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. 2025

    1. Anamika, R. Barthwal, and T. R. Sekhar, “Construction of solutions to a Riemann problem for a two-dimensional Keyfitz-Kranzer type model governing thin film flow,” Accepted for publication at Applied Mathematics and Computation, 2025.
    2. C. Rohde and F. Wendt, “Mathematical Justification of a Baer-Nunziato Model for a Compressible Viscous Fluid with Phase Transition.” 2025. [Online]. Available: https://arxiv.org/abs/2504.10161
    3. R. Barthwal, C. Rohde, and Y. Wang, “A generalized Riemann problem solver for a hyperbolic model of two-layer thin film flow.” 2025. [Online]. Available: https://arxiv.org/abs/2505.18737
    4. B. Schembera, “Data, Information, Knowledge, Wisdom, and Explainable Artificial Intelligence,” in Designing the Conceptual Landscape for a XAIR Validation Infrastructure, F. Al Machot, M. T. Horsch, and S. Scholze, Eds., Cham: Springer Nature Switzerland, 2025, pp. 122–132.
    5. 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.
    6. Z. Askarpour, M. Nottoli, and B. Stamm, “Grassmann Extrapolation for Accelerating Geometry Optimization,” Journal of Chemical Theory and Computation, vol. 21, Art. no. 4, Feb. 2025, doi: 10.1021/acs.jctc.4c01417.
    7. Y. Cao, Q. Huang, J. Koellermeier, A. Kurganov, and Y. Liu, “Flux Globalization Based Well-Balanced Path-Conservative Central-Upwind Schemes for Shallow Water Linearized Moment Equations.” 2025. [Online]. Available: https://arxiv.org/abs/2505.23144
    8. A. Schwarz, J. Keim, C. Rohde, and A. Beck, “Entropy stable shock capturing for high-order DGSEM on moving meshes.” 2025. [Online]. Available: https://arxiv.org/abs/2503.23237
    9. Y. Cheng, E. Cancès, V. Ehrlacher, A. J. Misquitta, and B. Stamm, “Multi-center decomposition of molecular densities: A numerical perspective,” The Journal of Chemical Physics, vol. 162, Art. no. 7, Feb. 2025, doi: 10.1063/5.0245287.
    10. Q. Huang, C. Rohde, W.-A. Yong, and R. Zhang, “A hyperbolic relaxation approximation of the incompressible Navier-Stokes equations with artificial compressibility,” J. Differential Equations, vol. 438, p. 113339, 2025, doi: 10.1016/j.jde.2025.113339.
    11. A. Schwarz, D. Kempf, J. Keim, P. Kopper, C. Rohde, and A. Beck, “Comparison of Entropy Stable Collocation High-Order DG Methods for Compressible Turbulent Flows.” 2025. [Online]. Available: https://arxiv.org/abs/2504.00173
    12. R. Barthwal and C. Rohde, “A hyperbolic model for two-layer thin film flow with a perfectly soluble anti-surfactant.” 2025. [Online]. Available: https://arxiv.org/abs/2502.17205
    13. C. Riethmüller, E. Storvik, J. W. Both, and F. A. Radu, “Well-posedness analysis of the Cahn–Hilliard–Biot model,” Nonlinear Analysis: Real World Applications, vol. 84, p. 104271, Aug. 2025, doi: 10.1016/j.nonrwa.2024.104271.
    14. T. Schollenberger, C. Rohde, and R. Helmig, “Two-phase pore-network model for evaporation-driven salt precipitation -- representation and analysis of pore-scale processes.” 2025. [Online]. Available: https://arxiv.org/abs/2503.22533
    15. L. Duvenbeck, C. Riethmüller, and C. Rohde, “Data-driven geometric parameter optimization for PD-GMRES.” 2025. doi: https://doi.org/10.48550/arXiv.2503.09728.
    16. L. Ruan and I. Rybak, “A hybrid-dimensional Stokes-Brinkman-Darcy model for arbitrary flows to the fluid-porous interface,” Transp. Porous Med. (submitted), 2025.
    17. T. Ghosh, C. Bringedal, C. Rohde, and R. Helmig, “A phase-field approach to model evaporation from porous media: Modeling and upscaling,” Advances in Water Resources, p. 104922, 2025, doi: https://doi.org/10.1016/j.advwatres.2025.104922.
    18. J. Keim, A. Schwarz, P. Kopper, M. Blind, C. Rohde, and A. Beck, “Entropy stable high-order discontinuous Galerkin spectral-element methods on curvilinear, hybrid meshes.” 2025. [Online]. Available: https://arxiv.org/abs/2507.04334
    19. A. Barth and A. Stein, “A stochastic transport problem with Lévy noise: Fully discrete numerical approximation.,” Mathematics and Computers in Simulation, vol. 227, pp. 347–370, 2025, [Online]. Available: https://doi.org/10.1016/j.matcom.2024.07.036
    20. P.-A. NAGY and U. SEMMELMANN, “Second order Einstein deformations,” Journal of the Mathematical Society of Japan, vol. 77, Art. no. 2, 2025, doi: 10.2969/jmsj/92169216.
    21. E. Storvik, C. Riethmüller, J. W. Both, and F. A. Radu, “Sequential Solution Strategies for the Cahn-Hilliard-Biot Model,” in Numerical Mathematics and Advanced Applications ENUMATH 2023, Volume 2, A. Sequeira, A. Silvestre, S. S. Valtchev, and J. Janela, Eds., Cham: Springer Nature Switzerland, 2025, pp. 369–378.
  2. 2024

    1. X. Claeys, M. Hassan, and B. Stamm, “Continuity estimates for Riesz potentials on polygonal boundaries,” Partial Differential Equations and Applications, Jun. 2024, doi: 10.1007/s42985-024-00280-4.
    2. Y. Miao, C. Rohde, and H. Tang, “Well-posedness for a stochastic Camassa-Holm type equation with higher order nonlinearities,” Stoch. Partial Differ. Equ. Anal. Comput., vol. 12, Art. no. 1, 2024, doi: 10.1007/s40072-023-00291-z.
    3. T. Mel’nyk and C. Rohde, “Asymptotic expansion for convection-dominated transport in a thin graph-like junction.,” Analysis and Applications, vol. 22 (05), pp. 833–879, 2024, doi: https://doi.org/10.1142/S0219530524500040.
    4. T. A. Mel’nyk and T. Durante, “Spectral problems with perturbed Steklov conditions in thick junctions with branched structure.,” Applicable Analysis, pp. 1–26, 2024, doi: https://doi.org/10.1080/00036811.2024.2322644.
    5. F. Döppel, T. Wenzel, R. Herkert, B. Haasdonk, and M. Votsmeier, “Goal‐Oriented Two‐Layered Kernel Models as Automated Surrogates for Surface Kinetics in Reactor Simulations,” Chemie Ingenieur Technik, vol. 96, Art. no. 6, Jan. 2024, doi: 10.1002/cite.202300178.
    6. R. R. Herkert, “Replication Code for: Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data.” 2024. doi: 10.18419/darus-4227.
    7. M. Bondanza, T. Nottoli, M. Nottoli, L. Cupellini, F. Lipparini, and B. Mennucci, “The OpenMMPol library for polarizable QM/MM calculations of properties and dynamics,” The Journal of Chemical Physics, vol. 160, Art. no. 13, Apr. 2024, doi: 10.1063/5.0198251.
    8. T. Schollenberger, L. von Wolff, C. Bringedal, I. S. Pop, C. Rohde, and R. Helmig, “Investigation of Different Throat Concepts for Precipitation Processes in Saturated Pore-Network Models,” Transport in Porous Media, Oct. 2024, doi: 10.1007/s11242-024-02125-5.
    9. L. Theisen and B. Stamm, “A Scalable Two-Level Domain Decomposition Eigensolver for Periodic Schrödinger Eigenstates in Anisotropically Expanding Domains,” SIAM Journal on Scientific Computing, vol. 46, Art. no. 5, Oct. 2024, doi: 10.1137/23m161848x.
    10. A. F. Albişoru, M. Kohr, I. Papuc, and W. L. Wendland, “On some Robin–transmission problems for the Brinkman system and a Navier–Stokes type system,” Math. Meth. Appl. Sci., pp. 1–28, 2024, doi: https://doi.org/10.1002/mma.10170.
    11. W. L. Wendland, “On the construction of the Stokes flow in a domain with cylindrical ends,” Math. Meth. Appl. Sci., pp. 1–6, 2024, doi: https://doi.org/10.1002/mma.10106.
    12. I. M. Karabash, C. Lienstromberg, and J. J. L. Velázquez, “Multi-parameter Hopf bifurcations of rimming flows,” 2024, doi: https://doi.org/10.48550/arXiv.2406.11690.
    13. P. Buchfink, S. Glas, B. Haasdonk, and B. Unger, “Model reduction on manifolds: A differential geometric framework.” 2024. [Online]. Available: https://arxiv.org/abs/2312.01963
    14. T. Wenzel, B. Haasdonk, H. Kleikamp, M. Ohlberger, and F. Schindler, “Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling,” in Large-Scale Scientific Computations, I. Lirkov and S. Margenov, Eds., Cham: Springer Nature Switzerland, 2024, pp. 117–125.
