Computational Mathematics for Complex Simulation in Science and Engineering

Chair

Mathematical Aspects of Scientific Computing and Computational Science.

Our mission is to bridge the gap between Numerical Mathematics on the one hand, and Computer Science and Applications on the other hand. Only interdisciplinary approaches can provide a reasonable balance of provability, applicabilty and actual implementations, and this predominantly drives our research: We target both applications and foundation research. Current focus areas include, but are not limited to iterative solvers (in particular multigrid and domain decomposition methods), the development of highly efficient parallel mathematical software, inverse problems, and the realisation of numerical techniques for unconventional hardware such as GPUs.

The chair CMCS is part of the Institute for Applied Analysis and Numerical Simulation. In addition, close relations exist to SC SimTech as co-opted fellow, and PI in the Cluster of Excellence 2075. Our research is furthermore supported by the German Research Foundation (DFG) within the Priority Programme 2311 and the National Research Data Initiative  (NFDI).

Publikationsliste Mathematik

  1. 2023

    1. T. Mel’nyk and C. Rohde, “Asymptotic approximations for semilinear parabolic convection-dominated transport problems in thin graph-like networks,” arXiv e-prints, 2023. [Online]. Available: https://doi.org/10.48550/arXiv.2302.10105
    2. M. J. Gander, S. B. Lunowa, and C. Rohde, “Non-Overlapping Schwarz Waveform-Relaxation for Nonlinear Advection-Diffusion Equations,” SIAM J. Sci. Comput., vol. 45, no. 1, Art. no. 1, 2023, doi: 10.1137/21M1415005.
    3. C. T. Miller, W. G. Gray, C. E. Kees, I. Rybak, and B. J. Shepherd, “Correction to: Modelling Sediment Transport in Three-Phase Surface Water Systems,” J. Hydraul. Res., vol. 61, pp. 168–171, 2023, doi: 10.1080/00221686.2022.2107580.
    4. A. Kharitenko and C. Scherer, “Time-varying Zames–Falb multipliers for LTI Systems are superfluous,” Automatica, vol. 147, p. 110577, Jan. 2023, doi: 10.1016/j.automatica.2022.110577.
    5. S. Burbulla, M. Hörl, and C. Rohde, “Flow in Porous Media with Fractures of Varying Aperture,” Accepted by SIAM J. Sci. Comput, 2023, [Online]. Available: https://doi.org/10.48550/arXiv.2207.09301
    6. A. Jha, M. Nottoli, A. Mikhalev, C. Quan, and B. Stamm, “Linear Scaling Computation of Forces for the Domain-Decomposition Linear Poisson--Boltzmann Method,” The Journal of Chemical Physics, vol. 158, p. 104105, Feb. 2023, doi: 10.1063/5.0141025.
    7. P. Cerejeiras, M. Ferreira, U. Kähler, and J. Wirth, “Global Operator Calculus on Spin Groups,” Journal of Fourier Analysis and Applications, vol. 29, no. 3, Art. no. 3, 2023, doi: 10.1007/s00041-023-10015-5.
    8. D. Seus, F. A. Radu, and C. Rohde, “Towards hybrid two-phase modelling using linear domain decomposition,” Numer. Methods Partial Differential Equations, vol. 39, no. 1, Art. no. 1, 2023, doi: https://doi.org/10.1002/num.22906.
    9. J. Keim, A. Schwarz, S. Chiocchetti, C. Rohde, and A. Beck, “A Reinforcement Learning Based Slope Limiter for Two-Dimensional Finite Volume Schemes,” 2023, doi: 10.13140/RG.2.2.18046.87363.
    10. E. Eggenweiler, J. Nickl, and I. Rybak, “Justification of generalized interface conditions for Stokes-Darcy problems,” in Finite Volumes for Complex Applications X (accepted), in Finite Volumes for Complex Applications X (accepted). 2023.
    11. D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart, “A Framework and Benchmark for Deep Batch Active Learning for Regression,” Journal of Machine Learning Research, vol. 24, no. 164, Art. no. 164, 2023, [Online]. Available: http://jmlr.org/papers/v24/22-0937.html
    12. L. Ruan and I. Rybak, “Stokes-Brinkman-Darcy models for coupled free-flow and porous-medium systems,” in Finite Volumes for Complex Applications X (accepted), in Finite Volumes for Complex Applications X (accepted). 2023.
    13. J. Magiera and C. Rohde, “A Multiscale Method for Two-Component, Two-Phase Flow with a Neural Network Surrogate,” submitted, 2023.
    14. Y. Miao, C. Rohde, and H. Tang, “Well-posedness for a stochastic Camassa-Holm type equation with higher order nonlinearities,” accepted by Stoch. Partial Differ. Equ. Anal. Comput., 2023, [Online]. Available: https://arxiv.org/abs/2105.08607
    15. P. Gladbach, J. Jansen, and C. Lienstromberg, “Non-Newtonian thin-film equations: global existence of solutions, gradient-flow structure and guaranteed lift-off,” 2023, doi: 10.48550/ARXIV.2301.10300.
    16. M. Griesemer and M. Hofacker, “On the weakness of short-range interactions in Fermi gases,” Lett. Math. Phys., vol. 113, no. 1, Art. no. 1, 2023, doi: 10.1007/s11005-022-01624-0.
    17. P. Strohbeck, E. Eggenweiler, and I. Rybak, “A modification of the Beavers-Joseph condition for arbitrary flows to the fluid-porous interface,” Transp. Porous Med., vol. 147, no. 3, Art. no. 3, Apr. 2023, doi: 10.1007/s11242-023-01919-3.
    18. J. Keim, C.-D. Munz, and C. Rohde, “A Relaxation Model for the Non-Isothermal Navier-Stokes-Korteweg Equations in Confined Domains,” J. Comput. Phys., vol. 474, p. 111830, 2023, doi: https://doi.org/10.1016/j.jcp.2022.111830.
    19. M. J. Gander, S. B. Lunowa, and C. Rohde, “Consistent and Asymptotic-Preserving Finite-Volume Robin Transmission Conditions for Singularly Perturbed Elliptic Equations,” in Domain Decomposition Methods in Science and Engineering XXVI, S. C. Brenner, E. Chung, A. Klawonn, F. Kwok, J. Xu, and J. Zou, Eds., in Domain Decomposition Methods in Science and Engineering XXVI. Cham: Springer International Publishing, 2023, pp. 443--450.
    20. S. Burbulla, L. Formaggia, C. Rohde, and A. Scotti, “Modeling fracture propagation in poro-elastic media combining phase-field and discrete fracture models,” Comput. Methods Appl. Mech. Engrg., vol. 403, 2023, doi: https://doi.org/10.1016/j.cma.2022.115699.
    21. M. M. Morato, T. Holicki, and C. W. Scherer, “Stabilizing Model Predictive Control Synthesis using Integral Quadratic Constraints and Full-Block Multipliers,” 2023, doi: 10.48550/ARXIV.2210.03712.
    22. B. N. Hahn, G. Rigaud, and R. Schmähl, “A class of regularizations based on nonlinear isotropic diffusion for inverse problems,” IMA Journal of Numerical Analysis, Feb. 2023, doi: 10.1093/imanum/drad002.
    23. N. Hornischer, “Model Order Reduction with Dynamically Transformed Modes for Electrophysiological Simulations,” GAMM Archive for Students, 2023.
    24. P. Strohbeck, C. Riethmüller, D. Göddeke, and I. Rybak, “Robust and efficient preconditioners for Stokes-Darcy problems,” in Finite Volumes for Complex Applications X (accepted), in Finite Volumes for Complex Applications X (accepted). 2023.
  2. 2022

