Publications

List of publications of the chair.

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  1. 2025

    1. B. Schembera et al., Towards a Knowledge Graph for Models and Algorithms in Applied Mathematics - Metadata and Semantic Research. MTSR 2024. Springer, 2025. doi: 10.1007/978-3-031-81974-2_8.

  2. 2024

    1. C. Homs-Pons et al., “Coupled Simulation and Parameter Inversion for Neural System and Electrophysiological Muscle Models,” GAMM-Mitteilungen, Mar. 2024, doi: 10.1002/gamm.202370009.
    2. F. Huber, P.-C. Bürkner, D. Göddeke, and M. Schulte, “Knowledge-based modeling of simulation behavior for Bayesian optimization,” Computational Mechanics, vol. 74, Art. no. 1, Jul. 2024, doi: 10.1007/s00466-023-02427-3.
    3. F. Huber, P.-C. Bürkner, D. Göddeke, and M. Schulte, “Knowledge-based modeling of simulation behavior for Bayesian optimization,” Computational Mechanics, Jan. 2024, doi: 10.1007/s00466-023-02427-3.
    4. B. Maier, D. Göddeke, F. Huber, T. Klotz, O. Röhrle, and M. Schulte, “OpenDiHu: An Efficient and Scalable Framework for Biophysical Simulations of the Neuromuscular System,” Journal of Computational Science, vol. 79, Art. no. 102291, Jul. 2024, doi: 10.1016/j.jocs.2024.102291.
    5. B. Maier, D. Göddeke, F. Huber, T. Klotz, O. Röhrle, and M. Schulte, “OpenDiHu: An Efficient and Scalable Framework for Biophysical Simulations of the Neuromuscular System,” Journal of Computational Science, vol. 79, 2024, doi: https://doi.org/10.1016/j.jocs.2024.102291.

  3. 2023

    1. N. Hornischer, “Model Order Reduction with Dynamically Transformed Modes for Electrophysiological Simulations,” GAMM Archive for Students, vol. 5, Art. no. 1, Sep. 2023, doi: 10.14464/gammas.v5i1.590.
    2. P. Strohbeck, C. Riethmüller, D. Göddeke, and I. Rybak, “Robust and efficient preconditioners for Stokes-Darcy problems,” in Finite Volumes for Complex Applications X - Volume 1, Elliptic and Parabolic Problems, E. Franck, J. Fuhrmann, V. Michel-Dansac, and L. Navoret, Eds., Springer Nature Switzerland, 2023, pp. 375–383. doi: 10.1007/978-3-031-40864-9_32.

  4. 2022

    1. E. Agullo et al., “Resiliency in numerical algorithm design for extreme scale simulations,” The International Journal of High Performance ComputingApplications, vol. 36, Art. no. 2, 2022, doi: 10.1177/10943420211055188.
    2. P. Benner et al., “Die mathematische Forschungsdateninitiative in der NFDI: MaRDI (Mathematical Research Data Initiative),” GAMM Rundbrief, vol. 2022, Art. no. 1, May 2022.
    3. T. Boege et al., “Research-Data Management Planning in the German Mathematical Community.” arXiv, 2022. doi: 10.48550/ARXIV.2211.12071.
    4. M. T. Horsch and B. Schembera, “Documentation of epistemic metadata by a mid-level ontology of cognitive processes,” Proc. JOWO 2022, 2022.
    5. K. Jung, B. Schembera, and M. Gärtner, “Best of Both Worlds? Mapping Process Metadata in Digital Humanities and Computational Engineering,” Metadata and Semantic Research, pp. 199–205, 2022, doi: 10.1007/978-3-030-98876-0_17.
    6. J. Kässinger, D. Rosin, F. Dürr, N. Hornischer, O. Röhrle, and K. Rothermel, “Persival: Simulating Complex 3D Meshes on Resource-Constrained Mobile AR Devices Using Interpolation,” in 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS), 2022, pp. 961–971. doi: 10.1109/ICDCS54860.2022.00097.
    7. B. Maier, D. Göddeke, F. Huber, T. Klotz, O. Röhrle, and M. Schulte, “OpenDiHu: An Efficient and Scalable Framework for Biophysical Simulations of the Neuromuscular System.” 2022.
    8. M. Zinßer et al., “Irradiation-dependent topology optimization of metallization grid patterns and variation of contact layer thickness used for latitude-based yield gain of thin-film solar modules,” MRS Advances, Aug. 2022, doi: 10.1557/s43580-022-00321-3.