    15. M. Nottoli, M. F. Herbst, A. Mikhalev, A. Jha, F. Lipparini, and B. Stamm, “ddX: Polarizable continuum solvation from small molecules to proteins,” WIREs Computational Molecular Science, Jul. 2024, doi: 10.1002/wcms.1726.
    16. A. Jha, “Residual-Based a Posteriori Error Estimators for Algebraic Stabilizations,” Applied Mathematics Letters, vol. 157, p. 109192, Jun. 2024, doi: 10.1016/j.aml.2024.109192.
    17. E. B. Lindgren, H. Avis, A. Miller, B. Stamm, E. Besley, and A. J. Stace, “The significance of multipole interactions for the stability of regular structures composed from charged particles,” Journal of Colloid and Interface Science, vol. 663, pp. 458–466, Jun. 2024, doi: 10.1016/j.jcis.2024.02.146.
    18. K. Morrison, A. Degeratu, V. Itskov, and C. Curto, “Diversity of Emergent Dynamics in Competitive Threshold-Linear Networks,” SIAM journal on applied dynamical systems, vol. 23, Art. no. 1, 2024, doi: 10.1137/22M1541666.
    19. W.-P. Düll, G. Schneider, and R. Taraca, “On the Korteweg--de Vries approximation for a Boussinesq equation posed on the infinite necklace graph,” Math. Methods Appl. Sci., vol. 47, Art. no. 12, 2024, doi: 10.1002/mma.10095.
    20. T. Mel’nyk and C. Rohde, “Asymptotic approximations for semilinear parabolic convection-dominated transport problems in thin graph-like networks,” J. Math. Anal. Appl., vol. 529, Art. no. 1, 2024, doi: 10.1016/j.jmaa.2023.127587.
    21. A. Braun, M. Kohler, S. Langer, and H. Walk, “Convergence rates for shallow neural networks learned by gradient descent,” Bernoulli, vol. 30, Art. no. 1, 2024, doi: 10.3150/23-bej1605.
    22. A. Kharitenko and C. W. Scherer, “On the exactness of a stability test for Lur’e systems with slope-restricted nonlinearities,” IEEE Transactions on Automatic Control, 2024, doi: 10.1109/TAC.2024.3362859.
    23. T. J. Meijer, T. Holicki, S. J. A. M. v. d. Eijnden, C. W. Scherer, and W. P. M. H. Heemels, “The Non-Strict Projection Lemma,” IEEE Transactions on Automatic Control, pp. 1–8, 2024, doi: 10.1109/TAC.2024.3371374.
    24. R. Herkert, P. Buchfink, T. Wenzel, B. Haasdonk, P. Toktaliev, and O. Iliev, “Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data,” Mathematics, vol. 12, Art. no. 13, 2024, doi: 10.3390/math12132111.
    25. P. “Knobloch, D. “Kuzmin, and A. “Jha, “Well-balanced convex limiting for finite element discretizations of steady convection-diffusion-reaction equations,” 2024.
    26. T. C. Corso, M. Hassan, A. Jha, and B. Stamm, “An $L^2$-maximum principle for circular arcs on the disk,” 2024.
    27. 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.
    28. J. Magiera and C. Rohde, “A Multiscale Method for Two-Component, Two-Phase Flow with a Neural Network Surrogate,” Communications on Applied Mathematics and Computation, 2024, doi: 10.1007/s42967-023-00349-8.
    29. M. Horsch et al., “Exploration of core concepts required for mid-and domain-level ontology development to facilitate explainable-AI-readiness of data and models,” 2024.
    30. M. Nottoli, E. Vanich, L. Cupellini, G. Scalmani, C. Pelosi, and F. Lipparini, “Importance of Polarizable Embedding for Computing Optical Rotation: The Case of Camphor in Ethanol,” The Journal of Physical Chemistry Letters, pp. 7992–7999, Jul. 2024, doi: 10.1021/acs.jpclett.4c01550.
    31. G. C. Hsiao, T. Sánchez-Vizuet, and W. L. Wendland, “Boundary-field formulation for transient electromagnetic scattering by dielectric scatterers and coated conductors,” SIAM J. Math. Analysis, to appear, 2024. doi: https://doi.org/10.48550/arXiv.2406.05367.
    32. M. Kohr, V. Nistor, and W. L. Wendland, “The Stokes operator on manifolds with cylindrical ends,” Journal of Differential Equations, Art. no. 407, 2024, doi: https://doi.org/10.1016/j.jde.2024.06.017.
    33. M. Nitzsche and B. N. Hahn, “Dynamic image reconstruction in MPI with RESESOP-Kaczmarz,” 2024, doi: 10.18416/IJMPI.2024.2411002.
    34. P. Strohbeck, M. Discacciati, and I. Rybak, “Optimized Schwarz method for the Stokes-Darcy problem with generalized interface conditions,” J. Comput. Phys. (submitted), 2024.
    35. J. Keim, H.-C. Konan, and C. Rohde, “A Note on Hyperbolic Relaxation of the Navier-Stokes-Cahn-Hilliard system for incompressible two-phase flow.” 2024. [Online]. Available: https://arxiv.org/abs/2412.11904
    36. M. Hörl and C. Rohde, “Rigorous Derivation of Discrete Fracture Models for Darcy Flow in the Limit of Vanishing Aperture,” Netw. Heterog. Media, vol. 19, Art. no. 1, 2024, doi: 10.3934/nhm.2024006.
    37. M. Alkämper, J. Magiera, and C. Rohde, “An Interface-Preserving Moving Mesh in Multiple Space Dimensions,” ACM Trans. Math. Softw., vol. 50, Art. no. 1, Mar. 2024, doi: 10.1145/3630000.
    38. T. Mel’nyk and C. Rohde, “Reduced-dimensional modelling for nonlinear convection-dominated flow in cylindric domains,” Nonlinear Differ. Equ. Appl., vol. 31:105, 2024, doi: https://doi.org/10.1007/s00030-024-00997-6.
    39. C. A. Beschle and A. Barth, “Quasi continuous level Monte Carlo for random elliptic PDEs,” in Hinrichs, A., Kritzer, P., Pillichshammer, F. (eds) Monte Carlo and Quasi-Monte Carlo Methods. MCQMC 2022, vol. 460, Springer Proceedings in Mathematics & Statistics, 2024, pp. 3–31. doi: 10.1007/978-3-031-59762-6_1.
    40. P. Strohbeck and I. Rybak, “Efficient preconditioners for coupled Stokes-Darcy problems with MAC scheme: Spectral analysis and numerical study,” J. Sci. Comput. (submitted), 2024.
    41. 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.
    42. M. Lukácová-Medvid’ová and C. Rohde, “Mathematical Challenges for the Theory of Hyperbolic Balance Laws in Fluid Mechanics: Complexity, Scales, Randomness,” Jahresber. Dtsch. Math.-Ver., vol. 126, Art. no. 4, 2024, doi: 10.1365/s13291-024-00290-6.
    43. R. Barthwal and T. R. Sekhar, “On a degenerate boundary value problem to relativistic magnetohydrodynamics with a general pressure law,” Zeitschrift für angewandte Mathematik und Physik, Art. no. 75, 2024, doi: 10.1007/s00033-024-02354-0.
    44. L. Ruan and I. Rybak, “Stokes-Brinkman-Darcy models for coupled fluid-porous systems: derivation, analysis and validation,” Appl. Math. Comp. (submitted), 2024.
    45. T. Mel’nyk and C. Rohde, “Puiseux asymptotic expansions for convection-dominated transport problems in thin graph-like networks: strong boundary interactions,” Asymptotic Analysis, vol. 137, pp. 27–52, 2024, doi: 10.3233/ASY-231876.
    46. I. Giannoulis, B. Schmidt, and G. Schneider, “NLS approximation for a scalar FPUT system on a 2D square lattice with a cubic nonlinearity,” J. Math. Anal. Appl., vol. 540, Art. no. 2, 2024, doi: 10.1016/j.jmaa.2024.128625.
    47. 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.
    48. M. Hammer et al., “A new method to design energy-conserving surrogate models for the coupled, nonlinear responses of intervertebral discs,” Biomechanics and Modeling in Mechanobiology, vol. 23, Art. no. 3, Jun. 2024, doi: 10.1007/s10237-023-01804-4.
    49. C. Beschle and A. Barth, “Complexity analysis of quasi continuous level Monte Carlo,” ESAIM: Mathematical Modelling and Numerical Analysis, 2024, doi: 10.1051/m2an/2024039.
    50. W.-P. Düll, D. Engl, and C. Kreisbeck, “A variational perspective on auxetic metamaterials of checkerboard-type,” Arch. Ration. Mech. Anal., vol. 248, Art. no. 3, 2024, doi: 10.1007/s00205-024-01989-7.
    51. T. Dohnal, D. E. Pelinovsky, and G. Schneider, “Travelling modulating pulse solutions with small tails for a nonlinear wave equation in periodic media,” Nonlinearity, vol. 37, Art. no. 5, 2024, doi: 10.1088/1361-6544/ad3097.
    52. M. Heß and G. Schneider, “The validity of the derivative NLS approximation for systems with cubic nonlinearities,” J. Differential Equations, vol. 410, pp. 251–277, 2024, doi: 10.1016/j.jde.2024.07.024.