    1. R. Merkle and A. Barth, “Subordinated Gaussian Random Fields in Elliptic Partial Differential Equations,” Stoch PDE: Anal Comp, 2022, [Online]. Available: https://doi.org/10.1007/s40072-022-00246-w
    2. M. Klink, “Time Error Estimators and Adaptive Time-stepping Schemes,” bathesis, 2022.
    3. N. Hornischer, “Model Order Reduction with Transformed Modes for Electrophysiological Simulations,” bathesis, 2022.
    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.” 2022.
    5. S. Burbulla, A. Dedner, M. Hörl, and C. Rohde, “Dune-MMesh: The Dune Grid Module for Moving Interfaces,” J. Open Source Softw., vol. 7, no. 74, Art. no. 74, 2022, doi: 10.21105/joss.03959.
    6. J. Magiera and C. Rohde, “A molecular–continuum multiscale model for inviscid liquid–vapor flow with sharp interfaces,” J. Comput. Phys., p. 111551, 2022, doi: https://doi.org/10.1016/j.jcp.2022.111551.
    7. E. Eggenweiler, M. Discacciati, and I. Rybak, “Analysis of the Stokes-Darcy problem with generalised interface conditions,” ESAIM Math. Model. Numer. Anal., vol. 56, pp. 727–742, 2022, doi: 10.1051/m2an/2022025.
    8. G. Schneider and M. Winter, “The amplitude system for a simultaneous short-wave Turing  and long-wave Hopf instability,” Discrete Contin. Dyn. Syst. Ser. S, vol. 15, no. 9, Art. no. 9, 2022, doi: 10.3934/dcdss.2021119.
    9. G. C. Hsiao, T. Sánchez-Vizuet, and W. L. Wendland, “A Boundary-Field Formulation for Elastodynamic Scattering,” Journal of Elasticity, 2022, doi: https://doi.org/10.1007/s10659-022-09964-7.
    10. L. von Wolff and I. S. Pop, “Upscaling of a Cahn–Hilliard Navier–Stokes model with precipitation and dissolution in a thin strip,” Journal of Fluid Mechanics, vol. 941, pp. A49--, 2022, doi: DOI: 10.1017/jfm.2022.308.
    11. T. Wenzel, G. Santin, and B. Haasdonk, “Stability of convergence rates: Kernel interpolation on non-Lipschitz domains.” arXiv, 2022. doi: 10.48550/ARXIV.2203.12532.
    12. J. Rettberg et al., “Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar.” 2022. doi: https://doi.org/10.48550/arXiv.2203.10061.
    13. B. Haasdonk, H. Kleikamp, M. Ohlberger, F. Schindler, and T. Wenzel, “A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs.” arXiv, 2022. doi: 10.48550/ARXIV.2204.13454.
    14. T. Boege et al., “Research-Data Management Planning in the German Mathematical Community.” arXiv, 2022. doi: 10.48550/ARXIV.2211.12071.
    15. D. Hägele et al., “Uncertainty Visualization: Fundamentals and Recent Developments,” it - Information Technology, vol. 64, no. 4–5, Art. no. 4–5, 2022, doi: 10.1515/itit-2022-0033.
    16. 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.
    17. D. Gramlich, C. Ebenbauer, and C. W. Scherer, “Synthesis of Accelerated Gradient Algorithms for Optimization and Saddle Point Problems using Lyapunov functions,” Syst. Control Lett., vol. 165, 2022, [Online]. Available: https://arxiv.org/abs/2006.09946
    18. D. Gramlich, C. W. Scherer, and C. Ebenbauer, “Robust Differential Dynamic Programming,” in 61st IEEE Conf. Decision and Control, in 61st IEEE Conf. Decision and Control. 2022. doi: 10.1109/cdc51059.2022.9992569.
    19. C. Scherer, “Dissipativity and Integral Quadratic Constraints, Tailored computational robustness tests for complex interconnections,” IEEE Control Systems Magazine, vol. 42, no. 3, Art. no. 3, 2022, [Online]. Available: https://arxiv.org/abs/2105.07401
    20. J. Berberich, C. W. Scherer, and F. Allgower, “Combining Prior Knowledge and Data for Robust Controller Design,” IEEE Transactions on Automatic Control, pp. 1--16, 2022, doi: 10.1109/tac.2022.3209342.
    21. T. Holicki and C. W. Scherer, “IQC Based Analysis and Estimator Design for Discrete-Time Systems Affected by Impulsive Uncertainties,” Dec. 2022.
    22. E. Agullo et al., “Resiliency in numerical algorithm design for extreme scale simulations,” The International Journal of High Performance ComputingApplications, vol. 36, no. 2, Art. no. 2, 2022, doi: 10.1177/10943420211055188.
    23. P. Gavrilenko et al., “A Full Order, Reduced Order and Machine Learning Model Pipeline for Efficient Prediction of Reactive Flows,” in Large-Scale Scientific Computing, I. Lirkov and S. Margenov, Eds., in Large-Scale Scientific Computing. Cham: Springer International Publishing, 2022, pp. 378--386.
    24. S. Shuva, P. Buchfink, O. Röhrle, and B. Haasdonk, “Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue,” in Large-Scale Scientific Computing, I. Lirkov and S. Margenov, Eds., in Large-Scale Scientific Computing. Springer International Publishing, 2022, pp. 402--409.
    25. J. Magiera and C. Rohde, “Analysis and Numerics of Sharp and Diffuse Interface Models for Droplet Dynamics,” in Droplet Dynamics under Extreme Ambient Conditions, K. Schulte, C. Tropea, and B. Weigand, Eds., in Droplet Dynamics under Extreme Ambient Conditions. Springer International Publishing, 2022. doi: 10.1007/978-3-031-09008-0_4.
    26. T. Holicki, “A Complete Analysis and Design Framework for Linear Impulsive and Related Hybrid Systems,” University of Stuttgart, 2022. doi: 10.18419/opus-12158.
    27. V. Zaverkin, D. Holzmüller, R. Schuldt, and J. Kästner, “Predicting properties of periodic systems from cluster data: A case study of liquid water,” The Journal of Chemical Physics, vol. 156, no. 11, Art. no. 11, 2022, doi: 10.1063/5.0078983.
    28. R. Frank, A. Laptev, and T. Weidl, Schrödinger Operators: Eigenvalues and Lieb–Thirring Inequalities. in Cambridge Studies in Advanced Mathematics. 2022, p. 512.
    29. M. T. Horsch and B. Schembera, “Documentation of epistemic metadata by a mid-level ontology of cognitive processes,” Proc. JOWO 2022, 2022.
    30. 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.
    31. C. A. Rösinger and C. W. Scherer, “Gain-Scheduling Controller Synthesis for Networked Systems with Full Block Scalings,” Oct. 2022.
    32. C. Beschle and B. Kovács, “Stability and error estimates for non-linear Cahn–Hilliard-type equations on evolving surfaces,” Numerische Mathematik, pp. 1--48, 2022, doi: 10.1007/s00211-022-01280-5.
    33. R. Merkle and A. Barth, “On some distributional properties of subordinated Gaussian random fields,” Methodol Comput Appl Probab, 2022.
    34. S. Burbulla and C. Rohde, “A finite-volume moving-mesh method for two-phase flow in fracturing porous media,” J. Comput. Phys., p. 111031, 2022, doi: https://doi.org/10.1016/j.jcp.2022.111031.
    35. T. Wenzel, M. Kurz, A. Beck, G. Santin, and B. Haasdonk, “Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows,” in Large-Scale Scientific Computing, I. Lirkov and S. Margenov, Eds., in Large-Scale Scientific Computing. Cham: Springer International Publishing, 2022, pp. 410--418.
    36. P. Benner et al., “Die mathematische Forschungsdateninitiative in der NFDI:  MaRDI (Mathematical Research Data Initiative),” GAMM Rundbrief, vol. 2022, no. 1, Art. no. 1, May 2022.
    37. R. L. Frank, A. Laptev, and T. Weidl, “An improved one-dimensional Hardy inequality,” J. Math. Sci. (N.Y.), vol. 268, no. 3, Problems in mathematical analysis. No. 118, Art. no. 3, Problems in mathematical analysis. No. 118, 2022, doi: 10.1007/s10958-022-06199-8.
    38. F. Mohammadi et al., “A Surrogate-Assisted Uncertainty-Aware Bayesian Validation Framework and its Application to Coupling Free Flow and Porous-Medium Flow,” Comput. Geosci. (submitted), 2022, [Online]. Available: https://arxiv.org/abs/2106.13639
    39. E. Eggenweiler, “Interface conditions for arbitrary flows in Stokes-Darcy systems : derivation, analysis and validation.” Universität Stuttgart, 2022. doi: 10.18419/OPUS-12573.
    40. M. Kohr, S. E. Mikhailov, and W. L. Wendland, “On some mixed-transmission problems for the anisotropic Stokes and Navier-Stokes systems in Lipschitz domains with transversal interfaces,” JMAA, vol. 516, no. 1, 126464, Art. no. 1, 126464, 2022, [Online]. Available: https://doi.org/10.1016/j.jmaa.2022.126464
    41. A. Kharitenko and C. W. Scherer, “On the exactness of a stability test for Lur’e systems with slope-restricted nonlinearities,” Oct. 2022.
    42. C. Lienstromberg, T. Pernas-Casta\ no, and J. J. L. Velázquez, “Analysis of a two-fluid Taylor-Couette flow with one              non-Newtonian fluid,” J. Nonlinear Sci., vol. 32, no. 2, Art. no. 2, 2022, doi: 10.1007/s00332-021-09750-0.
    43. F. Massa, L. Ostrowski, F. Bassi, and C. Rohde, “An artificial Equation of State based Riemann solver for a discontinuous Galerkin discretization of the incompressible Navier–Stokes equations,” J. Comput. Phys., p. 110705, 2022, doi: https://doi.org/10.1016/j.jcp.2021.110705.
    44. C. Beschle, “Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5,” 2022, doi: 10.18419/darus-3154.
    45. M. Griesemer and M. Hofacker, “From Short-Range to Contact Interactions in Two-dimensional Many-Body Quantum Systems,” Annales Henri Poincaré, vol. 23, no. 8, Art. no. 8, Aug. 2022, doi: 10.1007/s00023-021-01149-7.
    46. M. Griesemer, “Ground states of atoms and molecules in non-relativistic QED,” in The Physics and Mathematics of Elliott Lieb, in The Physics and Mathematics of Elliott Lieb. EMS Press, 2022, pp. 437--450. doi: 10.4171/90-1/18.
    47. B. Hilder and U. Sharma, “Quantitative coarse-graining of Markov chains.” 2022.
    48. J. Jansen, C. Lienstromberg, and K. Nik, “Long-time behaviour and stability for quasilinear doubly degenerate parabolic equations of higher order.” arXiv, 2022. doi: 10.48550/ARXIV.2204.08231.
    49. O. Assenmacher, G. Bruell, and C. Lienstromberg, “Non-Newtonian two-phase thin-film problem: local existence,              uniqueness, and stability,” Comm. Partial Differential Equations, vol. 47, no. 1, Art. no. 1, 2022, doi: 10.1080/03605302.2021.1957929.
    50. I. Kröker, S. Oladyshkin, and I. Rybak, “Global sensitivity analysis using multi-resolution polynomial chaos expansion for coupled Stokes-Darcy flow problems,” Comput. Geosci. (submitted), 2022, doi: 10.21203/rs.3.rs-1742793/v1.
    51. T. Holicki and C. W. Scherer, “Input-Output-Data-Enhanced Robust Analysis via Lifting,” Nov. 2022.
    52. G. Santin, T. Karvonen, and B. Haasdonk, “Sampling based approximation of linear functionals in reproducing kernel Hilbert spaces,” BIT - numerical mathematics, vol. 62, no. 1, Art. no. 1, 2022, doi: 10.1007/s10543-021-00870-3.
    53. V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, “Exploring chemical and conformational spaces by batch mode deep active learning,” Digital Discovery, vol. 1, pp. 605–620, 2022, doi: 10.1039/D₂DD00034B.
    54. R. Merkle and A. Barth, “Multilevel Monte Carlo estimators for elliptic PDEs with Lévy-type diffusion coefficient,” BIT Numer Math, 2022, [Online]. Available: https://doi.org/10.1007/s10543-022-00912-4
    55. T. Wenzel, G. Santin, and B. Haasdonk, “Analysis of Target Data-Dependent Greedy Kernel Algorithms: Convergence Rates for f-, \$\$f \backslashcdot P\$\$- and f/P-Greedy,” Constructive Approximation, Oct. 2022, doi: 10.1007/s00365-022-09592-3.
    56. L. Mehl, C. Beschle, A. Barth, and A. Bruhn, “Replication Data for: An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation,” 2022, doi: 10.18419/darus-2890.
    57. J. Wirth and M. E. Sebih, “On a wave equation with singular dissipation,” Mathematische Nachrichten, vol. 295, no. 8, Art. no. 8, 2022, doi: 10.1002/mana.202000076.
    58. T. Mel’nyk and C. Rohde, “Asymptotic expansion for convection-dominated transport in a thin graph-like junction,” arXiv e-prints, 2022. doi: 10.48550/ARXIV.2208.05812.
    59. B. Stamm and L. Theisen, “A Quasi-Optimal Factorization Preconditioner for Periodic Schrödinger Eigenstates in Anisotropically Expanding Domains,” SIAM Journal on Numerical Analysis, vol. 60, no. 5, Art. no. 5, Sep. 2022, doi: 10.1137/21m1456005.
    60. P. Buchfinck, S. Glas, and B. Haasdonk, “Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems,” 2022.
    61. T. Holicki and C. W. Scherer, “A Dynamic S-Procedure for Dynamic Uncertainties,” in IFAC-PapersOnline, in IFAC-PapersOnline, vol. 55. 2022, pp. 103–108. doi: 10.1016/j.ifacol.2022.09.331.
    62. B. Hilder, “Modulating traveling fronts in a dispersive Swift-Hohenberg equation coupled to an additional conservation law,” J. Math. Anal. Appl., vol. 513, no. 2, Art. no. 2, 2022, doi: 10.1016/j.jmaa.2022.126224.
    63. C. Lienstromberg, S. Schiffer, and R. Schubert, “A data-driven approach to viscous fluid mechanics -- the stationary case,” 2022, doi: 10.48550/ARXIV.2207.00324.
    64. C. Lienstromberg and J. J. L. Velázquez, “Long-time asymptotics and regularity estimates for weak solutions to a doubly degenerate thin-film equation in the Taylor-Couette setting.” arXiv, 2022. doi: 10.48550/ARXIV.2203.00075.
    65. M. Kohr, S. E. Mikhailov, and W. L. Wendland, “Non-homogeneous Dirichlet-transmission problems for the anisotropic Stokes and Navier-Stokes systems in Lipschitz domains with transversal interfaces,” Calc. Var. Partial Differential Equations, vol. 61, p. Paper No. 198 (2022) 47 pp., 2022.
    66. B. N. Hahn, M.-L. K. Garrido, C. Klingenberg, and S. Warnecke, “Using the Navier-Cauchy equation for motion estimation in dynamic imaging,” Inverse Problems and Imaging, vol. 0, no. 0, Art. no. 0, 2022, doi: 10.3934/ipi.2022018.
    67. C. W. Scherer, “Dissipativity, Convexity and Tight O\textquotesingleShea-Zames-Falb Multipliers for Safety Guarantees,” IFAC-PapersOnLine, vol. 55, no. 30, Art. no. 30, 2022, doi: 10.1016/j.ifacol.2022.11.044.
    68. C. Fiedler, C. W. Scherer, and S. Trimpe, “Learning Functions and Uncertainty Sets Using Geometrically Constrained Kernel Regression,” in 61st IEEE Conf. Decision and Control, in 61st IEEE Conf. Decision and Control. IEEE, Dec. 2022. doi: 10.1109/cdc51059.2022.9993144.
  3. 2021