  5. 2021

    1. M. Altenbernd, N.-A. Dreier, C. Engwer, and D. Göddeke, “Towards Local-Failure Local-Recovery in PDE Frameworks: The Case of Linear Solvers,” in High Performance Computing in Science and Engineering -- HPCSE 2019, T. Kozubek, P. Arbenz, J. Jaros, L. Ríha, J. Sístek, and P. Tichý, Eds., in Lecture Notes in Computer Science, vol. 12456. Springer, Jan. 2021, pp. 17–38. doi: 10.1007/978-3-030-67077-1_2.
    2. T. Benacchio et al., “Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction,” The International Journal of High Performance Computing Applications, vol. 35, Art. no. 4, Feb. 2021, doi: 10.1177/1094342021990433.
    3. A. Krämer et al., “Multi-physics multi-scale HPC simulations of skeletal muscles,” High Performance Computing in Science and Engineering ’20: Transactions of the High Performance Computing Center, Stuttgart(HLRS) 2020, 2021, doi: 10.1007/978-3-030-80602-6_13.
    4. J. Kühnert, D. Göddeke, and M. Herschel, “Provenance-integrated parameter selection and optimization in numerical simulations,” in 13th International Workshop on Theory and Practice ofProvenance (TaPP 2021), USENIX Association, Jul. 2021. [Online]. Available: https://www.usenix.org/conference/tapp2021/presentation/kühnert
    5. M. Osorno, M. Schirwon, N. Kijanski, R. Sivanesapillai, H. Steeb, and D. Göddeke, “A cross-platform, high-performance SPH toolkit for image-based flow simulations on the pore scale of porous media,” Computer Physics Communications, vol. 267, Art. no. 108059, Oct. 2021, doi: 10.1016/j.cpc.2021.108059.
    6. A. Rörich, T. A. Werthmann, D. Göddeke, and L. Grasedyck, “Bayesian inversion for electromyography using low-rank tensor formats,” Inverse Problems, vol. 37, Art. no. 5, Mar. 2021, doi: 10.1088/1361-6420/abd85a.
    7. J. Schmalfuss, C. Riethmüller, M. Altenbernd, K. Weishaupt, and D. Göddeke, “Partitioned coupling vs. monolithic block-preconditioning approaches for solving Stokes-Darcy systems,” in Proceedings of the International Conference on Computational Methods for Coupled Problems in Science and Engineering (COUPLED PROBLEMS), 2021. doi: 10.23967/coupled.2021.043.

  6. 2020

    1. P. Bastian et al., “Exa-Dune - Flexible PDE Solvers, Numerical Methods and Applications,” in Software for Exascale Computing -- SPPEXA 2016--2019, H.-J. Bungartz, S. Reiz, B. Uekermann, P. Neumann, and W. E. Nagel, Eds., Springer, 2020, pp. 225–269. doi: 10.1007/978-3-030-47956-5_9.
    2. M. Brehler, M. Schirwon, P. M. Krummrich, and D. Göddeke, “Simulation of Nonlinear Signal Propagation in Multimode Fibers on Multi-GPU Systems,” Communications in Nonlinear Science and Numerical Simulation, vol. 84, p. 105150, May 2020, doi: 10.1016/j.cnsns.2019.105150.
    3. L. Giraud, U. Rüde, and L. Stals, “Resiliency in Numerical Algorithm Design for Extreme Scale Simulations (Dagstuhl Seminar 20101),” Dagstuhl Reports, vol. 10, Art. no. 3, 2020, doi: 10.4230/DagRep.10.3.1.
    4. D. Göddeke, M. Schirwon, and N. Borg, “Smartphone-Apps im Mathematikstudium,” 2020, doi: 10.18419/darus-1147.
    5. R. Tielen, M. Möller, D. Göddeke, and C. Vuik, “p-multigrid methods and their comparison to h-multigrid methods in Isogeometric Analysis,” Computer Methods in Applied Mechanics and Engineering, vol. 372, p. 113347, Dec. 2020, doi: 10.1016/j.cma.2020.113347.