    53. S. Gilg and G. Schneider, “Approximation of a two-dimensional Gross-Pitaevskii equation with a periodic potential in the tight-binding limit,” Math. Nachr., vol. 297, Art. no. 10, 2024, doi: 10.1002/mana.202300322.
    54. T. Lamm and G. Schneider, “Diffusive stability and self-similar decay for the harmonic map heat flow,” J. Differential Equations, vol. 394, pp. 320–344, 2024, doi: 10.1016/j.jde.2024.03.017.
    55. F. Musco and A. Barth, “Deep learning methods for stochastic Galerkin approximations of elliptic random PDEs.” 2024. [Online]. Available: https://arxiv.org/abs/2409.08063
    56. T. Mel’nyk and C. Rohde, “Muskat-Leverett two-phase flow in thin cylindric porous media: Asymptotic approach.” 2024. [Online]. Available: https://arxiv.org/abs/2411.02923
  3. 2023

    1. F. Bamer, F. Ebrahem, B. Markert, and B. Stamm, “Molecular Mechanics of Disordered Solids,” Archives of computational methods in engineering, vol. 30, Art. no. 3, 2023, doi: 10.1007/s11831-022-09861-1.
    2. P. Brehmer, M. F. Herbst, S. Wessel, M. Rizzi, and B. Stamm, “Reduced basis surrogates for quantum spin systems based on tensor networks,” Physical Review E, Aug. 2023, doi: 10.1103/PhysRevE.108.025306.
    3. E. Cancès, M. F. Herbst, G. Kemlin, A. Levitt, and B. Stamm, “Numerical stability and efficiency of response property calculations in density functional theory,” Letters in Mathematical Physics, Feb. 2023, doi: 10.1007/s11005-023-01645-3.
    4. I. Kröker, S. Oladyshkin, and I. Rybak, “Global sensitivity analysis using multi-resolution polynomial chaos expansion for coupled Stokes-Darcy flow problems,” Comput. Geosci., 2023, doi: 10.1007/s10596-023-10236-z.
    5. C. Lienstromberg, S. Schiffer, and R. Schubert, “A data-driven approach to viscous fluid mechanics: the stationary case,” Arch. Ration. Mech. Anal., vol. 247, Art. no. 2, 2023, doi: 10.1007/s00205-023-01849-w.
    6. C. Lienstromberg, S. Schiffer, and R. Schubert, “A variational approach to the non-newtonian Navier-Stokes equations,” 2023. doi: doi:10.48550/ARXIV.2312.03546.
    7. D. Pelinovsky and G. Schneider, “KP-II approximation for a scalar Fermi-Pasta-Ul system on a 2D square lattice,” SIAM J. Appl. Math., vol. 83, Art. no. 1, 2023, doi: 10.1137/22M1509369.
    8. D. Maier, W. Reichel, and G. Schneider, “Breather solutions for a semilinear Klein-Gordon equation on a periodic metric graph,” J. Math. Anal. Appl., vol. 528, Art. no. 2, 2023, doi: 10.1016/j.jmaa.2023.127520.
    9. T. Haas, B. de Rijk, and G. Schneider, “Validity of Whitham’s modulation equations for dissipative systems with a conservation law: phase dynamics in a generalized Ginzburg-Landau system,” Indiana Univ. Math. J., vol. 72, Art. no. 1, 2023, doi: 10.1512/iumj.2023.72.9297.
    10. R. Fukuizumi, Y. Gao, G. Schneider, and M. Takahashi, “Pattern formation in 2D stochastic anisotropic Swift-Hohenberg equation,” Interdiscip. Inform. Sci., vol. 29, Art. no. 1, 2023, doi: 10.4036/iis.2023.a.03.
    11. J. Berberich, C. W. Scherer, and F. Allgower, “Combining Prior Knowledge and Data for Robust Controller Design,” IEEE Transactions on Automatic Control, vol. 68, Art. no. 8, 2023, doi: 10.1109/tac.2022.3209342.
    12. B. Hahn and B. Wirth, “Convex reconstruction of moving particles with inexact motion model,” PAMM, vol. 23, Art. no. 2, Sep. 2023, doi: 10.1002/pamm.202300054.
    13. S. Burbulla, M. Hörl, and C. Rohde, “Flow in Porous Media with Fractures of Varying Aperture,” SIAM J. Sci. Comput, vol. 45, Art. no. 4, 2023, doi: 10.1137/22M1510406.
    14. 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.
    15. C. W. Scherer, “Robust Exponential Stability and Invariance Guarantees with General Dynamic O’Shea-Zames-Falb Multipliers,” Jun. 2023, doi: 10.48550/ARXIV.2306.00571.
    16. T. A. Mel’nyk, Complex Analysis. Springer Nature Switzerland, 2023. doi: https://doi.org/10.1007/978-3-031-39615-1.
    17. G. Santin, T. Wenzel, and B. Haasdonk, “On the optimality of target-data-dependent kernel greedy interpolation in Sobolev Reproducing Kernel Hilbert Spaces.” 2023. [Online]. Available: https://arxiv.org/abs/2307.09811
    18. L. Theisen and B. Stamm, “A Scalable Two-Level Domain Decomposition Eigensolver for Periodic Schrödinger Eigenstates in Anisotropically Expanding Domains,” 2023. doi: 10.48550/arXiv.2311.08757.
    19. 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.
    20. 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.
    21. M. Kohr, V. Nistor, and W. L. Wendland, “Layer potentials and essentially translation invariant pseudodifferential operators on manifolds with cylindrical ends,” Postpandemic Operator Theory, Springer-Verlag Berlin, pp. 61–115, 2023. [Online]. Available: https://doi.org/10.48550/arXiv.2308.06308
    22. L. Györfi, T. Linder, and H. Walk, “Lossless Transformations and Excess Risk Bounds in Statistical Inference,” Entropy, vol. 25, Art. no. 10, 2023, doi: 10.3390/e25101394.
    23. S. Keckstein et al., “Sonomorphologic Changes in Colorectal Deep Endometriosis: The Long-Term Impact of Age and Hormonal Treatment,” Ultraschall in der Medizin - European Journal of Ultrasound, Art. no. EFirst, 2023, doi: 10.1055/a-2209-5653.
    24. 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, 2023. doi: 10.48550/ARXIV.2203.00075.
    25. C. A. Beschle and A. Barth, “Quasi continuous level Monte Carlo for random elliptic PDEs,” 2023.
    26. B. Schembera et al., “Building Ontologies and Knowledge Graphs for Mathematics and its Applications,” in Proceedings of the Conference on Research Data Infrastructure, 2023. doi: 10.52825/cordi.v1i.255.
    27. M. Brennenstuhl, R. Otto, B. Schembera, and U. Eicker, “Optimized Dimensioning and Economic Assessment of Decentralized Hybrid Small Wind and PV Power Systems for Residential Buildings,” 2023. [Online]. Available: https://www.researchsquare.com/article/rs-3677621/latest.pdf
    28. C. W. Scherer, C. Ebenbauer, and T. Holicki, “Optimization Algorithm Synthesis based on Integral Quadratic Constraints: A Tutorial,” 2023, doi: 10.48550/ARXIV.2306.00565.
    29. D. Gramlich, C. W. Scherer, H. Häring, and C. Ebenbauer, “Synthesis of constrained robust feedback policies and model predictive control,” arXiv, 2023. doi: 10.48550/ARXIV.2310.11404.
    30. T. Holicki and C. W. Scherer, “IQC based analysis and estimator design for discrete-time systems affected by impulsive uncertainties,” Nonlinear Analysis: Hybrid Systems, vol. 50, p. 101399, Nov. 2023, doi: 10.1016/j.nahs.2023.101399.
    31. E. Eggenweiler, J. Nickl, and I. Rybak, “Justification of generalized interface conditions 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. 275–283. doi: 10.1007/978-3-031-40864-9_22.
    32. B. Hilder, B. de Rijk, and G. Schneider, “Moving modulating pulse and front solutions of permanent form in a FPU model with nearest and next-to-nearest neighbor interaction,” SIAM J. Appl. Dyn. Syst., vol. 22, Art. no. 2, 2023, doi: 10.1137/22M1502902.
    33. T. Holicki and C. W. Scherer, “Input-Output-Data-Enhanced Robust Analysis via Lifting,” IFAC-PapersOnLine, vol. 56, Art. no. 2, 2023, doi: 10.1016/j.ifacol.2023.10.047.
    34. 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., Cham: Springer Nature Switzerland, 2023, pp. 375–383.
    35. 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.
    36. D. Seus, F. A. Radu, and C. Rohde, “Towards hybrid two-phase modelling using linear domain decomposition,” Numer. Methods Partial Differential Equations, vol. 39, Art. no. 1, 2023, doi: https://doi.org/10.1002/num.22906.
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    40. T. J. Meijer, T. Holicki, S. J. A. M. v. d. Eijnden, C. W. Scherer, and W. P. M. H. Heemels, “The Non-Strict Projection Lemma.” arXiv, 2023. doi: 10.48550/ARXIV.2305.08735.
    41. 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.