    1. D. Wittwar and B. Haasdonk, “Convergence rates for matrix P-greedy variants,” in Numerical mathematics and advanced applications---ENUMATH              2019, in Numerical mathematics and advanced applications---ENUMATH              2019, vol. 139. Springer, Cham, pp. 1195--1203. doi: 10.1007/978-3-030-55874-1\_119.
    2. I. Rybak, C. Schwarzmeier, E. Eggenweiler, and U. Rüde, “Validation and calibration of coupled porous-medium and free-flow problems using pore-scale resolved models,” Comput. Geosci., vol. 25, pp. 621–635, 2021, doi: 10.1007/s10596-020-09994-x.
    3. A. Rörich, T. A. Werthmann, D. Göddeke, and L. Grasedyck, “Bayesian inversion for electromyography using low-rank tensor formats,” Inverse Problems, vol. 37, no. 5, Art. no. 5, Mar. 2021, doi: 10.1088/1361-6420/abd85a.
    4. 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 High Performance Computing in Science and Engineering -- HPCSE 2019, vol. 12456. Springer, Jan. 2021, pp. 17--38. doi: 10.1007/978-3-030-67077-1_2.
    5. D. Alonso-Orán, C. Rohde, and H. Tang, “A local-in-time theory for singular SDEs with applications to fluid models with transport noise,” J. Nonlinear Sci., vol. 31, no. 6, Art. no. 6, 2021, doi: doi.org/10.1007/s00332-021-09755-9.
    6. T. Jentsch and G. Weingart, “Jacobi relations on naturally reductive spaces,” ANNALS OF GLOBAL ANALYSIS AND GEOMETRY, vol. 59, no. 1, Art. no. 1, Feb. 2021, doi: 10.1007/s10455-020-09740-7.
    7. U. Freiberg and S. Kohl, “Box dimension of fractal attractors and their numerical computation,” COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, vol. 95, Apr. 2021, doi: 10.1016/j.cnsns.2020.105615.
    8. C. Rohde and H. Tang, “On the stochastic Dullin-Gottwald-Holm equation: global existence and wave-breaking phenomena,” NoDEA Nonlinear Differential Equations Appl., vol. 28, no. 1, Art. no. 1, 2021, doi: 10.1007/s00030-020-00661-9.
    9. I. Steinwart and S. Fischer, “A Closer Look at Covering Number Bounds for Gaussian Kernels,” J. Complexity, vol. 62, p. 101513, 2021, doi: 10.1016/j.jco.2020.101513.
    10. I. Steinwart and J. F. Ziegel, “Strictly proper kernel scores and characteristic kernels on compact spaces,” Appl. Comput. Harmon. Anal., vol. 51, pp. 510--542, 2021, doi: 10.1016/j.acha.2019.11.005.
    11. J. Magiera, “A Molecular--Continuum Multiscale Solver for Liquid--Vapor Flow,” in Small Collaboration: Advanced Numerical Methods for Nonlinear Hyperbolic Balance Laws and Their Applications (hybrid meeting), in Small Collaboration: Advanced Numerical Methods for Nonlinear Hyperbolic Balance Laws and Their Applications (hybrid meeting), vol. 41. 2021. doi: 10.14760/OWR-2021-41.
    12. M. Nonnenmacher, D. Reeb, and I. Steinwart, “Which Minimizer Does My Neural Network Converge To?,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, N. Oliver, F. Pérez-Cruz, S. Kramer, J. Read, and J. A. Lozano, Eds., in Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham: Springer International Publishing, 2021, pp. 87--102. doi: https://doi.org/10.1007/978-3-030-86523-8_6.
    13. M. Kohr, S. E. Mikhailov, and W. L. Wendland, “Layer potential theory for the anisotropic Stokes system with variable L∞ symmetrically elliptic tensor coefficient,” Math. Methods Appl. Sci., vol. 44, no. 12, Art. no. 12, 2021, doi: 10.1002/mma.7167.
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    18. G. Santin and B. Haasdonk, “Kernel methods for surrogate modeling,” in Model Order Reduction, P. Benner, W. Schilders, S. Grivet-Talocia, A. Quarteroni, G. Rozza, and L. M. Silveira, Eds., in Model Order Reduction, vol. 1: System-and Data-Driven Methods and Algorithms. de Gruyter, 2021, pp. 311–354.
    19. B. Haasdonk, T. Wenzel, G. Santin, and S. Schmitt, “Biomechanical Surrogate Modelling Using Stabilized Vectorial Greedy Kernel Methods,” 2021.
    20. T. Wenzel, G. Santin, and B. Haasdonk, “A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability and uniform point distribution,” 2021.
    21. L. Brencher and A. Barth, “Stochastic conservation laws with discontinuous flux functions: The multidimensional case,” 2021.
    22. P. Buchfink, S. Glas, and B. Haasdonk, “Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds.” 2021. doi: https://doi.org/10.48550/arXiv.2112.10815.
    23. T. Ehring and B. Haasdonk, “Greedy sampling and approximation for realizing feedback control for high dimensional nonlinear systems,” 2021.
    24. T. B. Berrett, L. Gyorfi, and H. Walk, “Strongly universally consistent nonparametric regression and    classification with privatised data,” ELECTRONIC JOURNAL OF STATISTICS, vol. 15, no. 1, Art. no. 1, 2021, doi: 10.1214/21-EJS1845.
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    26. F. Echterdiek, D. Kitterer, J. Dippon, G. Paul, V. Schwenger, and J. Latus, “Impact of cardiopulmonary resuscitation on outcome of kidney transplantations from braindead donors aged ≥65 years.,” Clin Transplant., vol. 2021 Aug 13:, p. e14452, 2021, doi: 10.1111/ctr.14452.
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    33. T. Holicki and C. W. Scherer, “Robust Gain-Scheduled Estimation with Dynamic D-Scalings,” IEEE Trans. Autom. Control, 2021, doi: 10.1109/TAC.2021.3052751.
    34. J. Dürrwächter, F. Meyer, T. Kuhn, A. Beck, C.-D. Munz, and C. Rohde, “A high-order stochastic Galerkin code for the compressible Euler and Navier-Stokes equations,” Computers & Fluids, vol. 228, pp. 1850044, 20, 2021, doi: 10.1016/j.compfluid.2021.105039.
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    53. M. Kohr, S. E. Mikhailov, and W. L. Wendland, “Dirichlet and transmission problems for anisotropic Stokes and Navier-Stokes systems with L∞ tensor coefficient under relaxed ellipticity condition,” Discrete Contin. Dyn. Syst., vol. 41, no. 9, Art. no. 9, 2021, doi: 10.3934/dcds.2021042.
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    55. V. Zaverkin, J. Kästner, D. Holzmüller, and I. Steinwart, “Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,” J. Chem. Theory Comput., 2021, doi: https://doi.org/10.1021/acs.jctc.1c00527.
    56. A. Beck, J. Dürrwächter, T. Kuhn, F. Meyer, C.-D. Munz, and C. Rohde, “Uncertainty Quantification in High Performance Computational Fluid Dynamics,” in High Performance Computing in Science and Engineering ’19, W. E. Nagel, D. H. Kröner, and M. M. Resch, Eds., in High Performance Computing in Science and Engineering ’19. Cham: Springer International Publishing, 2021, pp. 355--371.
    57. C. Rohde and H. Tang, “On a stochastic Camassa-Holm type equation with higher order nonlinearities,” J. Dynam. Differential Equations, vol. 33, pp. 1823–1852, 2021, doi: https://doi.org/10.1007/s10884-020-09872-1.
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    60. T. Hamm and I. Steinwart, “Intrinsic Dimension Adaptive Partitioning for Kernel Methods,” Fakultät für Mathematik und Physik, Universität Stuttgart, 2021.
    61. B. de Rijk and B. Sandstede, “Diffusive stability against nonlocalized perturbations of              planar wave trains in reaction-diffusion systems,” J. Differential Equations, vol. 274, pp. 1223--1261, 2021, doi: 10.1016/j.jde.2020.10.027.
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    63. R. Cleyton, A. Moroianu, and U. Semmelmann, “Metric connections with parallel skew-symmetric torsion,” Adv. Math., vol. 378, pp. 107519, 50, 2021, doi: 10.1016/j.aim.2020.107519.
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    66. B. N. Hahn, “Motion compensation strategies in tomography,” 2021, doi: 10.1007/978-3-030-57784-1_3.
    67. S. Michalowsky, C. Scherer, and C. Ebenbauer, “Robust and structure exploiting optimisation algorithms: An integral quadratic constraint approach,” International Journal of Control, vol. 94, no. 11, Art. no. 11, 2021, doi: 10.1080/00207179.2020.1745286.
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    72. H. Hang and I. Steinwart, “Optimal Learning with Anisotropic Gaussian SVMs,” Appl. Comput. Harmon. Anal., no. 55, Art. no. 55, 2021, doi: http://doi.org/10.1016/j.acha.2021.06.004.
    73. P. Buchfink and B. Haasdonk, “Experimental Comparison of Symplectic and Non-symplectic Model Order Reduction an Uncertainty Quantification Problem,” in Numerical Mathematics and Advanced Applications ENUMATH 2019, F. J. Vermolen and C. Vuik, Eds., in Numerical Mathematics and Advanced Applications ENUMATH 2019, vol. 139. Springer International Publishing, 2021. doi: 10.1007/978-3-030-55874-1.
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  4. 2020