  7. 2018

    1. M. Altenbernd and D. Göddeke, “Soft fault detection and correction for multigrid,” The International Journal of High Performance Computing Applications, vol. 32, Art. no. 6, Nov. 2018, doi: 10.1177/1094342016684006.
    2. C. P. Bradley et al., “Enabling Detailed, Biophysics-Based Skeletal Muscle Models on HPC Systems,” Frontiers in Physiology, vol. 9, Art. no. 816, Jul. 2018, doi: 10.3389/fphys.2018.00816.
    3. M. Brehler, M. Schirwon, D. Göddeke, and P. Krummrich, “Modeling the Kerr-Nonlinearity in Mode-Division Multiplexing Fiber Transmission Systems on GPUs,” in Proceedings of Advanced Photonics 2018, Jul. 2018.
    4. C. Engwer, M. Altenbernd, N.-A. Dreier, and D. Göddeke, “A high-level C++ approach to manage local errors, asynchrony and faults in an MPI application,” in Proceedings of the 26th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2018), Mar. 2018.

  8. 2017

    1. M. Brehler, M. Schirwon, D. Göddeke, and P. M. Krummrich, “A GPU-Accelerated Fourth-Order Runge-Kutta in the Interaction Picture Method for the Simulation of Nonlinear Signal Propagation in Multimode Fibers,” Journal of Lightwave Technology, vol. 35, Art. no. 17, Sep. 2017, doi: 10.1109/JLT.2017.2715358.

  9. 2016

    1. P. Bastian et al., “Advances Concerning Multiscale Methods and Uncertainty Quantification in EXA-DUNE,” in Software for Exascale Computing -- SPPEXA 2013--2015, H.-J. Bungartz, P. Neumann, and W. E. Nagel, Eds., Springer, 2016, pp. 25–43. doi: 10.1007/978-3-319-40528-5_2.
    2. P. Bastian et al., “Hardware-Based Efficiency Advances in the EXA-DUNE Project,” in Software for Exascale Computing -- SPPEXA 2013--2015, H.-J. Bungartz, P. Neumann, and W. E. Nagel, Eds., Springer, 2016, pp. 3–23. doi: 10.1007/978-3-319-40528-5_1.
    3. M. Geveler, B. Reuter, V. Aizinger, D. Göddeke, and S. Turek, “Energy efficiency of the simulation of three-dimensional coastal ocean circulation on modern commodity and mobile processors -- A case study based on the Haswell and Cortex-A15 microarchitectures,” Computer Science -- Research and Development, vol. 31, Art. no. 4, Aug. 2016, doi: 10.1007/s00450-016-0324-5.

  10. 2015

    1. D. Göddeke, M. Altenbernd, and D. Ribbrock, “Fault-tolerant finite-element multigrid algorithms with hierarchically compressed asynchronous checkpointing,” Parallel Computing, vol. 49, pp. 117–135, 2015, doi: 10.1016/j.parco.2015.07.003.
    2. S. Müthing, D. Ribbrock, and D. Göddeke, “Integrating multi-threading and accelerators into DUNE-ISTL,” in Numerical Mathematics and Advanced Applications -- ENUMATH 2013, vol. 103, A. Abdulle, S. Deparis, D. Kressner, F. Nobile, and M. Picasso, Eds., in Lecture Notes in Computational Science and Engineering, vol. 103. , Springer, 2015, pp. 601–609. doi: 10.1007/978-3-319-10705-9_59.

