Publikationen des Fachbereichs Mathematik

Fachbereich Mathematik

Publikationen der Mitglieder des Fachbereichs Mathematik ab 2017

 

Einen ersten Eindruck über die vielfältigen Publikationen der Forschenden des Fachbereichs, nicht nur in begutachteten Fachzeitschriften, gibt die folgende Übersicht exemplarisch für den Zeitraum ab 2017. Einen detaillerteren, evtl. vollständigeren und themenspezifischeren Eindruck vermitteln die Seiten der einzelnen Institute, Arbeitsgruppen und koordinierten Forschungsprogramme

  1. 2025

    1. Barth, A., & Stein, A. (2025). A stochastic transport problem with Lévy noise: Fully discrete numerical approximation. Mathematics and Computers in Simulation, 227, 347–370. https://doi.org/10.1016/j.matcom.2024.07.036
  2. 2024

    1. "Knobloch, P., "Kuzmin, D., & "Jha, A. (2024). Well-balanced convex limiting for finite element discretizations of steady convection-diffusion-reaction equations (P. "Knobloch, D. "Kuzmin, & A. "Jha, Hrsg.).
    2. Albişoru, A. F., Kohr, M., Papuc, I., & Wendland, W. L. (2024). On some Robin–transmission problems for the Brinkman system and a Navier–Stokes type system. Math. Meth. Appl. Sci., 1–28. https://doi.org/10.1002/mma.10170
    3. Alkämper, M., Magiera, J., & Rohde, C. (2024). An Interface-Preserving Moving Mesh in Multiple Space  Dimensions. ACM Trans. Math. Softw., 50(1), Article 1. https://doi.org/10.1145/3630000
    4. Beschle, C., & Barth, A. (2024). Complexity analysis of quasi continuous level Monte Carlo. ESAIM: Mathematical Modelling and Numerical Analysis. https://doi.org/10.1051/m2an/2024039
    5. Beschle, C. A., & Barth, A. (2024). 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 (Bd. 460, S. 3–31). Springer Proceedings in Mathematics & Statistics. https://doi.org/10.1007/978-3-031-59762-6_1
    6. Bondanza, M., Nottoli, T., Nottoli, M., Cupellini, L., Lipparini, F., & Mennucci, B. (2024). The OpenMMPol library for polarizable QM/MM calculations of properties and dynamics. The Journal of Chemical Physics, 160(13), Article 13. https://doi.org/10.1063/5.0198251
    7. Braun, A., Kohler, M., Langer, S., & Walk, H. (2024). Convergence rates for shallow neural networks learned by gradient descent. Bernoulli, 30(1), Article 1. https://doi.org/10.3150/23-bej1605
    8. Buchfink, P., Glas, S., Haasdonk, B., & Unger, B. (2024). Model reduction on manifolds: A differential geometric framework (2024 Physica D, Hrsg.). https://arxiv.org/abs/2312.01963
    9. Claeys, X., Hassan, M., & Stamm, B. (2024). Continuity estimates for Riesz potentials on polygonal boundaries. Partial Differential Equations and Applications. https://doi.org/10.1007/s42985-024-00280-4
    10. Corso, T. C., Hassan, M., Jha, A., & Stamm, B. (2024). An $L^2$-maximum principle for circular arcs on the disk.
    11. Döppel, F., Wenzel, T., Herkert, R., Haasdonk, B., & Votsmeier, M. (2024). Goal‐Oriented Two‐Layered Kernel Models as Automated Surrogates for Surface Kinetics in Reactor Simulations. Chemie Ingenieur Technik, 96(6), Article 6. https://doi.org/10.1002/cite.202300178
    12. Ghosh, T., Bringedal, C., Rohde, C., & Helmig, R. (2024). A phase-field approach to model evaporation from porous media: Modeling and upscaling. https://arxiv.org/abs/2112.13104
    13. Giannoulis, I., Schmidt, B., & Schneider, G. (2024). NLS approximation for a scalar FPUT system on a 2D square lattice with a cubic nonlinearity. J. Math. Anal. Appl., 540(2), Article 2. https://doi.org/10.1016/j.jmaa.2024.128625
    14. Hammer, M., Wenzel, T., Santin, G., Meszaros-Beller, L., Little, J. P., Haasdonk, B., & Schmitt, S. (2024). A new method to design energy-conserving surrogate models for the coupled, nonlinear responses of intervertebral discs. Biomechanics and Modeling in Mechanobiology, 23(3), Article 3. https://doi.org/10.1007/s10237-023-01804-4
    15. Herkert, R., Buchfink, P., Wenzel, T., Haasdonk, B., Toktaliev, P., & Iliev, O. (2024). Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data. Mathematics, 12(13), Article 13. https://doi.org/10.3390/math12132111
    16. Herkert, R. R. (2024). Replication Code for: Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data. https://doi.org/10.18419/darus-4227
    17. Homs-Pons, C., Lautenschlager, R., Schmid, L., Ernst, J., Göddeke, D., Röhrle, O., & Schulte, M. (2024). Coupled Simulation and Parameter Inversion for Neural System  and Electrophysiological Muscle Models. GAMM-Mitteilungen. https://doi.org/10.1002/gamm.202370009
    18. Hsiao, G. C., Sánchez-Vizuet, T., & Wendland, W. L. (2024). Boundary-field formulation for transient electromagnetic scattering by dielectric scatterers and coated conductors. In SIAM J. Math. Analysis, to appear. https://doi.org/10.48550/arXiv.2406.05367
    19. Huber, F., Bürkner, P.-C., Göddeke, D., & Schulte, M. (2024). Knowledge-based modeling of simulation behavior for Bayesian optimization. Computational Mechanics, 74(1), Article 1. https://doi.org/10.1007/s00466-023-02427-3
    20. Huber, F., Bürkner, P.-C., Göddeke, D., & Schulte, M. (2024). Knowledge-based modeling of simulation behavior for Bayesian  optimization. Computational Mechanics. https://doi.org/10.1007/s00466-023-02427-3
    21. Hörl, M., & Rohde, C. (2024). Rigorous Derivation of Discrete Fracture Models for Darcy Flow in the Limit of Vanishing Aperture. Netw. Heterog. Media, 19(1), Article 1. https://doi.org/10.3934/nhm.2024006
    22. Jha, A. (2024). Residual-Based a Posteriori Error Estimators for Algebraic Stabilizations. Applied Mathematics Letters, 157, 109192. https://doi.org/10.1016/j.aml.2024.109192