    1. T. Holicki and C. W. Scherer, “Output-Feedback Synthesis for a Class of Aperiodic Impulsive Systems,” in IFAC-PapersOnline, in IFAC-PapersOnline, vol. 53. 2020, pp. 7299–7304. doi: 10.1016/j.ifacol.2020.12.981.
    2. J. Giesselmann, F. Meyer, and C. Rohde, “An a posteriori error analysis based on non-intrusive spectral projections for systems of random conservation laws,” in Hyperbolic Problems: Theory, Numerics, Applications. Proceedings of the Seventeenth International Conference on Hyperbolic Problems 2018, A. Bressan, M. Lewicka, D. Wang, and Y. Zheng, Eds., in Hyperbolic Problems: Theory, Numerics, Applications. Proceedings of the Seventeenth International Conference on Hyperbolic Problems 2018, vol. 10. AIMS Series on Applied Mathematics, 2020, pp. 449–456. [Online]. Available: https://www.aimsciences.org/fileAIMS/cms/news/info/upload//c0904f1f-97d5-451f-b068-25f1612b6852.pdf
    3. S. Burbulla and C. Rohde, “A fully conforming finite volume approach to two-phase flow in fractured porous media,” in Finite Volumes for Complex Applications IX - Methods, Theoretical Aspects, Examples, R. Klöfkorn, E. Keilegavlen, F. A. Radu, and J. Fuhrmann, Eds., in Finite Volumes for Complex Applications IX - Methods, Theoretical Aspects, Examples. Cham: Springer International Publishing, 2020, pp. 547–555. doi: https://doi.org/10.1007/978-3-030-43651-3_51.
    4. A. Armiti-Juber and C. Rohde, “On the well-posedness of a nonlinear fourth-order extension of Richards’ equation,” J. Math. Anal. Appl., vol. 487, no. 2, Art. no. 2, 2020, doi: https://doi.org/10.1016/j.jmaa.2020.124005.
    5. T. Haas and G. Schneider, “Failure of the N-wave interaction approximation without imposing    periodic boundary conditions,” ZAMM-ZEITSCHRIFT FUR ANGEWANDTE MATHEMATIK UND MECHANIK, vol. 100, no. 6, Art. no. 6, Jun. 2020, doi: 10.1002/zamm.201900230.
    6. M. Oesting and A. Schnurr, “Ordinal patterns in clusters of subsequent extremes of regularly varying time series,” Extremes, vol. 23, no. 4, Art. no. 4, 2020, doi: 10.1007/s10687-020-00391-2.
    7. M. Geck, “Green functions and Glauberman degree-divisibility,” Annals of Mathematics, vol. 192, no. 1, Art. no. 1, 2020, doi: 10.4007/annals.2020.192.1.4.
    8. S. E. Blanke, B. N. Hahn, and A. Wald, “Inverse problems with inexact forward operator: iterative regularization and application in dynamic imaging,” Inverse Problems, vol. 36, no. 12, Art. no. 12, 2020, doi: 10.1088/1361-6420/abb5e1.
    9. D. E. Pelinovsky and G. Schneider, “The monoatomic FPU system as a limit of a diatomic FPU system,” Appl. Math. Lett., vol. 107, p. 7, 2020.
    10. B. de Rijk and G. Schneider, “Global Existence and Decay in Nonlinearly Coupled Reaction-Diffusion-Advection Equations with Different Velocities,” J. Differential Equations, vol. 268, no. 7, Art. no. 7, 2020, doi: 10.1016/j.jde.2019.09.056.
    11. A. P. Polyakova, I. E. Svetov, and B. N. Hahn, “The Singular Value Decomposition of the Operators of the Dynamic Ray Transforms Acting on 2D Vector Fields,” in Numerical Computations: Theory and Algorithms, Y. D. Sergeyev and D. E. Kvasov, Eds., in Numerical Computations: Theory and Algorithms. Cham: Springer International Publishing, 2020, pp. 446--453. doi: 10.1007/978-3-030-40616-5_42.
    12. J. C. Díaz-Ramos, M. Domínguez-Vázquez, and A. Kollross, “On homogeneous manifolds whose isotropy actions are polar,” manuscripta mathematica, vol. 161, no. 1, Art. no. 1, Jan. 2020, doi: 10.1007/s00229-018-1077-1.
    13. D. Holzmüller and I. Steinwart, “Training two-layer ReLU networks with gradient descent is inconsistent,” arXiv:2002.04861, 2020, [Online]. Available: https://arxiv.org/abs/2002.04861
    14. A. Bitter, “Virtual levels of multi-particle quantum systems and their implications for the Efimov effect,” Dissertation, Universität Stuttgart, Stuttgart, 2020. doi: 10.18419/opus-11315.
    15. M. Barreau, C. W. Scherer, F. Gouaisbaut, and A. Seuret, “Integral Quadratic Constraints on Linear Infinite-dimensional Systems for Robust Stability Analysis,” in IFAC World Congress, in IFAC World Congress. 2020.
    16. D. Grunert, J. Fehr, and B. Haasdonk, “Well-scaled, a-posteriori error estimation for model order reduction of large second-order mechanical systems,” ZAMM, vol. 100, no. 8, Art. no. 8, 2020, doi: 10.1002/zamm.201900186.
    17. J. Fehr and B. Haasdonk, Eds., IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart,  Germany, May 22-25, 2018: MORCOS 2018. in IUTAM Bookseries. Springer, 2020.
    18. D. Göddeke, M. Schirwon, and N. Borg, “Smartphone-Apps im Mathematikstudium,” 2020, doi: 10.18419/darus-1147.
    19. J. Giesselmann, F. Meyer, and C. Rohde, “A posteriori error analysis and adaptive non-intrusive numerical schemes for systems of random conservation laws,” BIT Numer. Math., 2020, [Online]. Available: https://doi.org/10.1007/s10543-019-00794-z
    20. M. Kohr, S. E. Mikhailov, and W. L. Wendland, “Potentials and transmission problems in weighted Sobolev spaces for anisotropic Stokes and Navier–Stokes systems with L∞ strongly elliptic coefficient tensor,” Complex Variables and Elliptic Equations, vol. 65, no. 1, Art. no. 1, 2020, doi: 10.1080/17476933.2019.1631293.
    21. S. Oladyshkin, F. Mohammadi, I. Kroeker, and W. Nowak, “Bayesian(3)Active Learning for the Gaussian Process Emulator Using    Information Theory,” ENTROPY, vol. 22, no. 8, Art. no. 8, Aug. 2020, doi: 10.3390/e22080890.
    22. L. A. Minorics, “Spectral asymptotics for Krein-Feller operators with respect to V-variable Cantor measures,” Forum Mathematicum, vol. 32, no. 1, Art. no. 1, Jan. 2020, doi: 10.1515/forum-2018-0188.
    23. T. Haas, B. de Rijk, and G. Schneider, “MODULATION EQUATIONS NEAR THE ECKHAUS BOUNDARY: THE KdV EQUATION,” SIAM JOURNAL ON MATHEMATICAL ANALYSIS, vol. 52, no. 6, Art. no. 6, 2020, doi: 10.1137/19M1266873.
    24. S. Baumstark, G. Schneider, K. Schratz, and D. Zimmermann, “Effective slow dynamics models for a class of dispersive systems,” J. Dyn. Differ. Equations, vol. 32, no. 4, Art. no. 4, 2020.
    25. G. Schneider, “The KdV approximation for a system with unstable resonances,” Math. Methods Appl. Sci., vol. 43, no. 6, Art. no. 6, 2020.
    26. M. L. Barberis, A. Moroianu, and U. Semmelmann, “Generalized vector cross products and Killing forms on negatively curved manifolds,” Geom. Dedicata, vol. 205, pp. 113--127, 2020, doi: 10.1007/s10711-019-00467-9.
    27. I. Steinwart, “Reproducing Kernel Hilbert Spaces Cannot Contain all Continuous Functions on a Compact Metric Space,” Fakultät für Mathematik und Physik, Universität Stuttgart, 2020.
    28. D. Holzmüller and I. Steinwart, “Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent,” Fakultät für Mathematik und Physik, Universität Stuttgart, 2020.
    29. M. Geck, “Computing Green functions in small characteristic,” Journal of Algebra, vol. 561, pp. 163--199, Nov. 2020, doi: 10.1016/j.jalgebra.2019.12.016.
    30. S. Fischer and I. Steinwart, “Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm,” J. Mach. Learn. Res., no. 205, Art. no. 205, 2020.
    31. S. Fischer and I. Steinwart, “Sobolev norm learning rates for regularized least-squares algorithms,” J. Mach. Learn. Res., vol. 21, no. 205, Art. no. 205, Oct. 2020, [Online]. Available: http://jmlr.org/papers/v21/19-734.html
    32. C. A. Rösinger and C. W. Scherer, “Lifting to Passivity for $H_2$-Gain-Scheduling Synthesis with Full Block Scalings,” in IFAC-PapersOnline, in IFAC-PapersOnline, vol. 53. 2020, pp. 7292–7298. doi: 10.1016/j.ifacol.2020.12.570.
    33. J. Magiera, D. Ray, J. S. Hesthaven, and C. Rohde, “Constraint-aware neural networks for Riemann problems,” J. Comput. Phys., vol. 409, no. 109345, Art. no. 109345, 2020, doi: https://doi.org/10.1016/j.jcp.2020.109345.
    34. I. Berre et al., “Verification benchmarks for single-phase flow in three-dimensional fractured porous media.” 2020.
    35. S. Michalowsky, C. Scherer, and C. Ebenbauer, “Robust and structure exploiting optimisation algorithms: An integral quadratic constraint approach,” International Journal of Control, vol. 2020, pp. 1–24, 2020, doi: 10.1080/00207179.2020.1745286.
    36. I. Rybak and S. Metzger, “A dimensionally reduced Stokes-Darcy model for fluid flow in fractured porous media,” Appl. Math. Comp., vol. 384, 2020, doi: 10.1016/j.amc.2020.125260.
    37. E. Eggenweiler and I. Rybak, “Unsuitability of the Beavers-Joseph interface condition for filtration problems,” J. Fluid Mech., vol. 892, p. A10, 2020, doi: http://dx.doi.org/10.1017/jfm.2020.194.
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    39. A. Beck, J. Dürrwächter, T. Kuhn, F. Meyer, C.-D. Munz, and C. Rohde, “$hp$-Multilevel Monte Carlo methods for uncertainty quantification of compressible flows,” SIAM J. Sci. Comput., vol. 42, no. 4, Art. no. 4, 2020, doi: https://doi.org/10.1137/18M1210575.
    40. J. B. Kennedy and R. Lang, “On the eigenvalues of quantum graph Laplacians with large complex δ couplings.,” Portugaliae Mathematica. A Journal of the Portuguese Mathematical Society, vol. 77, no. 2, Art. no. 2, 2020.