  11. 2014

    1. P. Bastian et al., “EXA-DUNE: Flexible PDE Solvers, Numerical Methods and Applications,” in Euro-Par 2014: Parallel Processing Workshops, vol. 8806, L. Lopes, J. Zilinskas, A. Costan, RobertoG. Cascella, G. Kecskemeti, E. Jeannot, M. Cannataro, L. Ricci, S. Benkner, S. Petit, V. Scarano, J. Gracia, S. Hunold, StephenL. Scott, S. Lankes, C. Lengauer, J. Carretero, J. Breitbart, and M. Alexander, Eds., in Lecture Notes in Computer Science, vol. 8806. , Springer, 2014, pp. 530–541. doi: 10.1007/978-3-319-14313-2_45.
    2. D. Göddeke, D. Komatitsch, and M. Möller, “Finite and Spectral Element Methods on Unstructured Grids for Flow and Wave Propagation Methods,” in Numerical Computations with GPUs, V. Kindratenko, Ed., Springer, 2014, pp. 183–206. doi: 10.1007/978-3-319-06548-9_9.
    3. S. Müthing, P. Bastian, D. Göddeke, and D. Ribbrock, “Node-level performance engineering for an advanced density driven porous media flow solver,” in 3rd Workshop on Computational Engineering 2014, Stuttgart, Germany, Oct. 2014, pp. 109–113.

  12. 2013

    1. M. Geveler, D. Ribbrock, D. Göddeke, P. Zajac, and S. Turek, “Towards a complete FEM-based simulation toolkit on GPUs: Unstructured Grid Finite Element Geometric Multigrid solvers with strong smoothers based on Sparse Approximate Inverses,” Computers & Fluids, vol. 80, pp. 327–332, Jul. 2013, doi: 10.1016/j.compfluid.2012.01.025.
    2. D. Göddeke et al., “Energy efficiency vs. performance of the numerical solution of PDEs: an application study on a low-power ARM-based cluster,” Journal of Computational Physics, vol. 237, pp. 132–150, Mar. 2013, doi: 10.1016/j.jcp.2012.11.031.
    3. S. Turek and D. Göddeke, “Hardware-oriented Numerics for PDE,” in Encyclopedia of Applied and Computational Mathematics, B. Engquist, T. Chan, W. J. Cook, E. Hairer, J. Hastad, A. Iserles, H. P. Langtangen, C. Le Bris, P. L. Lions, C. Lubich, A. J. Majda, J. R. McLaughlin, R. M. Nieminen, J. T. Oden, P. Souganidis, and A. Tveito, Eds., Springer, 2013.

  13. 2011

    1. M. Geveler, D. Ribbrock, D. Göddeke, P. Zajac, and S. Turek, “Efficient Finite Element Geometric Multigrid Solvers for Unstructured Grids on GPUs,” in Second International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering, P. Iványi and B. H. V. Topping, Eds., Apr. 2011. doi: 10.4203/ccp.95.22.
    2. M. Geveler, D. Ribbrock, D. Göddeke, P. Zajac, and S. Turek, “Towards a complete FEM-based simulation toolkit on GPUs: Geometric multigrid solvers,” in 23rd International Conference on Parallel Computational Fluid Dynamics (ParCFD′11), May 2011.
    3. M. Geveler, D. Ribbrock, S. Mallach, D. Göddeke, and S. Turek, “A Simulation Suite for Lattice-Boltzmann based Real-Time CFD Applications Exploiting Multi-Level Parallelism on modern Multi- and Many-Core Architectures,” Journal of Computational Science, vol. 2, pp. 113–123, Jan. 2011, doi: 10.1016/j.jocs.2011.01.008.
    4. D. Göddeke and R. Strzodka, “Cyclic Reduction Tridiagonal Solvers on GPUs Applied to Mixed Precision Multigrid,” IEEE Transactions on Parallel and Distributed Systems, vol. 22, Art. no. 1, Jan. 2011, doi: 10.1109/TPDS.2010.61.