    42. 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, Art. no. 164, 2023, [Online]. Available: http://jmlr.org/papers/v24/22-0937.html
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    44. H. Afşer, L. Györfi, and H. Walk, “Classification With Repeated Observations,” IEEE Signal Processing Letters, vol. 30, pp. 1522–1526, 2023, doi: 10.1109/LSP.2023.3326057.
    45. J. Dippon, J. Gwinner, A. A. Khan, and M. Sama, “A new regularized stochastic approximation framework for stochastic inverse problems,” Nonlinear Anal. Real World Appl., vol. 73, p. Paper No. 103869, 29, 2023, doi: 10.1016/j.nonrwa.2023.103869.
    46. E. Eggenweiler and I. Rybak, “Higher-order coupling conditions for arbitrary flows in Stokes-Darcy systems,” J. Fluid Mech. (submitted), 2023.
    47. B. Hilder, B. de Rijk, and G. Schneider, “Nonlinear stability of periodic roll solutions in the real Ginzburg-Landau equation against $C_ub^m$-perturbations,” Comm. Math. Phys., vol. 400, Art. no. 1, 2023, doi: 10.1007/s00220-022-04619-z.
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    49. P. Schwahn, U. Semmelmann, and G. Weingart, “Stability of the Non-Symmetric Space $E_7/PSO(8)$.” 2023.
    50. C. A. Rösinger and C. W. Scherer, “Gain-Scheduling Controller Synthesis for Networked Systems with Full Block Scalings,” 2023, doi: 10.1109/TAC.2023.3329851.
    51. C. A. Rösinger and C. W. Scherer, “Gain-Scheduling Controller Synthesis for Nested Systems With Full Block Scalings,” IEEE Transactions on Automatic Control, pp. 1–16, 2023, doi: 10.1109/TAC.2023.3329851.
    52. E. Cancès, M. F. Herbst, G. Kemlin, A. Levitt, and B. Stamm, “Numerical stability and efficiency of response property calculations in density functional theory,” Letters in Mathematical Physics, vol. 113, Art. no. 1, Feb. 2023, doi: 10.1007/s11005-023-01645-3.
    53. F. Pes, É. Polack, P. Mazzeo, G. Dusson, B. Stamm, and F. Lipparini, “A Quasi Time-Reversible Scheme Based on Density Matrix Extrapolation on the Grassmann Manifold for Born–Oppenheimer Molecular Dynamics,” The Journal of Physical Chemistry Letters, pp. 9720–9726, Oct. 2023, doi: 10.1021/acs.jpclett.3c02098.
    54. 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,” SIAM Journal on Scientific Computing, vol. 45, Art. no. 3, May 2023, doi: 10.1137/22m1493318.
    55. P. Buchfink, S. Glas, and B. Haasdonk, “Approximation Bounds for Model Reduction on Polynomially Mapped Manifolds.” 2023. [Online]. Available: https://arxiv.org/abs/2312.00724
    56. W. L. Wendland, “My relation with GAMM,” GAMM Rundbrief. [Online]. Available: https://www.gamm.org/wp-content/uploads/2024/03/GAMM_1-23_web.pdf
    57. L. Ruan and I. Rybak, “Stokes-Brinkman-Darcy models for coupled free-flow and porous-medium systems,” 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. 365–373. doi: 10.1007/978-3-031-40864-9_31.
    58. M. M. Morato, T. Holicki, and C. W. Scherer, “Stabilizing Model Predictive Control Synthesis using Integral Quadratic Constraints and Full-Block Multipliers,” International journal of robust and nonlinear control, vol. 33, Art. no. 18, 2023, doi: https://doi.org/10.1002/rnc.6952.
    59. P.-A. Nagy and U. Semmelmann, “Eigenvalue estimates for 3-Sasaki structures.” 2023.
    60. M. Nottoli et al., “QM/AMOEBA description of properties and dynamics of embedded molecules,” WIREs Computational Molecular Science, vol. 13, Art. no. 6, Jun. 2023, doi: 10.1002/wcms.1674.
    61. A. Jha, V. John, and P. Knobloch, “Adaptive Grids in the Context of Algebraic Stabilizations for Convection-Diffusion-Reaction Equations,” SIAM Journal on Scientific Computing, vol. 45, Art. no. 4, Aug. 2023, doi: 10.1137/21m1466360.
    62. T. Wenzel, G. Santin, and B. Haasdonk, “Analysis of Target Data-Dependent Greedy Kernel Algorithms: Convergence Rates for f -, f · P - and f /P -greedy,” Constructive Approximation, vol. 57, Art. no. 1, Feb. 2023, doi: 10.1007/s00365-022-09592-3.
    63. 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.
    64. M. Griesemer and M. Hofacker, “On the weakness of short-range interactions in Fermi gases,” Lett. Math. Phys., vol. 113, Art. no. 1, 2023, doi: 10.1007/s11005-022-01624-0.
    65. 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, Art. no. 3, Apr. 2023, doi: 10.1007/s11242-023-01919-3.
    66. 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., 2023, doi: 10.1007/s10596-023-10228-z.
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    68. F. A. Taha, S. Yan, and E. Bitar, “A Distributionally Robust Approach to Regret Optimal Control using the Wasserstein Distance,” in 2023 62nd IEEE Conference on Decision and Control (CDC), 2023, pp. 2768–2775. doi: 10.1109/CDC49753.2023.10384311.
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    72. D. Gramlich, P. Pauli, C. W. Scherer, F. Allgöwer, and C. Ebenbauer, “Convolutional Neural Networks as 2-D systems,” Mar. 2023, doi: 10.48550/ARXIV.2303.03042.
    73. G. Dusson, I. M. Sigal, and B. Stamm, “Analysis of the Feshbach-Schur method for the Fourier spectral discretizations of Schrödinger operators,” Mathematics of computation, vol. 92, Art. no. 340, 2023, doi: 10.1090/mcom/3774.
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    76. 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., Cham: Springer International Publishing, 2023, pp. 443–450.
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  4. 2022

    1. 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.
    2. T. Boege et al., “Research-Data Management Planning in the German Mathematical Community.” arXiv, 2022. doi: 10.48550/ARXIV.2211.12071.
    3. D. Hägele et al., “Uncertainty Visualization: Fundamentals and Recent Developments,” it - Information Technology, vol. 64, Art. no. 4–5, 2022, doi: 10.1515/itit-2022-0033.
    4. 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. 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, p. A49––, 2022, doi: DOI: 10.1017/jfm.2022.308.
    6. 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
    7. M. Hassan et al., “Manipulating Interactions between Dielectric Particles with Electric Fields : A General Electrostatic Many-Body Framework,” Journal of chemical theory and computation, vol. 18, Art. no. 10, 2022, doi: 10.1021/acs.jctc.2c00008.
    8. N. Hornischer, “Model Order Reduction with Transformed Modes for Electrophysiological Simulations,” bathesis, 2022.
    9. S. Burbulla, A. Dedner, M. Hörl, and C. Rohde, “Dune-MMesh: The Dune Grid Module for Moving Interfaces,” J. Open Source Softw., vol. 7, Art. no. 74, 2022, doi: 10.21105/joss.03959.
    10. M. Klink, “Time Error Estimators and Adaptive Time-stepping Schemes,” bathesis, 2022.
    11. F. Echterdiek et al., “Impact of the explanting surgeon’s impression of donor arteriosclerosis on outcome of kidney transplantations from donors aged ≥65 years,” Langenbeck’s Archives of Surgery, vol. 407, Art. no. 2, Mar. 2022, doi: 10.1007/s00423-021-02383-7.
    12. M. Cekić, T. Lefeuvre, A. Moroianu, and U. Semmelmann, “Towards Brin’s conjecture on frame flow ergodicity: new progress and perspectives.” 2022.
    13. D. Hägele et al., “Uncertainty visualization : Fundamentals and recent developments,” Information technology, vol. 64, Art. no. 4–5, 2022, doi: 10.1515/itit-2022-0033.
    14. G. Santin, T. Karvonen, and B. Haasdonk, “Sampling based approximation of linear functionals in reproducing kernel Hilbert spaces,” BIT Numerical Mathematics, vol. 62, Art. no. 1, Mar. 2022, doi: 10.1007/s10543-021-00870-3.
    15. 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.
    16. 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
    17. D. Gramlich, C. W. Scherer, and C. Ebenbauer, “Robust Differential Dynamic Programming,” in 61st IEEE Conf. Decision and Control, 2022. doi: 10.1109/cdc51059.2022.9992569.
    18. C. Scherer, “Dissipativity and Integral Quadratic Constraints, Tailored computational robustness tests for complex interconnections,” IEEE Control Systems Magazine, vol. 42, Art. no. 3, 2022, [Online]. Available: https://arxiv.org/abs/2105.07401
    19. 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.
    20. T. Holicki and C. W. Scherer, “IQC Based Analysis and Estimator Design for Discrete-Time Systems Affected by Impulsive Uncertainties,” Dec. 2022.
    21. 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.
    22. 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.
    23. 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.
    24. 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, Art. no. 9, 2022, doi: 10.3934/dcdss.2021119.
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    26. 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., Cham: Springer International Publishing, 2022, pp. 378–386.
    27. 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., Springer International Publishing, 2022, pp. 402–409.
