate growth. In: Particle Methods in Natural Science and Engineering. pp. 1515–1527. Springer (2019). https://doi.org/10.1140/epjst/e2018-800177-y.
  72. Rehme, M.F., Pflüger, D.: Active Subspaces with B-Spline Surrogates on Sparse Grids. In: Papadrakakis, M., Papadopoulos, V., and Stefanou, G. (eds.) UNCECOMP 2019 : Uncertainty Quantification in Computational Sciences and Engineering. pp. 202–214. Institute of Structural Analysis and Antiseismic Research, School of Civil Engineering, National Technical University of Athens (2019). https://doi.org/10.7712/120219.6336.18612.
  73. Daiß, G., Amini, P., Biddiscombe, J., Diehl, P., Frank, J., Huck, K., Kaiser, H., Marcello, D., Pfander, D., Pflüger, D.: From piz daint to the stars : simulation of stellar mergers using high-level abstractions. In: SC ’19: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. pp. 1–37. Association for Computing Machinery (2019). https://doi.org/10.1145/3295500.3356221.
  74. Pfander, D., Daiß, G., Pflüger, D.: Heterogeneous Distributed Big Data Clustering on Sparse Grids. Algorithms. 12, 60 (2019). https://doi.org/10.3390/a12030060.
  75. Heller, T., Lelbach, B.A., Huck, K.A., Biddiscombe, J., Grubel, P., Koniges, A.E., Kretz, M., Marcello, D., Pfander, D., Serio, A., Frank, J., Clayton, G.C., Pflüger, D., Eder, D., Kaiser, H.: Harnessing billions of tasks for a scalable portable hydrodynamic simulation of the merger of two stars. The international journal of high performance computing applications. 33, 699–715 (2019). https://doi.org/10.1177/1094342018819744.
  76. Hirschmann, S., Glass, C.W., Pflüger, D.: Enabling unstructured domain decompositions for inhomogeneous short-range molecular dynamics in ESPResSo. In: Particle Methods in Natural Science and Engineering. pp. 1779–1788. Springer (2019). https://doi.org/10.1140/epjst/e2019-800159-0.
  77. Köppel, M., Franzelin, F., Kröker, I., Oladyshkin, S., Santin, G., Wittwar, D., Barth, A., Haasdonk, B., Nowak, W., Pflüger, D., Rohde, C.: Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario. Computational geosciences. 23, 339–354 (2019). https://doi.org/10.1007/s10596-018-9785-x.
  78. Hirschmann, S., Lahnert, M., Schober, C., Brunn, M., Mehl, M., Pflüger, D.: Load-Balancing and Spatial Adaptivity for Coarse-Grained Molecular Dynamics Applications. In: Nagel, W.E., Kröner, D.H., and Resch, M.M. (eds.) High performance computing in science and engineering ’18 : transactions of the High Performance Computing Center, Stuttgart (HLRS) 2018. pp. 409–423. Springer (2019). https://doi.org/10.1007/978-3-030-13325-2.
  79. Hinojosa, A.P., Bungartz, H.-J., Pflüger, D.: Scalable Algorithmic Detection of Silent Data Corruption for High-Dimensional PDEs. In: Garcke, J., Pflüger, D., Webster, C.G., and Zhang, G. (eds.) Sparse grids and applications - Miami 2016. pp. 93–115. Springer (2018). https://doi.org/10.1007/978-3-319-75426-0_5.
  80. Hirschmann, S., Glass, C.W., Pflüger, D.: Load-balancing and hybrid parallelization for simulations with large particle numbers. Presented at the , Thuwal, Saudi Arabia (2018).
  81. Valentin, J., Sprenger, M., Pflüger, D., Röhrle, O.: 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. 34, e2965 (2018). https://doi.org/10.1002/cnm.2965.
  82. Franzelin, F., Pflüger, D.: Limiting Ranges of Function Values of Sparse Grid Surrogates. In: Garcke, J., Pflüger, D., Webster, C.G., and Zhang, G. (eds.) Sparse grids and applications - Miami 2016. pp. 69–91. Springer (2018). https://doi.org/10.1007/978-3-319-75426-0_4.
  83. Smiljanic, M., Kronenburg, A., Weeber, R., Holm, C., Pflüger, D.: Towards a coarse-grained approach of nanoparticle agglomeration. Presented at the , Ghent, Belgium (2018).
  84. Pfander, D., Daiß, G., Marcello, D., Kaiser, H., Pflüger, D.: Accelerating Octo-Tiger : Stellar Mergers on Intel Knights Landing with HPX. In: Deakin, T. (ed.) IWOCL′18 : Proceedings of the International Workshop on OpenCL. pp. 68–75. ACM (2018). https://doi.org/10.1145/3204919.3204938.
  85. Benk, J., Pflüger, D.: Pricing multidimensional financial derivatives with stochastic volatilities using the dimensional-adaptive combination technique. The Journal of computational finance. 21, 75–104 (2017). https://doi.org/10.21314/JCF.2017.335.
