Sebastian Götschel

Sebastian Götschel

Computational Mathematics

Publications

You can also find my articles on my Google Scholar profile.

Preprints and Submitted Articles

  1. M. Witte, F. R. Lapolli, P. Freese, S. Götschel, D. Ruprecht, P. Korn, C. Kadow:Dynamic Deep Learning Based Super-Resolution For The Shallow Water EquationsarXiv:2404.06400, submitted, 2024. https://arxiv.org/abs/2404.06400
  2. J. Angel, J. Behrens, S. Götschel, M. Hollm, D. Ruprecht, R. Seifried:Bathymetry reconstruction from experimental data using PDE- constrained optimisationarXiv:2404.05556, submitted, 2024. https://arxiv.org/abs/2404.05556
    Data Code
  3. A. Q. Ibrahim, S. Götschel, D. Ruprecht: Space-time parallel scaling of Parareal with a Fourier Neural Operator as coarse propagatorarXiv:2404.02521, submitted, 2024. https://arxiv.org/abs/2404.02521
  4. P. Freese, S. Götschel, T. Lunet, D. Ruprecht, M. SchreiberParallel performance of shared memory parallel spectral deferred correctionsarXiv:2403.20135, submitted, 2024. https://arxiv.org/abs/2403.20135
  5. G. Čaklović, T. Lunet, S. Götschel, D. Ruprecht:Improving Efficiency of Parallel Across the Method Spectral Deferred CorrectionsarXiv:2403.18641, submitted, 2024. https://arxiv.org/abs/2403.18641
  6. T. Baumann, S. Götschel, T. Lunet, D. Ruprecht, R. Speck:Adaptive time step selection for Spectral Deferred CorrectionsarXiv:2403.13454, submitted, 2024. https://arxiv.org/abs/2403.13454
  7. J. Angel, S. Götschel, D. Ruprecht:Impact of spatial coarsening on Parareal convergencearXiv:2111.10228, 2021. https://doi.org/10.48550/arXiv.2111.10228