  14. 2010

    1. M. Geveler, D. Ribbrock, D. Göddeke, and S. Turek, “Lattice-Boltzmann Simulation of the Shallow-Water Equations with Fluid-Structure Interaction on Multi- and Manycore Processors,” in Facing the Multicore Challenge, vol. 6310, R. Keller, D. Kramer, and J.-P. Weiß, Eds., in Lecture Notes in Computer Science, vol. 6310. , Springer, 2010, pp. 92–104. doi: 10.1007/978-3-642-16233-6_11.
    2. D. Göddeke, “Fast and Accurate Finite-Element Multigrid Solvers for PDE Simulations on GPU Clusters,” Technische Universität Dortmund, Fakultät für Mathematik, 2010. [Online]. Available: http://hdl.handle.net/2003/27243
    3. D. Göddeke and R. Strzodka, “Mixed Precision GPU-Multigrid Solvers with Strong Smoothers,” in Scientific Computing with Multicore and Accelerators, J. Kurzak, D. A. Bader, and J. J. Dongarra, Eds., CRC Press, 2010, pp. 131–147. doi: 10.1201/b10376-11.
    4. D. Komatitsch, G. Erlebacher, D. Göddeke, and D. Michéa, “High-order finite-element seismic wave propagation modeling with MPI on a large GPU cluster,” Journal of Computational Physics, vol. 229, pp. 7692–7714, Oct. 2010, doi: 10.1016/j.jcp.2010.06.024.
    5. D. Komatitsch, D. Göddeke, G. Erlebacher, and D. Michéa, “Modeling the propagation of elastic waves using spectral elements on a cluster of 192 GPUs,” Computer Science -- Research and Development, vol. 25, Art. no. 1––2, May 2010, doi: 10.1007/s00450-010-0109-1.
    6. D. Komatitsch, Michéa, G. Erlebacher, and D. Göddeke, “Running 3D finite-difference or spectral-element wave propagation codes 25x to 50x faster using a GPU cluster,” in 72nd European Association of Geoscientists and Engineers Conference and Exhibition (EAGE′2010), Jun. 2010, pp. 2920–2924.
    7. D. Ribbrock, M. Geveler, D. Göddeke, and S. Turek, “Performance and Accuracy of Lattice-Boltzmann Kernels on Multi- and Manycore Architectures,” in International Conference on Computational Science (ICCS′10), P. M. A. Sloot, G. D. van Albada, and J. J. Dongarra, Eds., in Procedia Computer Science, vol. 1. 2010, pp. 239–247. doi: 10.1016/j.procs.2010.04.027.
    8. S. Turek, D. Göddeke, C. Becker, S. H. M. Buijssen, and H. Wobker, “FEAST -- Realisation of hardware-oriented Numerics for HPC simulations with Finite Elements,” Concurrency and Computation: Practice and Experience, vol. 22, Art. no. 6, Nov. 2010, doi: 10.1002/cpe.1584.
    9. S. Turek, D. Göddeke, S. H. M. Buijssen, and H. Wobker, “Hardware-Oriented Multigrid Finite Element Solvers on GPU-Accelerated Clusters,” in Scientific Computing with Multicore and Accelerators, J. Kurzak, D. A. Bader, and J. J. Dongarra, Eds., CRC Press, 2010, pp. 113–130. doi: 10.1201/b10376-10.

  15. 2009

    1. D. Göddeke, S. H. M. Buijssen, H. Wobker, and S. Turek, “GPU Acceleration of an Unmodified Parallel Finite Element Navier-Stokes Solver,” in High Performance Computing & Simulation 2009, W. W. Smari and J. P. McIntire, Eds., Jun. 2009, pp. 12–21. doi: 10.1109/HPCSIM.2009.5191718.
    2. D. Göddeke, H. Wobker, R. Strzodka, J. Mohd-Yusof, P. S. McCormick, and S. Turek, “Co-Processor Acceleration of an Unmodified Parallel Solid Mechanics Code with FEASTGPU,” International Journal of Computational Science and Engineering, vol. 4, Art. no. 4, Oct. 2009, doi: 10.1504/IJCSE.2009.029162.
    3. D. van Dyk, M. Geveler, S. Mallach, D. Ribbrock, D. Göddeke, and C. Gutwenger, “HONEI: A collection of libraries for numerical computations targeting multiple processor architectures,” Computer Physics Communications, vol. 180, Art. no. 12, Dec. 2009, doi: 10.1016/j.cpc.2009.04.018.