    28. 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.
    29. C. A. Rösinger and C. W. Scherer, “Gain-Scheduling Controller Synthesis for Networked Systems with Full Block Scalings,” Oct. 2022.
    30. T. Holicki, “A Complete Analysis and Design Framework for Linear Impulsive and Related Hybrid Systems,” University of Stuttgart, 2022. doi: 10.18419/opus-12158.
    31. M. T. Horsch and B. Schembera, “Documentation of epistemic metadata by a mid-level ontology of cognitive processes,” Proc. JOWO 2022, 2022.
    32. 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.
    33. T. Mel’nyk and A. V. Klevtsovskiy, “Asymptotic expansion for the solution of a convection-diffusion problem in a thin graph-like junction,” Asymptotic Analysis, vol. 130, Art. no. 3–4, 2022, doi: 10.3233/ASY-221761.
    34. 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, Art. no. 2, 2022, doi: 10.1007/s00332-021-09750-0.
    35. R. Frank, A. Laptev, and T. Weidl, Schrödinger Operators: Eigenvalues and Lieb–Thirring Inequalities. 2022, p. 512.
    36. 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, Art. no. 11, 2022, doi: 10.1063/5.0078983.
    37. 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 Fluid Mechanics and its Application. , Springer International Publishing, 2022. doi: 10.1007/978-3-031-09008-0_4.
    38. 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.
    39. S. Gilg, G. Schneider, and H. Uecker, “Nonlinear dynamics of modulated waves on graphene like quantum graphs,” Math. Nachr., vol. 295, Art. no. 11, 2022, doi: 10.1002/mana.202100009.
    40. G. Dusson, I. Sigal, and B. Stamm, “Analysis of the Feshbach–Schur method for the Fourier spectral discretizations of Schrödinger operators,” Mathematics of Computation, vol. 92, Art. no. 339, Sep. 2022, doi: 10.1090/mcom/3774.
    41. M. Nottoli, A. Mikhalev, B. Stamm, and F. Lipparini, “Coarse-Graining ddCOSMO through an Interface between Tinker and the ddX Library,” The Journal of Physical Chemistry B, vol. 126, Art. no. 43, Oct. 2022, doi: 10.1021/acs.jpcb.2c04579.
    42. 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.
    43. 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., Cham: Springer International Publishing, 2022, pp. 410–418.
    44. R. Merkle and A. Barth, “On some distributional properties of subordinated Gaussian random fields,” Methodol Comput Appl Probab, 2022.
    45. 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.
    46. D. Holzmüller and I. Steinwart, “Training two-layer ReLU networks with gradient descent is inconsistent,” Journal of Machine Learning Research, vol. 23, Art. no. 181, 2022, [Online]. Available: http://jmlr.org/papers/v23/20-830.html
    47. P. Benner et al., “Die mathematische Forschungsdateninitiative in der NFDI: MaRDI (Mathematical Research Data Initiative),” GAMM Rundbrief, vol. 2022, Art. no. 1, May 2022.
    48. R. L. Frank, A. Laptev, and T. Weidl, “An improved one-dimensional Hardy inequality,” J. Math. Sci. (N.Y.), vol. 268, Art. no. 3, Problems in mathematical analysis. No. 118, 2022, doi: 10.1007/s10958-022-06199-8.
    49. 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, Art. no. 1, 126464, 2022, [Online]. Available: https://doi.org/10.1016/j.jmaa.2022.126464
    50. A. Kharitenko and C. W. Scherer, “On the exactness of a stability test for Lur’e systems with slope-restricted nonlinearities,” Oct. 2022.
    51. 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, Art. no. 2, 2022, doi: 10.1007/s00332-021-09750-0.
    52. E. Eggenweiler, “Interface conditions for arbitrary flows in Stokes-Darcy systems : derivation, analysis and validation.” Universität Stuttgart, 2022. doi: 10.18419/OPUS-12573.
    53. R. Fukuizumi and G. Schneider, “Interchanging space and time in nonlinear optics modeling and dispersion management models,” J. Nonlinear Sci., vol. 32, Art. no. 3, 2022, doi: 10.1007/s00332-022-09788-8.
    54. M. Klumpp and G. Schneider, “The Schrödinger approximation for the Helmholtz equation if the refractive index is a step function,” Wave Motion, vol. 110, p. Paper No. 102891, 6, 2022, doi: 10.1016/j.wavemoti.2022.102891.
    55. M. Klumpp and G. Schneider, “A note on the validity of the Schrödinger approximation for the Helmholtz equation,” J. Appl. Anal., vol. 28, Art. no. 1, 2022, doi: 10.1515/jaa-2021-2058.
    56. A. Barth and A. Stein, “Numerical analysis for time-dependent advection-diffusion problems with random discontinuous coefficients,” ESAIM: M2AN, vol. 56, Art. no. 5, 2022, doi: 10.1051/m2an/2022054.
    57. A. Mikhalev, M. Nottoli, and B. Stamm, “Linearly scaling computation of ddPCM solvation energy and forces using the fast multipole method,” The Journal of Chemical Physics, vol. 157, Art. no. 11, Sep. 2022, doi: 10.1063/5.0104536.
    58. T. Focks, F. Bamer, B. Markert, Z. Wu, and B. Stamm, “Displacement field splitting of defective hexagonal lattices,” Physical Review B, Jul. 2022, doi: 10.1103/PhysRevB.106.014105.
    59. 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.
    60. M. Nitzsche, H. Albers, T. Kluth, and B. Hahn, “Compensating model imperfections during image reconstruction via Resesop,” International Journal on Magnetic Particle Imaging, p. Vol 8 No 1 Suppl 1 (2022), 2022, doi: 10.18416/IJMPI.2022.2203062.
    61. G. Schneider and M. Winter, “The amplitude system for a imultaneous short-wave Turing and long-wave Hopf instability,” Discrete Contin. Dyn. Syst. Ser. S, vol. 15, Art. no. 9, 2022, doi: 10.3934/dcdss.2021119.
    62. 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
    63. M. Griesemer and M. Hofacker, “From Short-Range to Contact Interactions in Two-dimensional Many-Body Quantum Systems,” Annales Henri Poincaré, vol. 23, Art. no. 8, Aug. 2022, doi: 10.1007/s00023-021-01149-7.
    64. M. Griesemer, “Ground states of atoms and molecules in non-relativistic QED,” in The Physics and Mathematics of Elliott Lieb, EMS Press, 2022, pp. 437–450. doi: 10.4171/90-1/18.
    65. 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, Art. no. 1, 2022, doi: 10.1080/03605302.2021.1957929.
    66. 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.
    67. T. Holicki and C. W. Scherer, “Input-Output-Data-Enhanced Robust Analysis via Lifting,” Nov. 2022.
    68. B. Hilder and U. Sharma, “Quantitative coarse-graining of Markov chains.” 2022.
    69. C. Beschle, “Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5,” 2022, doi: 10.18419/darus-3154.
    70. 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, Art. no. 5, Sep. 2022, doi: 10.1137/21m1456005.
    71. 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.
    72. J. Wirth and M. E. Sebih, “On a wave equation with singular dissipation,” Mathematische Nachrichten, vol. 295, Art. no. 8, 2022, doi: 10.1002/mana.202000076.
    73. P. Buchfinck, S. Glas, and B. Haasdonk, “Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems,” 2022.
    74. 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, Art. no. 0, 2022, doi: 10.3934/ipi.2022018.
    75. C. Beschle and A. Barth, “Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5.” 2022. doi: 10.18419/darus-3154.
    76. F. Echterdiek et al., “Outcome of kidney transplantations from ≥65‐year‐old deceased donors with acute kidney injury,” Clinical Transplantation, vol. 36, Art. no. 5, Feb. 2022, doi: 10.1111/ctr.14612.
    77. 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.
    78. C. W. Scherer, “Dissipativity, Convexity and Tight O\textquotesingleShea-Zames-Falb Multipliers for Safety Guarantees,” IFAC-PapersOnLine, vol. 55, Art. no. 30, 2022, doi: 10.1016/j.ifacol.2022.11.044.
    79. 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, IEEE, Dec. 2022. doi: 10.1109/cdc51059.2022.9993144.
    80. 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.
    81. T. Holicki and C. W. Scherer, “A Dynamic S-Procedure for Dynamic Uncertainties,” in IFAC-PapersOnline, 2022, pp. 103–108. doi: 10.1016/j.ifacol.2022.09.331.
    82. 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.
    83. B. Hilder, “Modulating traveling fronts in a dispersive Swift-Hohenberg equation coupled to an additional conservation law,” J. Math. Anal. Appl., vol. 513, Art. no. 2, 2022, doi: 10.1016/j.jmaa.2022.126224.
  5. 2021

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    2. 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.
    3. 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, Art. no. 6, 2021, doi: doi.org/10.1007/s00332-021-09755-9.
    4. 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.
    5. 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.
    6. T. Jentsch and G. Weingart, “Jacobi relations on naturally reductive spaces,” ANNALS OF GLOBAL ANALYSIS AND GEOMETRY, vol. 59, 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. 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 Oberwolfach Rep., vol. 41. 2021. doi: 10.14760/OWR-2021-41.