Refereed Articles

  1. I. Akramov, S. Götschel, M. Minion, D. Ruprecht, R. Speck:Spectral deferred correction methods for second-order problems SIAM J. Sci. Comput., accepted, 2024. Preprint: https://arxiv.org/abs/2310.08352
  2. C. Yang, C. Adam, S. Götschel:Complex Near-Field Measurement Using On-The-Fly Scan with In-phase and Quadrature Demodulation In: 15th German Microwave Conference (GeMiC), pp. 181-184, 2024. https://doi.org/10.23919/GeMiC59120.2024.10485338
  3. C. Yang, C. Adam, S. Götschel:Single-probe Near-field Phase Retrieval using On-The-Fly Scan and Hilbert Transform In: 2023 International Symposium on Electromagnetic Compatibility – EMC Europe, 2023. https://doi.org/10.1109/EMCEurope57790.2023.10274183
  4. A. Q. Ibrahim, S. Götschel, D. Ruprecht: Parareal with a physics-informed neural network as coarse propagator In: Cano, J., Dikaiakos, M.D., Papadopoulos, G.A., Pericàs, M., Sakellariou, R. (eds) Euro-Par 2023: Parallel Processing. Euro-Par 2023. Lecture Notes in Computer Science, vol 14100. Springer, 2023. https://doi.org/10.1007/978-3-031-39698-4_44
  5. D. Hristov, L. Mustonen, R. Von Eyben, S. Götschel, M. Minion and A. E. Kaffas:Dynamic Contrast-Enhanced Ultrasound Modeling of an Analog to Pseudo-Diffusivity in Intravoxel Incoherent Motion Magnetic Resonance ImagingIEEE Trans Med Imaging, 2022. https://doi.org/10.1109/TMI.2022.3197363
  6. S. Götschel, M. Minion, D. Ruprecht, R. Speck:Twelve Ways to Fool the Masses When Giving Parallel-in-Time ResultsIn: Ong, B., Schroder, J., Shipton, J., Friedhoff, S. (eds) Parallel-in-Time Integration Methods. PinT 2020. Springer Proceedings in Mathematics & Statistics, vol 356. Springer, 2021. https://doi.org/10.1007/978-3-030-75933-9_4
  7. S. Götschel, A. Schiela, M. Weiser:Kaskade 7 - a Flexible Finite Element ToolboxComput. Math. Appl. 81:444-458, 2021. https://doi.org/10.1016/j.camwa.2020.02.011
  8. M.-C. Weber, L. Fischer, A. Damerau, I. Ponomarev, M. Pfeiffenberger, T. Gaber, S. Götschel, J. Lang, S. Röblitz, F. Buttgereit, R. Ehrig, A. Lang:Macroscale mesenchymal condensation to study cytokine-driven cellular and matrix-related changes during cartilage degradation Biofabrication 12(4):045016, 2020. https://doi.org/10.1088/1758-5090/aba08f
  9. S. Götschel, M. Minion:An Efficient Parallel-in-Time Method for Optimization with Parabolic PDEsSIAM J. Sci. Comput. 41(6):C603-C626, 2019. https://doi.org/10.1137/19M1239313
  10. S. Götschel, M. Weiser:Compression Challenges in Large Scale Partial Differential Equation SolversAlgorithms 12(9):197, 2019. https://doi.org/10.3390/a12090197
  11. S. Götschel, M. Minion:Parallel-in-Time for Parabolic Optimal Control Problems Using PFASSTIn: Bjørstad P. et al. (eds) Domain Decomposition Methods in Science and Engineering XXIV. DD 2017. Lecture Notes in Computational Science and Engineering, vol 125., Springer, pp.363-371, 2018. https://doi.org/10.1007/978-3-319-93873-8_34
  12. L. Fischer, S. Götschel, M. Weiser:Lossy data compression reduces communication time in hybrid time-parallel integrators Comput. Vis. Sci. 19(1):19-30, 2018. https://doi.org/10.1007/s00791-018-0293-2
  13. J. P. Müller, S. Götschel, C. Maierhofer, M. Weiser:Determining the material parameters for the reconstruction of defects in carbon fiber reinforced polymers from data measured by flash thermographyAIP Conference Proceedings pp. 100006, 2017. https://doi.org/10.1063/1.4974671
  14. S. Götschel, M. Weiser:Lossy Compression for PDE-constrained Optimization: Adaptive Error ControlComput. Optim. Appl. 62(1):131-155, 2015. https://doi.org/10.1007/s10589-014-9712-6
  15. S. Götschel, C. von Tycowicz, K. Polthier, M. Weiser:Reducing Memory Requirements in Scientific Computing and Optimal Control In: T. Carraro, M. Geiger, S. Koerkel, R. Rannacher (Eds.) Multiple Shooting and Time Domain Decomposition Methods, Springer, 2015. https://doi.org/10.1007/978-3-319-23321-5_10
  16. S. Götschel, N. Chamakuri, K. Kunisch, M. Weiser:Lossy Compression in Optimal Control of Cardiac DefibrillationJ. Sci. Comput. 60(1):35-59, 2014. https://doi.org/10.1007%2Fs10915-013-9785-x
  17. S. Götschel, M. Weiser, C. Maierhofer, R. Richter, M. Röllig:Fast Defect Shape Reconstruction Based on the Travel Time in Pulse Thermography In: O. Büyüköztürk et al. (Eds.) Nondestructive Testing of Materials and Structures, RILEM Bookseries, Vol. 6, pp. 83-89, Springer, 2013. https://doi.org/10.1007/978-94-007-0723-8_11
  18. M. Weiser, S. Götschel:State Trajectory Compression for Optimal Control with Parabolic PDEsSIAM J. Sci. Comput. 34(1):A161-A184, 2012. https://doi.org/10.1137/11082172X
  19. Götschel, M. Weiser, A. Schiela:Solving Optimal Control Problems with the Kaskade 7 Finite Element ToolboxIn: A. Dedner, B. Flemisch, R. Klöfkorn (Eds.) Advances in DUNE, pp. 101-112, Springer, 2012. https://doi.org/10.1007/978-3-642-28589-9_8

Unrefereed Articles in Journals and Conference Proceedings

  1. J. P., Müller, S. Götschel, M. Weiser, C. Maierhofer:Thermografie mit optimierter Anregung für die quantitative Untersuchung von Delaminationen in kohlenstofffaserverstärkten KunststoffenIn: NDT.net Proc. DGZfP 2017, 2017. Proceedings paper
  2. S. Mitzscherling, E. Barth, S. Götschel, T. Homann, J. Prager, M. Weiser:Verbesserung und Qualifizierung der Ultraschallprüfung von Mischnähten im Primärkreis von KKWIn: NDT.net Proc. DGZfP 2017, 2017. Proceedings paper
  3. S. Götschel, C. Höhne, S. Kolkoori, S. Mitzscherling, J. Prager, M. Weiser: Ray Tracing Boundary Value Problems:Simulation and SAFT Reconstruction for Ultrasonic TestingIn: Proceedings 19th World Conference on Non-Destructive Testing (WCNDT 2016), 2016. Proceedings paper
  4. S. Götschel, C. Maierhofer, J. P. Müller, N. Rothbart, M. Weiser:Quantitative Defect Reconstruction in Active Thermography for Fiber-Reinforced CompositesIn: Proceedings 19th World Conference on Non-Destructive Testing (WCNDT 2016), 2016. Proceedings paper
  5. S. Götschel, M. Weiser, Ch. Maierhofer, R. Richter:Data Enhancement for Active Thermography In: G. Cardone (Ed.) E-book Proceedings, 11th International Conference on Quantitative Infrared Thermography, 2012. Proceedings paper
  6. S. Götschel, M. Weiser:State Trajectory Compression in Optimal ControlProc. Appl. Math. Mech. 10(1), 579-580, 2010. https://doi.org/10.1002/pamm.201010282

Theses