    41. P. Buchfink, B. Haasdonk, and S. Rave, “PSD-Greedy Basis Generation for Structure-Preserving Model Order Reduction of Hamiltonian Systems,” in Proceedings of the Conference Algoritmy 2020, P. Frolkovič, K. Mikula, and D. Ševčovič, Eds., in Proceedings of the Conference Algoritmy 2020. Vydavateľstvo SPEKTRUM, Aug. 2020, pp. 151--160. [Online]. Available: http://www.iam.fmph.uniba.sk/amuc/ojs/index.php/algoritmy/article/view/1577/829
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    44. T. Hitz, J. Keim, C.-D. Munz, and C. Rohde, “A parabolic relaxation model for the Navier-Stokes-Korteweg equations,” J. Comput. Phys., vol. 421, p. 109714, 2020, doi: https://doi.org/10.1016/j.jcp.2020.109714.
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    47. A. Alla, B. Haasdonk, and A. Schmidt, “Feedback control of parametrized PDEs via model order              reduction and dynamic programming principle,” Adv. Comput. Math., vol. 46, no. 1, Art. no. 1, 2020, doi: 10.1007/s10444-020-09744-8.
    48. B. Haasdonk, B. Hamzi, G. Santin, and D. Wittwar, “Greedy kernel methods for center manifold approximation,” in Spectral and high order methods for partial differential              equations---ICOSAHOM 2018, in Spectral and high order methods for partial differential              equations---ICOSAHOM 2018, vol. 134. Springer, Cham, 2020, pp. 95--106. doi: 10.1007/978-3-030-39647-3\_6.
    49. C. A. Rösinger and C. W. Scherer, “A Flexible Synthesis Framework of Structured Controllers for Networked Systems,” IEEE Trans. Control Netw. Syst., vol. 7, no. 1, Art. no. 1, 2020, doi: 10.1109/TCNS.2019.2914411.
    50. C. Lienstromberg and S. Müller, “Local strong solutions to a quasilinear degenerate              fourth-order thin-film equation,” NoDEA Nonlinear Differential Equations Appl., vol. 27, no. 2, Art. no. 2, 2020, doi: 10.1007/s00030-020-0619-x.
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    52. J. Berberich, A. Koch, C. W. Scherer, and F. Allgöwer, “Robust data-driven state-feedback design,” in 2020 American Control Conference (ACC), in 2020 American Control Conference (ACC). Jul. 2020, pp. 1532–1538. doi: 10.23919/acc45564.2020.9147320.
    53. E. Eggenweiler and I. Rybak, “Interface conditions for arbitrary flows in coupled porous-medium and free-flow systems,” in Finite Volumes for Complex Applications IX - Methods, Theoretical Aspects, Examples, R. Klöfkorn, E. Keilegavlen, F. Radu, and J. Fuhrmann, Eds., in Finite Volumes for Complex Applications IX - Methods, Theoretical Aspects, Examples, vol. 323. Springer International Publishing, 2020, pp. 345--353. doi: 10.1007/978-3-030-43651-3_31.
    54. J. T. Gerstenberger, S. Burbulla, and D. Kröner, “Discontinuous Galerkin method for incompressible two-phase flows,” in Finite Volumes for Complex Applications IX - Methods, Theoretical Aspects, Examples, R. Klöfkorn, E. Keilegavlen, F. A. Radu, and J. Fuhrmann, Eds., in Finite Volumes for Complex Applications IX - Methods, Theoretical Aspects, Examples. Cham: Springer International Publishing, 2020, pp. 675–683.
    55. T. Koch et al., “DuMux 3 – an open-source simulator for solving flow and transport problems in porous media with a focus on model coupling,” Computers & Mathematics with Applications, 2020, doi: https://doi.org/10.1016/j.camwa.2020.02.012.
    56. J. Giesselmann, F. Meyer, and C. Rohde, “A posteriori error analysis for random scalar conservation laws using the Stochastic Galerkin method,” IMA J. Numer. Anal., vol. 40, no. 2, Art. no. 2, 2020, doi: 10.1093/imanum/drz004.
    57. C. Rohde and L. von Wolff, “Homogenization of non-local Navier-Stokes-Korteweg equations for compressible liquid-vapour flow in porous media,” SIAM J. Math. Anal., vol. 52, no. 6, Art. no. 6, 2020, doi: 10.1137/19M1242434.
    58. A. Vonica et al., “Apcdd1 is a dual BMP/Wnt inhibitor in the developing nervous system and skin,” Developmental Biology, vol. 464, no. 1, Art. no. 1, Aug. 2020, doi: 10.1016/j.ydbio.2020.03.015.
    59. M. Geck, “On Jacob’s construction of the rational canonical form of a matrix,” The Electronic Journal of Linear Algebra, vol. 36, no. 36, Art. no. 36, Apr. 2020, doi: 10.13001/ela.2020.5055.
    60. B. Hilder, “Modulating traveling fronts for the Swift-Hohenberg equation in the case of an additional conservation law,” Journal of Differential Equations, vol. 269, no. 5, Art. no. 5, Aug. 2020, doi: 10.1016/j.jde.2020.03.033.
    61. M. Griesemer, M. Hofacker, and U. Linden, “From short-range to contact interactions in the 1d Bose gas,” Math. Phys. Anal. Geom., vol. 23, no. 2, Art. no. 2, 2020, doi: 10.1007/s11040-020-09344-4.
    62. S. Baumstark, G. Schneider, and K. Schratz, “Effective numerical simulation of the Klein-Gordon-Zakharov system in the Zakharov limit,” in Mathematics of wave phenomena. Selected papers based on the presentations at the conference, Karlsruhe, Germany, July 23--27, 2018, in Mathematics of wave phenomena. Selected papers based on the presentations at the conference, Karlsruhe, Germany, July 23--27, 2018. Cham: Birkhäuser, 2020, pp. 37--48.
    63. N. Ginoux, G. Habib, M. Pilca, and U. Semmelmann, “An Obata-type characterisation of Calabi metrics on line bundles,” North-West. Eur. J. Math., vol. 6, pp. 119--136, i, 2020.
    64. J. Brinker and J. Wirth, “Gelfand Triples for the Kohn–Nirenberg Quantization on Homogeneous Lie Groups,” in Advances in Harmonic Analysis and Partial Differential Equations., in Advances in Harmonic Analysis and Partial Differential Equations. Birkhäuser, 2020, pp. 51–97. doi: 10.1007/978-3-030-58215-9_3.
    65. D. Maier, “Construction of breather solutions for nonlinear Klein-Gordon equations    on periodic metric graphs,” JOURNAL OF DIFFERENTIAL EQUATIONS, vol. 268, no. 6, Art. no. 6, Mar. 2020, doi: 10.1016/j.jde.2019.09.035.
    66. D. Maier, “BREATHER SOLUTIONS ON DISCRETE NECKLACE GRAPHS,” OPERATORS AND MATRICES, vol. 14, no. 3, Art. no. 3, Sep. 2020, doi: 10.7153/oam-2020-14-48.
    67. C. Bringedal, L. Von Wolff, and I. S. Pop, “Phase Field Modeling of Precipitation and Dissolution Processes in Porous Media: Upscaling and Numerical Experiments,” Multiscale Modeling &amp$\mathsemicolon$ Simulation, vol. 18, no. 2, Art. no. 2, Jan. 2020, doi: 10.1137/19m1239003.
    68. L. Ostrowski and C. Rohde, “Compressible multi-component flow in porous media with Maxwell-Stefan diffusion,” Math. Meth. Appl. Sci., pp. 1–22, 2020, [Online]. Available: https://doi.org/10.1002/mma.6185
    69. L. Ostrowski and C. Rohde, “Phase field modelling for compressible droplet impingement,” in Hyperbolic Problems: Theory, Numerics, Applications. Proceedings of the Seventeenth International Conference on Hyperbolic Problems 2018, A. Bressan, M. Lewicka, D. Wang, and Y. Zheng, Eds., in Hyperbolic Problems: Theory, Numerics, Applications. Proceedings of the Seventeenth International Conference on Hyperbolic Problems 2018, vol. 10. AIMS Series on Applied Mathematics, 2020, pp. 586–593. [Online]. Available: https://www.aimsciences.org/fileAIMS/cms/news/info/upload//c0904f1f-97d5-451f-b068-25f1612b6852.pdf
    70. L. Ostrowski, F. C. Massa, and C. Rohde, “A phase field approach to compressible droplet impingement,” in Droplet Interactions and Spray Processes, G. Lamanna, S. Tonini, G. E. Cossali, and B. Weigand, Eds., in Droplet Interactions and Spray Processes. Cham: Springer International Publishing, 2020, pp. 113–126. [Online]. Available: https://doi.org/10.1007/978-3-030-33338-6_9
    71. 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.
    72. A. M. Naveira and U. Semmelmann, “Conformal Killing forms on nearly Kähler manifolds,” Differential Geom. Appl., vol. 70, pp. 101628, 9, 2020, doi: 10.1016/j.difgeo.2020.101628.
    73. U. Semmelmann, C. Wang, and M. Y.-K. Wang, “On the linear stability of nearly Kähler 6-manifolds,” Ann. Global Anal. Geom., vol. 57, no. 1, Art. no. 1, 2020, doi: 10.1007/s10455-019-09686-5.
    74. V. Georgiev, T. Ozawa, M. Ruzhansky, and J. Wirth, Eds., Advances in Harmonic Analysis and Partial Differential Equations. in Trends in Mathematics. Birkhäuser, 2020. doi: 10.1007/978-3-030-58215-9.
    75. M. Geck and G. Malle, “The character theory of finite groups of Lie type. A guided tour,” in Cambridge Studies in Advanced Mathematics, in Cambridge Studies in Advanced Mathematics, vol. 187. Cambridge University Press, 2020, p. ix+394. doi: https://doi.org/10.1017/9781108779081.
    76. G. Rigaud and B. N. Hahn, “Reconstruction algorithm for 3D Compton scattering imaging with incomplete data,” Inverse Problems in Science and Engineering, vol. 29, no. 7, Art. no. 7, 2020, doi: 10.1080/17415977.2020.1815723.
    77. T. Jentsch and G. Weingart, “RIEMANNIAN AND KAHLERIAN NORMAL COORDINATES,” ASIAN JOURNAL OF MATHEMATICS, vol. 24, no. 3, Art. no. 3, Jun. 2020.
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  5. 2019