    9. 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., Cham: Springer International Publishing, 2021, pp. 87–102. doi: https://doi.org/10.1007/978-3-030-86523-8_6.
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    11. 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.
    12. 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, Art. no. 12, 2021, doi: 10.1002/mma.7167.
    13. A. Wagner et al., “Permeability estimation of regular porous structures: a benchmark for comparison of methods,” Transp. Porous Med., vol. 138, pp. 1–23, 2021, doi: 10.1007/s11242-021-01586-2.
    14. C. Rohde and L. Von Wolff, “A ternary Cahn–Hilliard–Navier–Stokes model for two-phase flow with precipitation and dissolution,” Mathematical Models and Methods in Applied Sciences, vol. 31, Art. no. 01, 2021, doi: 10.1142/S0218202521500019.
    15. A. Krämer et al., “Multi-physics multi-scale HPC simulations of skeletal muscles,” in High Performance Computing in Science and Engineering ’20: Transactions of the High Performance Computing Center, Stuttgart(HLRS) 2020, W. E. Nagel, D. H. Kröner, and M. M. Resch, Eds., 2021. doi: 10.1007/978-3-030-80602-6_13.
    16. S. Schricker, D. C. Monje, J. Dippon, M. Kimmel, M. D. Alscher, and M. Schanz, “Physician-guided, hybrid genetic testing exerts promising effects on health-related behavior without compromising quality of life,” Scientific Reports, vol. 11, Art. no. 1, Apr. 2021, doi: 10.1038/s41598-021-87821-8.
    17. C. Scherer and C. Ebenbauer, “Convex Synthesis of Accelerated Gradient Algorithms,” SIAM J. Contr. Optim. (to appear), 2021, [Online]. Available: https://arxiv.org/abs/2102.06520
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    19. G. Santin and B. Haasdonk, “Kernel methods for surrogate modeling,” in Model Order Reduction, vol. 1: System- and Data-Driven Methods and Algorithms, P. Benner, W. Schilders, S. Grivet-Talocia, A. Quarteroni, G. Rozza, and L. M. Silveira, Eds., de Gruyter, 2021, pp. 311–354.
    20. B. Haasdonk, T. Wenzel, G. Santin, and S. Schmitt, “Biomechanical Surrogate Modelling Using Stabilized Vectorial Greedy Kernel Methods,” 2021.
    21. T. Wenzel, G. Santin, and B. Haasdonk, “A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability and uniform point distribution,” 2021.
    22. L. Brencher and A. Barth, “Stochastic conservation laws with discontinuous flux functions: The multidimensional case,” 2021.
    23. 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.
    24. T. Ehring and B. Haasdonk, “Greedy sampling and approximation for realizing feedback control for high dimensional nonlinear systems,” 2021.
    25. T. Holicki and C. W. Scherer, “Revisiting and Generalizing the Dual Iteration for Static and Robust Output-Feedback Synthesis,” Int. J. Robust Nonlin., pp. 1–33, 2021, doi: 10.1002/rnc.5547.
    26. 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.
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    29. E. Eggenweiler and I. Rybak, “Effective coupling conditions for arbitrary flows in Stokes-Darcy systems,” Multiscale Model. Simul., vol. 19, pp. 731–757, 2021, doi: 10.1137/20M1346638.
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    32. 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.
    33. R. Lang, “On the eigenvalues of the non-self-adjoint Robin Laplacian on bounded domains and compact quantum graphs.,” Dissertation, Universität Stuttgart, Stuttgart, 2021. doi: 10.18419/opus-11428.
    34. T. B. Berrett, L. Gyorfi, and H. Walk, “Strongly universally consistent nonparametric regression and classification with privatised data,” ELECTRONIC JOURNAL OF STATISTICS, vol. 15, Art. no. 1, 2021, doi: 10.1214/21-EJS1845.
    35. 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.
    36. 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
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    38. L. Mehl, C. Beschle, A. Barth, and A. Bruhn, “An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation,” Proceedings of the International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), pp. 140–152, 2021, doi: 10.1007/978-3-030-75549-2_12.
    39. T. Wenzel, G. Santin, and B. Haasdonk, “Universality and Optimality of Structured Deep Kernel Networks.” arXiv, 2021. doi: 10.48550/ARXIV.2105.07228.
    40. K. Altmann and F. Witt, “Toric co-Higgs sheaves,” Journal of pure and applied algebra, vol. 225, Art. no. 8, 2021, doi: 10.1016/j.jpaa.2020.106634.
    41. L. von Wolff, F. Weinhardt, H. Class, J. Hommel, and C. Rohde, “Investigation of Crystal Growth in Enzymatically Induced Calcite Precipitation by Micro-Fluidic Experimental Methods and Comparison with Mathematical Modeling,” Transp. Porous Media, vol. 137, Art. no. 2, 2021, doi: 10.1007/s11242-021-01560-y.
    42. T. Hamm and I. Steinwart, “Adaptive Learning Rates for Support Vector Machines Working on Data with Low Intrinsic Dimension,” Ann. Statist., 2021.
    43. G. Stauch et al., “The Importance of Clinical Data for the Diagnosis of Breast Tumours in North Afghanistan,” Int. Jounal Breast Cancer, vol. Jul 30;2021, p. 6625239, 2021, doi: 10.1155/2021/6625239.
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    48. 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.
    49. T. Wenzel, G. Santin, and B. Haasdonk, “Analysis of target data-dependent greedy kernel algorithms: Convergence rates for $f$-, $f P$- and $f/P$-greedy.” arXiv, 2021. doi: 10.48550/ARXIV.2105.07411.
    50. T. Ehring and B. Haasdonk, “Feedback control for a coupled soft tissue system by kernel surrogates,” in Coupled Problems 2021, 2021. doi: 10.23967/coupled.2021.026.
    51. T. Holicki, C. W. Scherer, and S. Trimpe, “Controller Design via Experimental Exploration with Robustness Guarantees,” IEEE Control Syst. Lett., vol. 5, Art. no. 2, 2021, doi: 10.1109/LCSYS.2020.3004506.
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    56. T. Hamm and I. Steinwart, “Intrinsic Dimension Adaptive Partitioning for Kernel Methods,” Fakultät für Mathematik und Physik, Universität Stuttgart, 2021.
    57. C. Fiedler, C. W. Scherer, and S. Trimpe, “Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2021, pp. 7439–7447. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/16912
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    80. B. Haasdonk, “Model Order Reduction, Applications, MOR Software,” vol. 3, D. Gruyter, Ed., De Gruyter, 2021. doi: 10.1515/9783110499001.
    81. 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.
    82. J. Magiera, “A Molecular--Continuum Multiscale Solver for Liquid--Vapor Flow: Modeling and Numerical Simulation,” Ph.D. Thesis, 2021. doi: 10.18419/opus-11797.
    83. 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.
    84. T. Mel’nyk, “Asymptotic approximations for eigenvalues and eigenfunctions of a spectral problem in a thin graph-like junction with a concentrated mass in the node,” Analysis and Applications, vol. 19, Art. no. 05, 2021, doi: 10.1142/S0219530520500219.
    85. 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.
  6. 2020

    1. 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, Art. no. 2, 2020, doi: https://doi.org/10.1016/j.jmaa.2020.124005.
    2. T. Holicki and C. W. Scherer, “Output-Feedback Synthesis for a Class of Aperiodic Impulsive Systems,” in IFAC-PapersOnline, 2020, pp. 7299–7304. doi: 10.1016/j.ifacol.2020.12.981.
    3. 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., 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
    4. 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., Cham: Springer International Publishing, 2020, pp. 547–555. doi: https://doi.org/10.1007/978-3-030-43651-3_51.
    5. 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, 2020.
    6. 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, Art. no. 12, 2020, doi: 10.1088/1361-6420/abb5e1.
    7. 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., Cham: Springer International Publishing, 2020, pp. 446–453. doi: 10.1007/978-3-030-40616-5_42.
    8. J. C. Díaz-Ramos, M. Domínguez-Vázquez, and A. Kollross, “On homogeneous manifolds whose isotropy actions are polar,” manuscripta mathematica, vol. 161, Art. no. 1, Jan. 2020, doi: 10.1007/s00229-018-1077-1.
    9. 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
    10. 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.
    11. 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, Art. no. 7, 2020, doi: 10.1016/j.jde.2019.09.056.
    12. M. Geck, “Green functions and Glauberman degree-divisibility,” Annals of Mathematics, vol. 192, Art. no. 1, 2020, doi: 10.4007/annals.2020.192.1.4.
    13. 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, Art. no. 6, Jun. 2020, doi: 10.1002/zamm.201900230.
    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. Oesting and A. Schnurr, “Ordinal patterns in clusters of subsequent extremes of regularly varying time series,” Extremes, vol. 23, Art. no. 4, 2020, doi: 10.1007/s10687-020-00391-2.
    16. D. Göddeke, M. Schirwon, and N. Borg, “Smartphone-Apps im Mathematikstudium,” 2020, doi: 10.18419/darus-1147.
    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. 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, Art. no. 8, 2020, doi: 10.1002/zamm.201900186.
    19. S. Fischer and I. Steinwart, “Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm,” J. Mach. Learn. Res., Art. no. 205, 2020.