    23. Karabash, I. M., Lienstromberg, C., & Velázquez, J. J. L. (2024). Multi-parameter Hopf bifurcations of rimming flows. https://doi.org/10.48550/arXiv.2406.11690
    24. Kharitenko, A., & Scherer, C. W. (2024). On the exactness of a stability test for Lur’e systems with slope-restricted nonlinearities. IEEE Transactions on Automatic Control. https://doi.org/10.1109/TAC.2024.3362859
    25. Kohr, M., Nistor, V., & Wendland, W. L. (2024). The Stokes operator on manifolds with cylindrical ends. Journal of Differential Equations, 407, Article 407. https://doi.org/10.1016/j.jde.2024.06.017
    26. Lindgren, E. B., Avis, H., Miller, A., Stamm, B., Besley, E., & Stace, A. J. (2024). The significance of multipole interactions for the stability of regular structures composed from charged particles. Journal of Colloid and Interface Science, 663, 458–466. https://doi.org/10.1016/j.jcis.2024.02.146
    27. Lukácová-Medvid’ová, M., & Rohde, C. (2024). Mathematical Challenges for the Theory of Hyperbolic Balance Laws in Fluid Mechanics: Complexity, Scales, Randomness. In Accepted for publication in Jahresber. Dtsch. Math.-Ver.
    28. Lukácová-Medvid’ová, M., & Rohde, C. (2024). Mathematical Challenges for the Theory of Hyperbolic Balance Laws in Fluid Mechanics: Complexity, Scales, Randomness.
    29. Magiera, J., & Rohde, C. (2024). A Multiscale Method for Two-Component, Two-Phase Flow with a Neural Network Surrogate. Communications on Applied Mathematics and Computation. https://doi.org/10.1007/s42967-023-00349-8
    30. Maier, B., Göddeke, D., Huber, F., Klotz, T., Röhrle, O., & Schulte, M. (2024). OpenDiHu: An Efficient and Scalable Framework for Biophysical  Simulations of the Neuromuscular System. Journal of Computational Science, 79(102291), Article 102291. https://doi.org/10.1016/j.jocs.2024.102291
    31. Maier, B., Göddeke, D., Huber, F., Klotz, T., Röhrle, O., & Schulte, M. (2024). OpenDiHu: An Efficient and Scalable Framework for Biophysical Simulations of the Neuromuscular System. Journal of Computational Science, 79. https://doi.org/10.1016/j.jocs.2024.102291
    32. Meijer, T. J., Holicki, T., Eijnden, S. J. A. M. van den, Scherer, C. W., & Heemels, W. P. M. H. (2024). The Non-Strict Projection Lemma. IEEE Transactions on Automatic Control, 1–8. https://doi.org/10.1109/TAC.2024.3371374
    33. Mel’nyk, T. A., & Durante, T. (2024). Spectral problems with perturbed Steklov conditions in thick junctions with branched structure. Applicable Analysis, 1–26. https://doi.org/10.1080/00036811.2024.2322644
    34. Mel’nyk, T., & Rohde, C. (2024). Asymptotic expansion for convection-dominated transport in a thin graph-like junction. Analysis and Applications, 22 (05), 833–879. https://doi.org/10.1142/S0219530524500040
    35. Mel’nyk, T., & Rohde, C. (2024). Asymptotic approximations for semilinear parabolic convection-dominated transport problems in thin graph-like networks. J. Math. Anal. Appl., 529(1), Article 1. https://doi.org/10.1016/j.jmaa.2023.127587
    36. Mel’nyk, T., & Rohde, C. (2024). Reduced-dimensional modelling for nonlinear convection-dominated flow in cylindric domains. Nonlinear Differ. Equ. Appl., 31:105. https://doi.org/10.1007/s00030-024-00997-6
    37. Mel’nyk, T., & Rohde, C. (2024). Puiseux asymptotic expansions for convection-dominated transport problems in thin graph-like networks: strong boundary interactions. Asymptotic Analysis, 137, 27–52. https://doi.org/10.3233/ASY-231876
    38. Miao, Y., Rohde, C., & Tang, H. (2024). Well-posedness for a stochastic Camassa-Holm type equation with higher order nonlinearities. Stoch. Partial Differ. Equ. Anal. Comput., 12(1), Article 1. https://doi.org/10.1007/s40072-023-00291-z
    39. Nottoli, M., Herbst, M. F., Mikhalev, A., Jha, A., Lipparini, F., & Stamm, B. (2024). ddX: Polarizable continuum solvation from small molecules to proteins. WIREs Computational Molecular Science. https://doi.org/10.1002/wcms.1726
    40. Nottoli, M., Vanich, E., Cupellini, L., Scalmani, G., Pelosi, C., & Lipparini, F. (2024). Importance of Polarizable Embedding for Computing Optical Rotation: The Case of Camphor in Ethanol. The Journal of Physical Chemistry Letters, 7992–7999. https://doi.org/10.1021/acs.jpclett.4c01550
    41. Ruan, L., & Rybak, I. (2024). Stokes-Brinkman-Darcy models for coupled fluid-porous systems: derivation, analysis and validation. Appl. Math. Comp.  (submitted).
    42. Schollenberger, T., von Wolff, L., Bringedal, C., Pop, I. S., Rohde, C., & Helmig, R. (2024). Investigation of Different Throat Concepts for Precipitation Processes in Saturated Pore-Network Models. Transport in Porous Media. https://doi.org/10.1007/s11242-024-02125-5
    43. Strohbeck, P., Discacciati, M., & Rybak, I. (2024). Optimized Schwarz method for the Stokes-Darcy problem with generalized interface conditions. J. Comput. Phys. (submitted).
    44. Strohbeck, P., & Rybak, I. (2024). Efficient preconditioners for coupled Stokes-Darcy problems with MAC scheme: Spectral analysis and numerical study. J. Sci. Comput. (submitted).
    45. Wendland, W. L. (2024). On the construction of the Stokes flow in a domain with cylindrical ends. Math. Meth. Appl. Sci., 1–6. https://doi.org/10.1002/mma.10106
    46. Wenzel, T., Haasdonk, B., Kleikamp, H., Ohlberger, M., & Schindler, F. (2024). Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling. In I. Lirkov & S. Margenov (Hrsg.), Large-Scale Scientific Computations (S. 117--125). Springer Nature Switzerland.
  3. 2023