    1. R. M. Colombo, P. G. LeFloch, C. Rohde, and K. Trivisa, “Nonlinear Hyperbolic Problems: Modeling, Analysis, and Numerics,” Oberwohlfach Rep., no. 16, Art. no. 16, 2019, [Online]. Available: https://www.ems-ph.org/journals/show_issue.php?issn=1660-8933&vol=16&iss=2
    2. V. Sharanya, G. P. R. Sekhar, and C. Rohde, “Surfactant-induced migration of a spherical droplet in non-isothermal Stokes flow,” Physics of Fluids, vol. 31, no. 1, Art. no. 1, 2019, doi: 10.1063/1.5064694.
    3. M. Kohr, S. E. Mikhailov, and W. L. Wendland, “Newtonian and Single Layer Potentials for the Stokes System with L∞ Coefficients and the Exterior Dirichlet Problem,” in Analysis as a Life: Dedicated to Heinrich Begehr on the Occasion of his 80th Birthday, S. Rogosin and A. O. Celebi, Eds., in Analysis as a Life: Dedicated to Heinrich Begehr on the Occasion of his 80th Birthday. Cham: Springer International Publishing, 2019, pp. 237--260. doi: 10.1007/978-3-030-02650-9_12.
    4. D. Seus, F. A. Radu, and C. Rohde, “A linear domain decomposition method for two-phase flow in porous media,” Numerical Mathematics and Advanced Applications ENUMATH 2017, pp. 603–614, 2019, doi: https://doi.org/10.1007/978-3-319-96415-7_55.
    5. B. N. Hahn and M.-L. Kienle Garrido, “An efficient reconstruction approach for a class of dynamic imaging operators,” Inverse Problems, vol. 35, no. 9, Art. no. 9, 2019, doi: 10.1088/1361-6420/ab178b.
    6. K. Höllig and J. Hörner, Aufgaben und Lösungen zur Höheren Mathematik. - 1., 2. Auflage., vol. 1. in Aufgaben und Lösungen zur Höheren Mathematik ; 1, vol. 1. Berlin ; Heidelberg: Springer Spektrum, 2019, pp. x, 235 Seiten.
    7. R. Bauer, W.-P. Düll, and G. Schneider, “The Korteweg--de Vries, Burgers and Whitham limits for a spatially periodic Boussinesq model,” Proc. Roy. Soc. Edinburgh Sect. A, vol. 149, no. 1, Art. no. 1, 2019, doi: 10.1017/S0308210518000227.
    8. L. A. Bianchi, D. Blömker, and G. Schneider, “Modulation equation and SPDEs on unbounded domains,” Commun. Math. Phys., vol. 371, no. 1, Art. no. 1, 2019.
    9. R. Bauer, P. Cummings, and G. Schneider, “A model for the periodic water wave problem and its long wave amplitude equations,” in Nonlinear water waves. An interdisciplinary interface. Based on the workshop held at the Erwin Schrödinger International Institute for Mathematics and Physics, Vienna, Austria, November 27 -- December 7, 2017, in Nonlinear water waves. An interdisciplinary interface. Based on the workshop held at the Erwin Schrödinger International Institute for Mathematics and Physics, Vienna, Austria, November 27 -- December 7, 2017. Cham: Birkhäuser, 2019, pp. 123--138.
    10. P. Buchfink, A. Bhatt, and B. Haasdonk, “Symplectic Model Order Reduction with Non-Orthonormal Bases,” Mathematical and Computational Applications, vol. 24, no. 2, Art. no. 2, 2019, doi: 10.3390/mca24020043.
    11. G. Santin and B. Haasdonk, “Kernel Methods for Surrogate Modelling,” University of Stuttgart, 2019.
    12. G. Baggio, S. Zampieri, and C. W. Scherer, “Gramian Optimization with Input-Power Constraints,” in 58th IEEE Conf. Decision and Control, in 58th IEEE Conf. Decision and Control. 2019, pp. 5686–5691. doi: 10.1109/CDC40024.2019.9029169.
    13. T. Holicki and C. W. Scherer, “A Homotopy Approach for Robust Output-Feedback Synthesis,” in Proc. 27th. Med. Conf. Control Autom., in Proc. 27th. Med. Conf. Control Autom. 2019, pp. 87–93. doi: 10.1109/MED.2019.8798536.
    14. A. Bhatt, J. Fehr, and B. Haasdonk, “Model order reduction of an elastic body under large rigid motion,” Proceedings of ENUMATH 2017, vol. Lect. Notes Comput. Sci. Eng., no. 126, Art. no. 126, 2019, doi: 10.1007/978-3-319-96415-7\_23.
    15. M. Köppel et al., “Comparison of data-driven uncertainty quantification methods for  a carbon dioxide storage benchmark scenario,” Comput. Geosci., vol. 2, no. 23, Art. no. 23, 2019, doi: https://doi.org/10.1007/s10596-018-9785-x.
    16. U. Semmelmann and G. Weingart, “The standard Laplace operator,” Manuscripta Math., vol. 158, no. 1–2, Art. no. 1–2, 2019, doi: 10.1007/s00229-018-1023-2.
    17. M. Schanz et al., “Urinary TIMP-2·IGFBP7-guided randomized controlled intervention trial to prevent acute kidney injury in the emergency department.,” Transplant., vol. 2019 Nov 1;34(11), pp. 1902–1909, 2019, doi: 10.1093/ndt/gfy186.
    18. D. Wittwar, G. Santin, and B. Haasdonk, “Part II on matrix valued kernels including analysis,” 2019.
    19. M. Oesting, M. Schlather, and C. Schillings, “Sampling sup-normalized spectral functions for Brown-Resnick processes,” Stat, vol. 8, pp. e228, 11, 2019, doi: 10.1002/sta4.228.
    20. M. Farooq and I. Steinwart, “Learning Rates for Kernel-Based Expectile Regression,” Mach. Learn., vol. 108, pp. 203--227, 2019, doi: 10.1007/s10994-018-5762-9.
    21. A. Defant, M. Mastyo, E. A. Sánchez-Pérez, and I. Steinwart, “Translation invariant maps on function spaces over locally compact groups,” J. Math. Anal. Appl., vol. 470, pp. 795--820, 2019, doi: 10.1016/j.jmaa.2018.10.033.
    22. C. A. Rösinger and C. W. Scherer, “A Flexible Synthesis Framework of Structured Controllers for Networked Systems,” IEEE Trans. Control Netw. Syst., vol. 7, no. 1, Art. no. 1, 2019, doi: 10.1109/TCNS.2019.2914411.
    23. T. Kuhn, J. Dürrwächter, F. Meyer, A. Beck, C. Rohde, and C.-D. Munz, “Uncertainty quantification for direct aeroacoustic simulations of cavity flows,” J. Theor. Comput. Acoust., vol. 27, no. 1, Art. no. 1, 2019, doi: https://doi.org/10.1142/S2591728518500445.
    24. R. Mazzeo, J. Swoboda, H. Weiss, and F. Witt, “Asymptotic geometry of the Hitchin metric,” Commun. Math. Phys., vol. 367, no. 1, Art. no. 1, 2019, doi: 10.1007/s00220-019-03358-y.
    25. C. A. Rösinger and C. W. Scherer, “A Scalings Approach to $H_2$-Gain-Scheduling Synthesis without Elimination,” in IFAC-PapersOnLine, in IFAC-PapersOnLine, vol. 52. 2019, pp. 50–57. doi: 10.1016/j.ifacol.2019.12.347.
    26. T. Holicki and C. W. Scherer, “Stability analysis and output-feedback synthesis of hybrid systems affected by piecewise constant parameters via dynamic resetting scalings,” Nonlinear Analysis: Hybrid Systems, vol. 34, pp. 179--208, Nov. 2019, doi: 10.1016/j.nahs.2019.06.003.
    27. M. Chirilus-Bruckner, D. Maier, and G. Schneider, “Diffusive stability for periodic metric graphs,” Math. Nachr., vol. 292, no. 6, Art. no. 6, 2019.
    28. Y. Homma and U. Semmelmann, “The Kernel of the Rarita-Schwinger Operator on Riemannian Spin Manifolds,” Comm. Math. Phys., vol. 370, no. 3, Art. no. 3, 2019, doi: 10.1007/s00220-019-03324-8.
    29. M. Geck, “Eigenvalues and Polynomial Equations,” The American Mathematical Monthly, vol. 126, no. 10, Art. no. 10, Nov. 2019, doi: 10.1080/00029890.2019.1651168.