  16. 2008

    1. S. H. M. Buijssen, H. Wobker, D. Göddeke, and S. Turek, “FEASTSolid and FEASTFlow: FEM Applications Exploiting FEAST’s HPC Technologies,” in High Performance Computing in Science and Engineering ’08, vol. 2008, W. Nagel, D. Kröner, and M. Resch, Eds., in Transactions of the High Performance Computing Center, Stuttgart (HLRS), vol. 2008. , Springer, 2008, pp. 425–440. doi: 10.1007/978-3-540-88303-6_30.
    2. D. Göddeke and R. Strzodka, “Performance and accuracy of hardware-oriented native, emulated- and mixed-precision solvers in FEM simulations (Part 2: Double Precision GPUs),” Fakultät für Mathematik, Technische Universität Dortmund, Aug. 2008.
    3. D. Göddeke et al., “Using GPUs to Improve Multigrid Solver Performance on a Cluster,” International Journal of Computational Science and Engineering, vol. 4, Art. no. 1, Nov. 2008, doi: 10.1504/IJCSE.2008.021111.
    4. M. Köster, D. Göddeke, H. Wobker, and S. Turek, “How to gain speedups of 1000 on single processors with fast FEM solvers ---- Benchmarking numerical and computational efficiency,” Fakultät für Mathematik, TU Dortmund, Oct. 2008.
    5. S. Turek, D. Göddeke, C. Becker, S. H. M. Buijssen, and H. Wobker, “UCHPC -- Unconventional High-Performance Computing for Finite Element Simulations,” in International Supercomputing Conference (ISC′08), Jun. 2008.

  17. 2007

    1. D. Göddeke et al., “Exploring weak scalability for FEM calculations on a GPU-enhanced cluster,” Parallel Computing, vol. 33, Art. no. 10––11, Sep. 2007, doi: 10.1016/j.parco.2007.09.002.
    2. D. Göddeke, R. Strzodka, and S. Turek, “Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations,” International Journal of Parallel, Emergent and Distributed Systems, vol. 22, Art. no. 4, Jan. 2007, doi: 10.1080/17445760601122076.
    3. D. Göddeke, H. Wobker, R. Strzodka, J. Mohd-Yusof, P. S. McCormick, and S. Turek, “Co-processor acceleration of an unmodified parallel structural mechanics code with FEAST-GPU.” Nov. 2007.

  18. 2006

    1. D. Göddeke, C. Becker, and S. Turek, “Integrating GPUs as fast co-processors into the parallel FE package FEAST,” in 19th Symposium Simulationstechnique (ASIM′06), M. Becker and H. Szczerbicka, Eds., in Frontiers in Simulation. Sep. 2006, pp. 277–282.
    2. R. Strzodka and D. Göddeke, “Pipelined Mixed Precision Algorithms on FPGAs for Fast and Accurate PDE Solvers from Low Precision Components,” in Proceedings of the 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM′06), Apr. 2006, pp. 259–270. doi: 10.1109/FCCM.2006.57.
    3. R. Strzodka and D. Göddeke, “Mixed Precision Methods for Convergent Iterative Schemes,” in Proceedings of the Workshop on Edge Computing Using New Commodity Architectures, May 2006, pp. D–59.

  19. 2005

    1. D. Göddeke, “GPGPU--Basic Math Tutorial,” Fachbereich Mathematik, Universität Dortmund, Nov. 2005.
    2. D. Göddeke, R. Strzodka, and S. Turek, “Accelerating Double Precision FEM Simulations with GPUs,” in 18th Symposium Simulationstechnique (ASIM′05), F. Hülsemann, M. Kowarschik, and U. Rüde, Eds., in Frontiers in Simulation. Sep. 2005, pp. 139–144.
This image shows Dominik Göddeke

Dominik Göddeke

Prof. Dr. rer. nat.

Head of Institute and Head of Group

This image shows Britta Lenz

Britta Lenz

 

Secretary's Office

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