    20. S. Fischer and I. Steinwart, “Sobolev norm learning rates for regularized least-squares algorithms,” J. Mach. Learn. Res., vol. 21, Art. no. 205, Oct. 2020, [Online]. Available: http://jmlr.org/papers/v21/19-734.html
    21. 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.
    22. 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, Art. no. 1, 2020, doi: 10.1080/17476933.2019.1631293.
    23. 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
    24. I. Steinwart, “Reproducing Kernel Hilbert Spaces Cannot Contain all Continuous Functions on a Compact Metric Space,” Fakultät für Mathematik und Physik, Universität Stuttgart, 2020.
    25. D. Holzmüller and I. Steinwart, “Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent,” Fakultät für Mathematik und Physik, Universität Stuttgart, 2020.
    26. 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, Art. no. 4, 2020.
    27. G. Schneider, “The KdV approximation for a system with unstable resonances,” Math. Methods Appl. Sci., vol. 43, Art. no. 6, 2020.
    28. 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.
    29. S. Oladyshkin, F. Mohammadi, I. Kroeker, and W. Nowak, “Bayesian(3)Active Learning for the Gaussian Process Emulator Using Information Theory,” ENTROPY, vol. 22, Art. no. 8, Aug. 2020, doi: 10.3390/e22080890.
    30. L. A. Minorics, “Spectral asymptotics for Krein-Feller operators with respect to V-variable Cantor measures,” Forum Mathematicum, vol. 32, Art. no. 1, Jan. 2020, doi: 10.1515/forum-2018-0188.
    31. T. Haas, B. de Rijk, and G. Schneider, “MODULATION EQUATIONS NEAR THE ECKHAUS BOUNDARY: THE KdV EQUATION,” SIAM JOURNAL ON MATHEMATICAL ANALYSIS, vol. 52, Art. no. 6, 2020, doi: 10.1137/19M1266873.
    32. C. A. Rösinger and C. W. Scherer, “Lifting to Passivity for $H_2$-Gain-Scheduling Synthesis with Full Block Scalings,” in IFAC-PapersOnline, 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, Art. no. 109345, 2020, doi: https://doi.org/10.1016/j.jcp.2020.109345.
    34. A. Stein and A. Barth, “A Multilevel Monte Carlo Algorithm for Parabolic Advection-Diffusion Problems with Discontinuous Coefficients,” in Monte Carlo and Quasi-Monte Carlo Methods, B. Tuffin and P. L’Ecuyer, Eds., Cham: Springer International Publishing, 2020, pp. 445–466. doi: 10.1007/978-3-030-43465-6_22.
    35. 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, Art. no. 2, 2020.
    36. 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.
    37. 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.
    38. 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.
    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, Art. no. 4, 2020, doi: https://doi.org/10.1137/18M1210575.
    40. I. Berre et al., “Verification benchmarks for single-phase flow in three-dimensional fractured porous media.” 2020.
    41. 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, Art. no. 1, 2020, doi: 10.1007/s10444-020-09744-8.
    42. 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, vol. 134, in Lect. Notes Comput. Sci. Eng., vol. 134. , Springer, Cham, 2020, pp. 95–106. doi: 10.1007/978-3-030-39647-3\_6.
    43. C. A. Rösinger and C. W. Scherer, “A Flexible Synthesis Framework of Structured Controllers for Networked Systems,” IEEE Trans. Control Netw. Syst., vol. 7, Art. no. 1, 2020, doi: 10.1109/TCNS.2019.2914411.
    44. C. Lienstromberg and S. Müller, “Local strong solutions to a quasilinear degenerate fourth-order thin-film equation,” NoDEA Nonlinear Differential Equations Appl., vol. 27, Art. no. 2, 2020, doi: 10.1007/s00030-020-0619-x.
    45. J. Escher, P. Knopf, C. Lienstromberg, and B.-V. Matioc, “Stratified periodic water waves with singular density gradients,” Ann. Mat. Pura Appl. (4), vol. 199, Art. no. 5, 2020, doi: 10.1007/s10231-020-00950-1.
    46. S. Fischer, “Some new bounds on the entropy numbers of diagonal operators,” J. Approx. Theory, vol. 251, p. 105343, Mar. 2020, doi: 10.1016/j.jat.2019.105343.
    47. A. Kollross, “Octonions, triality, the exceptional Lie algebra F4 and polar actions on the Cayley hyperbolic plane,” International Journal of Mathematics, vol. 31, Art. no. 07, May 2020, doi: 10.1142/s0129167x20500512.
    48. 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.
    49. M. Geck, “ChevLie: Constructing Lie algebras and Chevalley groups,” Journal of Software for Algebra and Geometry, vol. 10, Art. no. 1, May 2020, doi: 10.2140/jsag.2020.10.41.
    50. 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., Vydavateľstvo SPEKTRUM, Aug. 2020, pp. 151–160. [Online]. Available: http://www.iam.fmph.uniba.sk/amuc/ojs/index.php/algoritmy/article/view/1577/829
    51. A. Barth and R. Merkle, “Subordinated Gaussian Random Fields in Elliptic Partial Differential Equations,” ArXiv e-prints, arXiv:2011.09311 math.NA, 2020.
    52. 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.
    53. 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, Art. no. 2, 2020, doi: 10.1093/imanum/drz004.
    54. 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.
    55. J. T. Gerstenberger, S. Burbulla, and D. Kröner, “Discontinuous Galerkin method for incompressible two-phase flows,” Submitted to: Springer Proceedings in Mathematics & Statistics, 2020.
    56. B. Hilder, “Modulating traveling fronts for the Swift-Hohenberg equation in the case of an additional conservation law,” Journal of Differential Equations, vol. 269, Art. no. 5, Aug. 2020, doi: 10.1016/j.jde.2020.03.033.
    57. 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, Cham: Birkhäuser, 2020, pp. 37–48.
    58. M. Griesemer, M. Hofacker, and U. Linden, “From short-range to contact interactions in the 1d Bose gas,” Math. Phys. Anal. Geom., vol. 23, Art. no. 2, 2020, doi: 10.1007/s11040-020-09344-4.
    59. 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.
    60. D. Maier, “Construction of breather solutions for nonlinear Klein-Gordon equations on periodic metric graphs,” JOURNAL OF DIFFERENTIAL EQUATIONS, vol. 268, Art. no. 6, Mar. 2020, doi: 10.1016/j.jde.2019.09.035.
    61. D. Maier, “BREATHER SOLUTIONS ON DISCRETE NECKLACE GRAPHS,” OPERATORS AND MATRICES, vol. 14, Art. no. 3, Sep. 2020, doi: 10.7153/oam-2020-14-48.
    62. 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 Trends in Mathematics. , Birkhäuser, 2020, pp. 51–97. doi: 10.1007/978-3-030-58215-9_3.
    63. 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, Art. no. 6, 2020, doi: 10.1137/19M1242434.
    64. J. Berberich, A. Koch, C. W. Scherer, and F. Allgöwer, “Robust data-driven state-feedback design,” in 2020 American Control Conference (ACC), Jul. 2020, pp. 1532–1538. doi: 10.23919/acc45564.2020.9147320.
    65. M. Geck, “On Jacob’s construction of the rational canonical form of a matrix,” The Electronic Journal of Linear Algebra, vol. 36, Art. no. 36, Apr. 2020, doi: 10.13001/ela.2020.5055.
    66. A. Vonica et al., “Apcdd1 is a dual BMP/Wnt inhibitor in the developing nervous system and skin,” Developmental Biology, vol. 464, Art. no. 1, Aug. 2020, doi: 10.1016/j.ydbio.2020.03.015.
    67. 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 Springer Proceedings in Mathematics & Statistics, vol. 323. Springer International Publishing, 2020, pp. 345–353. doi: 10.1007/978-3-030-43651-3_31.
    68. P.-A. Nagy and U. Semmelmann, “Conformal Killing forms in Kaehler geometry.” 2020.
    69. 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, Art. no. 2, Jan. 2020, doi: 10.1137/19m1239003.
    70. L. Ostrowski and C. Rohde, “Compressible multicomponent flow in porous media with Maxwell-Stefan diffusion,” Math. Meth. Appl. Sci., vol. 43, Art. no. 7, 2020, doi: 10.1002/mma.6185.
    71. 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., 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
    72. 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., Cham: Springer International Publishing, 2020, pp. 113–126. [Online]. Available: https://doi.org/10.1007/978-3-030-33338-6_9
    73. 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.
    74. U. Semmelmann, C. Wang, and M. Y.-K. Wang, “On the linear stability of nearly Kähler 6-manifolds,” Ann. Global Anal. Geom., vol. 57, Art. no. 1, 2020, doi: 10.1007/s10455-019-09686-5.
    75. 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.
    76. A. Barth and R. Merkle, “Subordinated Gaussian Random Fields,” ArXiv e-prints, arXiv:2012.06353 math.PR, 2020.
    77. L. Brencher and A. Barth, “Hyperbolic Conservation Laws with Stochastic Discontinuous Flux Functions,” in International Conference on Finite Volumes for Complex Applications, Springer, 2020, pp. 265–273.