    30. M. Hansmann, M. Kohler, and H. Walk, “On the strong universal consistency of local averaging regression    estimates (vol 71, pg 1233, 2019),” ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, vol. 71, no. 5, Art. no. 5, Oct. 2019, doi: 10.1007/s10463-018-0687-4.
    31. T. Wenzel, G. Santin, and B. Haasdonk, “A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution.” 2019.
    32. N. Mücke and I. Steinwart, “Empirical Risk Minimization in the Interpolating Regime with Application to Neural Network Learning,” Fakultät für Mathematik und Physik, Universität Stuttgart, 2019.
    33. I. Steinwart, “A Sober Look at Neural Network Initializations,” Fakultät für Mathematik und Physik, Universität Stuttgart, 2019.
    34. S. Schricker et al., “Strong Associations Between Inflammation, Pruritus and Mental Health in Dialysis Patients,” Acta Derm Venereol., vol. 2019 May 1;99(6), pp. 524–529, 2019, doi: 10.2340/00015555-3128.
    35. F. G. Zhang R, Dippon J, “Refined risk stratification for thoracoscopic lobectomy or segmentectomy,” Dis., J Thorac, vol. 2019 Jan;11(1), p. :222-230, 2019, doi: 10.21037/jtd.2018.12.44.
    36. A. Denzel, B. Haasdonk, and J. Kästner, “Gaussian Process Regression for Minimum Energy Path Optimization and Transition State Search,” J. Phys. Chem. A, vol. 123, no. 44, Art. no. 44, 2019, [Online]. Available: https://doi.org/10.1021/acs.jpca.9b08239
    37. A. Schmidt, D. Wittwar, and B. Haasdonk, “Rigorous and effective a-posteriori error bounds for nonlinear problems -- Application to RB methods,” Advances in Computational Mathematics, 2019, doi: 10.1007/s10444-019-09730-9.
    38. D. Wittwar and B. Haasdonk, “Greedy Algorithms for Matrix-Valued Kernels,” in Numerical Mathematics and Advanced Applications ENUMATH 2017, F. A. Radu, K. Kumar, I. Berre, J. M. Nordbotten, and I. S. Pop, Eds., in Numerical Mathematics and Advanced Applications ENUMATH 2017. Cham: Springer International Publishing, 2019, pp. 113--121.
    39. K. Carlberg, L. Brencher, B. Haasdonk, and A. Barth, “Data-driven time parallelism via forecasting,” SIAM Journal on Scientific Computing, vol. 41, no. 3, Art. no. 3, 2019.
    40. R. Föll, B. Haasdonk, M. Hanselmann, and H. Ulmer, “Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation.” 2019. [Online]. Available: https://openreview.net/forum?id=BkgosiRcKm
    41. M. Kohr and W. L. Wendland, “Boundary value problems for the Brinkman system with L∞ coefficients in Lipschitz domains on compact Riemannian manifolds. A variational approach,” Journal de Mathématiques Pures et Appliquées, no. 131, Art. no. 131, Nov. 2019, doi: https://doi.org/10.1016/j.matpur.2019.04.002.
    42. I. Steinwart, “Convergence Types and Rates  in Generic Karhunen-Loève Expansions with Applications to Sample Path Properties,” Potential Anal., vol. 51, pp. 361--395, 2019, doi: 10.1007/s11118-018-9715-5.
    43. A. Armiti-Juber and C. Rohde, “Existence of weak solutions for a nonlocal pseudo-parabolic model for Brinkman two-phase flow in asymptotically flat porous media,” J. Math. Anal. Appl., vol. 477, no. 1, Art. no. 1, 2019, doi: https://doi.org/10.1016/j.jmaa.2019.04.049.
    44. T. Kluth, B. N. Hahn, and C. Brandt, “Spatio-temporal concentration reconstruction using motion priors in magnetic particle imaging,” in Proc. Int. Workshop Magnetic Particle Imaging, in Proc. Int. Workshop Magnetic Particle Imaging. 2019.
    45. T. Brünnette, G. Santin, and B. Haasdonk, “Greedy Kernel Methods for Accelerating Implicit Integrators for Parametric ODEs,” in Numerical Mathematics and Advanced Applications - ENUMATH 2017, F. A. Radu, K. Kumar, I. Berre, J. M. Nordbotten, and I. S. Pop, Eds., in Numerical Mathematics and Advanced Applications - ENUMATH 2017. Cham: Springer International Publishing, 2019, pp. 889--896.
    46. G. Santin and B. Haasdonk, “Kernel Methods for Surrogate Modeling,” ArXiv 1907.10556, 2019. [Online]. Available: https://arxiv.org/abs/1907.10556
    47. R. Zhang, T. Kyriss, J. Dippon, E. Boedeker, and G. Friedel, “Preoperative serum lactate dehydrogenase level as a predictor of major omplications following thoracoscopic lobectomy: a propensity-adjusted analysis.,” European Journal of Cardio-Thoracic Surgery, vol. 56, no. 2, Art. no. 2, 2019, doi: 10.1093/ejcts/ezz027.
    48. M. Griesemer and U. Linden, “Spectral theory of the Fermi polaron,” Ann. Henri Poincaré, vol. 20, no. 6, Art. no. 6, 2019, doi: 10.1007/s00023-019-00796-1.
    49. K. Heil and T. Jentsch, “A special class of symmetric Killing 2-tensors,” JOURNAL OF GEOMETRY AND PHYSICS, vol. 138, pp. 103–123, Apr. 2019, doi: 10.1016/j.geomphys.2018.12.009.
    50. B. Ammann, K. Kröncke, H. Weiss, and F. Witt, “Holonomy rigidity for Ricci-flat metrics,” Math. Z., vol. 291, no. 1–2, Art. no. 1–2, 2019, doi: 10.1007/s00209-018-2084-3.
    51. A. Armiti-Juber and C. Rohde, “On Darcy-and Brinkman-type models for two-phase flow in asymptotically flat domains,” Comput. Geosci., vol. 23, no. 2, Art. no. 2, 2019, doi: https://doi.org/10.1007/s10596-018-9756-2.
    52. G. Santin, D. Wittwar, and B. Haasdonk, “Sparse approximation of regularized kernel interpolation by greedy algorithms,” 2019.
    53. S. Engelke, R. de Fondeville, and M. Oesting, “Extremal behaviour of aggregated data with an application to downscaling,” Biometrika, vol. 106, no. 1, Art. no. 1, 2019, doi: 10.1093/biomet/asy052.
    54. R. Conlon, A. Degeratu, and F. Rochon, “Quasi-asymptotically conical Calabi-Yau manifolds,” Geom. Topol., vol. 23, no. 1, Art. no. 1, 2019, doi: 10.2140/gt.2019.23.29.
    55. C. T. Miller, W. G. Gray, C. E. Kees, I. V. Rybak, and B. J. Shepherd, “Modeling sediment transport in three-phase surface water systems,” J. Hydraul. Res., vol. 57, 2019, doi: 10.1080/00221686.2019.1581673.
    56. L. Ostrowski and F. Massa, “An incompressible-compressible approach for droplet impact,” in Proceedings of the DIPSI Workshop 2019: Droplet ImpactPhenomena & Spray Investigations, Bergamo, Italy, 17th May 2019, G. Cossali and S. Tonini, Eds., in Proceedings of the DIPSI Workshop 2019: Droplet ImpactPhenomena & Spray Investigations, Bergamo, Italy, 17th May 2019. Università degli studi di Bergamo, 2019, pp. 18–21. doi: 10.6092/DIPSI2019_pp18-21.
    57. A. Bhatt, J. Fehr, D. Grunert, and B. Haasdonk, “A Posteriori Error Estimation in Model Order Reduction of Elastic Multibody Systems with Large Rigid Motion,” in IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22–25, 2018, J. Fehr and B. Haasdonk, Eds., in IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22–25, 2018. Springer, 2019. doi: DOI:10.1007/978-3-030-21013-7_7.
  6. 2017