  86. Brunn, M., Glass, C.W., Lahnert, M., Mehl, M., Pflüger, D.: Load balancing with p4est for short-range molecular dynamics with ESPResSo. In: Bassini, S., Danelutto, M., Dazzi, P., Joubert, G.R., and Peters, F. (eds.) Parallel computing is everywhere. pp. 455–464. IOS Press (2017). https://doi.org/10.3233/978-1-61499-843-3-455.
  87. Heene, M., Hinojosa, A.P., Bungartz, H.-J., Pflüger, D.: A Massively-Parallel, Fault-Tolerant Solver for High-Dimensional PDEs. In: Desprez, F. and et. al (eds.) Euro-Par 2016: Parallel Processing Workshops. pp. 635–647. Springer (2017). https://doi.org/10.1007/978-3-319-58943-5_51.
  88. Inci, G., Kronenburg, A., Weeber, R., Pflüger, D.: Langevin Dynamics Simulation of Transport and Aggregation of Soot Nano-particles in Turbulent Flows. Flow turbulence and combustion. 98, 1065–1085 (2017). https://doi.org/10.1007/s10494-016-9797-3.
  89. Bussler, M., Diehl, P., Pflüger, D., Frey, S., Sadlo, F., Ertl, T., Schweitzer, M.A.: Visualization of fracture progression in peridynamics. Computers & graphics. 67, 45–57 (2017). https://doi.org/10.1016/j.cag.2017.05.003.
  90. Hegland, M., Harding, B., Kowitz, C., Pflüger, D., Strazdins, P.: Recent Developments in the Theory and Application of the Sparse Grid Combination Technique. In: Bungartz, H.-J., Neumann, P., and Nagel, W.E. (eds.) Software for Exascale Computing - SPPEXA 2013-2015. pp. 143–163. Springer (2016). https://doi.org/10.1007/978-3-319-40528-5_7.
  91. Garcke, J., Pflüger, D. eds: Sparse Grids and Applications - Stuttgart 2014. Springer (2016). https://doi.org/10.1007/978-3-319-28262-6.
  92. Hupp, P., Heene, M., Jacob, R., Pflüger, D.: Global communication schemes for the numerical solution of high-dimensional PDEs. Parallel computing. 52, 78–105 (2016). https://doi.org/10.1016/j.parco.2015.12.006.
  93. Heinecke, A., Karlstetter, R., Pflüger, D., Bungartz, H.-J.: Data mining on vast data sets as a cluster system benchmark. In: Combined Special issues on GCE15 Special Issue Conference Publications and New advances in High Performance Computing and simulation: parallel and distributed systems, algorithms, and applications (HPCS 2013). pp. 2145–2165. Wiley-Blackwell (2016). https://doi.org/10.1002/cpe.3514.
  94. Hirschmann, S., Pflüger, D., Glass, C.W.: Towards Understanding Optimal Load-Balancing of Heterogeneous Short-Range Molecular Dynamics. In: 2016 IEEE 23rd International Conference on High Performance Computing Workshops (HiPCW). pp. 130–141. IEEE (2016). https://doi.org/10.1109/HiPCW.2016.027.
  95. Heene, M., Pflüger, D.: 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. pp. 339–348. IOS Press (2016). https://doi.org/10.3233/978-1-61499-621-7-339.
  96. Franzelin, F., Diehl, P., Pflüger, D.: Non-intrusive uncertainty quantification with sparse grids for multivariate peridynamic simulations. In: Griebel, M. and Schweitzer, M.A. (eds.) Meshfree methods for partial differential equations. pp. 115–143. Springer (2015).
  97. Valentin, J., Pflüger, D.: Gradient-based optimization with hierarchical B-splines on sparse grids. Presented at the , University of Bonn (2015).
  98. Peherstorfer, B., Kowitz, C., Pflüger, D., Bungartz, H.-J.: Selected Recent Applications of Sparse Grids. Numerical mathematics : theory, methods and applications. 8, 47–77 (2015). https://doi.org/10.4208/nmtma.2015.w05si.
  99. Parra Hinojosa, A., Kowitz, C., Heene, M., Pflüger, D., Bungartz, H.-J.: Towards a fault-tolerant, scalable implementation of GENE. In: Mehl, M., Bischoff, M., and Schäfer, M. (eds.) Recent trends in computational engineering-CE2014 : optimization, uncertainty, parallel algorithms, coupled and complex problems. pp. 47–65. Springer (2015). https://doi.org/10.1007/978-3-319-22997-3_3.
  100. Heene, M., Kowitz, C., Pflüger, D.: Load Balancing for Massively Parallel Computations with the Sparse Grid Combination Technique. In: Parallel Computing: Accelerating Computational Science and Engineering (CSE). pp. 574–583 (2014).
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