    78. D. F. B. Häufle, I. Wochner, D. Holzmüller, D. Driess, M. Günther, and S. Schmitt, “Muscles Reduce Neuronal Information Load : Quantification of Control Effort in Biological vs. Robotic Pointing and Walking,” Frontiers In Robotics and AI, vol. 7, p. 77, 2020, doi: 10.3389/frobt.2020.00077.
    79. G. Rigaud and B. N. Hahn, “Reconstruction algorithm for 3D Compton scattering imaging with incomplete data,” Inverse Problems in Science and Engineering, vol. 29, Art. no. 7, 2020, doi: 10.1080/17415977.2020.1815723.
    80. M. Geck and G. Malle, “The character theory of finite groups of Lie type. A guided tour,” vol. 187, in Cambridge Studies in Advanced Mathematics, vol. 187. , Cambridge University Press, 2020, p. ix. doi: https://doi.org/10.1017/9781108779081.
    81. 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.
    82. 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.
    83. 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.
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  7. 2019

    1. 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.
    2. 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., Cham: Springer International Publishing, 2019, pp. 237–260. doi: 10.1007/978-3-030-02650-9_12.
    3. 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, Art. no. 1, 2019, doi: 10.1063/1.5064694.
    4. G. Baggio, S. Zampieri, and C. W. Scherer, “Gramian Optimization with Input-Power Constraints,” in 58th IEEE Conf. Decision and Control, 2019, pp. 5686–5691. doi: 10.1109/CDC40024.2019.9029169.
    5. T. Holicki and C. W. Scherer, “A Homotopy Approach for Robust Output-Feedback Synthesis,” in Proc. 27th. Med. Conf. Control Autom., 2019, pp. 87–93. doi: 10.1109/MED.2019.8798536.
    6. B. N. Hahn and M.-L. Kienle Garrido, “An efficient reconstruction approach for a class of dynamic imaging operators,” Inverse Problems, vol. 35, Art. no. 9, 2019, doi: 10.1088/1361-6420/ab178b.
    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, 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, 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, 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, Art. no. 2, 2019, doi: 10.3390/mca24020043.
    11. G. Santin and B. Haasdonk, “Kernel Methods for Surrogate Modelling,” University of Stuttgart, 2019.
    12. R. M. Colombo, P. G. LeFloch, C. Rohde, and K. Trivisa, “Nonlinear Hyperbolic Problems: Modeling, Analysis, and Numerics,” Oberwohlfach Rep., Art. no. 16, 2019, [Online]. Available: https://www.ems-ph.org/journals/show_issue.php?issn=1660-8933&vol=16&iss=2
    13. 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, p. x, 235 Seiten.
    14. L. Györfi and H. Walk, “Nearest neighbor based conformal prediction,” Annales de l’ISUP, vol. 63, Art. no. 2–3, 2019, [Online]. Available: https://hal.science/hal-03603867
    15. 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., Art. no. 126, 2019, doi: 10.1007/978-3-319-96415-7\_23.
    16. C. A. Rösinger and C. W. Scherer, “A Flexible Synthesis Framework of Structured Controllers for Networked Systems,” IEEE Trans. Control Netw. Syst., vol. 7, Art. no. 1, 2019, doi: 10.1109/TCNS.2019.2914411.
    17. 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.
    18. 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.
    19. M. Köppel et al., “Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario,” Comput. Geosci., vol. 2, Art. no. 23, 2019, doi: https://doi.org/10.1007/s10596-018-9785-x.
    20. 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.
    21. U. Semmelmann and G. Weingart, “The standard Laplace operator,” Manuscripta Math., vol. 158, Art. no. 1–2, 2019, doi: 10.1007/s00229-018-1023-2.
    22. D. Wittwar, G. Santin, and B. Haasdonk, “Part II on matrix valued kernels including analysis,” 2019.
    23. 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.
    24. 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, Art. no. 1, 2019, doi: https://doi.org/10.1142/S2591728518500445.
    25. R. Mazzeo, J. Swoboda, H. Weiss, and F. Witt, “Asymptotic geometry of the Hitchin metric,” Commun. Math. Phys., vol. 367, Art. no. 1, 2019, doi: 10.1007/s00220-019-03358-y.
    26. T. Wenzel, G. Santin, and B. Haasdonk, “A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution.” 2019.
    27. 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.
    28. 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.
    29. F. G. Zhang R, Dippon J, “Refined risk stratification for thoracoscopic lobectomy or segmentectomy,” Dis., J Thorac, vol. 2019 Jan;11(1), pp. :222–230, 2019, doi: 10.21037/jtd.2018.12.44.
    30. I. Steinwart, “A Sober Look at Neural Network Initializations,” Fakultät für Mathematik und Physik, Universität Stuttgart, 2019.
    31. M. Chirilus-Bruckner, D. Maier, and G. Schneider, “Diffusive stability for periodic metric graphs,” Math. Nachr., vol. 292, Art. no. 6, 2019.
    32. Y. Homma and U. Semmelmann, “The Kernel of the Rarita-Schwinger Operator on Riemannian Spin Manifolds,” Comm. Math. Phys., vol. 370, Art. no. 3, 2019, doi: 10.1007/s00220-019-03324-8.
    33. K. Carlberg, L. Brencher, B. Haasdonk, and A. Barth, “Data-driven time parallelism via forecasting,” SIAM Journal on Scientific Computing, vol. 41, Art. no. 3, 2019.
    34. 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
    35. M. Geck, “Eigenvalues and Polynomial Equations,” The American Mathematical Monthly, vol. 126, Art. no. 10, Nov. 2019, doi: 10.1080/00029890.2019.1651168.
    36. 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 Anal. Hybri., vol. 34, pp. 179–208, 2019, doi: https://doi.org/10.1016/j.nahs.2019.06.003.
    37. C. A. Rösinger and C. W. Scherer, “A Scalings Approach to $H_2$-Gain-Scheduling Synthesis without Elimination,” in IFAC-PapersOnLine, 2019, pp. 50–57. doi: 10.1016/j.ifacol.2019.12.347.
    38. 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, Art. no. 44, 2019, [Online]. Available: https://doi.org/10.1021/acs.jpca.9b08239
    39. 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.
    40. 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., Cham: Springer International Publishing, 2019, pp. 113–121.
    41. 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, Art. no. 5, Oct. 2019, doi: 10.1007/s10463-018-0687-4.
    42. R. Zhang, J. Dippon, and G. Friedel, “Refined risk stratification for thoracoscopic lobectomy or segmentectomy,” Journal of Thoracic Disease, vol. 11, Art. no. 1, Jan. 2019, doi: 10.21037/jtd.2018.12.44.
    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, Art. no. 1, 2019, doi: https://doi.org/10.1016/j.jmaa.2019.04.049.
    44. 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, Art. no. 131, Nov. 2019, doi: https://doi.org/10.1016/j.matpur.2019.04.002.
    45. 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, 2019.
    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, Art. no. 2, 2019, doi: 10.1093/ejcts/ezz027.
    48. 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., Cham: Springer International Publishing, 2019, pp. 889–896.
    49. M. Griesemer and U. Linden, “Spectral theory of the Fermi polaron,” Ann. Henri Poincaré, vol. 20, Art. no. 6, 2019, doi: 10.1007/s00023-019-00796-1.
    50. 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.
    51. 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.
    52. B. Ammann, K. Kröncke, H. Weiss, and F. Witt, “Holonomy rigidity for Ricci-flat metrics,” Math. Z., vol. 291, Art. no. 1–2, 2019, doi: 10.1007/s00209-018-2084-3.
    53. A. Armiti-Juber and C. Rohde, “On Darcy-and Brinkman-type models for two-phase flow in asymptotically flat domains,” Comput. Geosci., vol. 23, Art. no. 2, 2019, doi: https://doi.org/10.1007/s10596-018-9756-2.
    54. R. Conlon, A. Degeratu, and F. Rochon, “Quasi-asymptotically conical Calabi-Yau manifolds,” Geom. Topol., vol. 23, Art. no. 1, 2019, doi: 10.2140/gt.2019.23.29.
    55. G. Santin, D. Wittwar, and B. Haasdonk, “Sparse approximation of regularized kernel interpolation by greedy algorithms,” 2019.
    56. S. Engelke, R. de Fondeville, and M. Oesting, “Extremal behaviour of aggregated data with an application to downscaling,” Biometrika, vol. 106, Art. no. 1, 2019, doi: 10.1093/biomet/asy052.
    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., Springer, 2019. doi: DOI:10.1007/978-3-030-21013-7_7.
    58. L. Gyorfi, N. Henze, and H. Walk, “The Limit Distribution Of The Maximum Probability Nearest-Neighbour Ball,” Journal of Applied Probability, vol. 56, Art. no. 2, 2019, doi: 10.1017/jpr.2019.37.
    59. 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.
    60. 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., Università degli studi di Bergamo, 2019, pp. 18–21. doi: 10.6092/DIPSI2019_pp18-21.
  8. 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 Contemporary Math., 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, Art. no. 6, 2017, doi: 10.1002/num.22180.
  9. 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, Art. no. 4, 2012, doi: 10.1002/num.20668.
  10. 2011

    1. A. Lalegname and A. Sändig, “Wave-crack interaction in finite elastic bodies,” International Journal of Fracture, vol. 172, 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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