    1. M. Geck, “Minuscule weights and Chevalley                      groups,” in Finite Simple Groups: Thirty Years of the Atlas and Beyond (Celebrating the Atlases and Honoring John Conway, November 2-5, 2015 at Princeton University), in Finite Simple Groups: Thirty Years of the Atlas and Beyond (Celebrating the Atlases and Honoring John Conway, November 2-5, 2015 at Princeton University), vol. 694. American Mathematical                      Society, 2017, pp. 159--176. doi: 10.1090/conm/694/13955.
    2. H. Minbashian, H. Adibi, and M. Dehghan, “An adaptive wavelet space-time SUPG method for hyperbolic conservation  laws,” Numerical Methods for Partial Differential Equations, vol. 33, no. 6, Art. no. 6, 2017, doi: 10.1002/num.22180.
  7. 2012

    1. M. Feistauer and A.-M. Sändig, “Graded mesh refinement and error estimates of higher order for DGFE solutions of elliptic boundary value problems in polygons,” Numerical Methods for Partial Differential Equations, vol. 28, no. 4, Art. no. 4, 2012, doi: 10.1002/num.20668.
  8. 2011

    1. A. Lalegname and A. Sändig, “Wave-crack interaction in finite elastic bodies,” International Journal of Fracture, vol. 172, no. 2, Art. no. 2, 2011, doi: 10.1007/s10704-011-9650-6.

Teaching

Have a look at our ongoing and past lectures as well as possible thesis topics.

 

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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