    1. Afşer, H., Györfi, L., & Walk, H. (2023). Classification With Repeated Observations. IEEE Signal Processing Letters, 30, 1522–1526. https://doi.org/10.1109/LSP.2023.3326057
    2. Arridge, S. R., Burger, M., Hahn, B., & Quinto, E. T. (2023). Tomographic Inverse Problems: Mathematical Challenges and Novel Applications. Oberwolfach Reports, 20(2), Article 2. https://doi.org/10.4171/owr/2023/21
    3. Bamer, F., Ebrahem, F., Markert, B., & Stamm, B. (2023). Molecular Mechanics of Disordered Solids. Archives of Computational Methods in Engineering, 30(3), Article 3. https://doi.org/10.1007/s11831-022-09861-1
    4. Berberich, J., Scherer, C. W., & Allgower, F. (2023). Combining Prior Knowledge and Data for Robust Controller Design. IEEE Transactions on Automatic Control, 68(8), Article 8. https://doi.org/10.1109/tac.2022.3209342
    5. Beschle, C. A., & Barth, A. (2023). Quasi continuous level Monte Carlo for random elliptic PDEs.
    6. Brehmer, P., Herbst, M. F., Wessel, S., Rizzi, M., & Stamm, B. (2023). Reduced basis surrogates for quantum spin systems based on tensor networks. Physical Review E. https://doi.org/10.1103/PhysRevE.108.025306
    7. Brennenstuhl, M., Otto, R., Schembera, B., & Eicker, U. (2023). Optimized Dimensioning and Economic Assessment of Decentralized Hybrid Small Wind and PV Power Systems for Residential Buildings. https://www.researchsquare.com/article/rs-3677621/latest.pdf
    8. Buchfink, P., Glas, S., & Haasdonk, B. (2023). Approximation Bounds for Model Reduction on Polynomially Mapped Manifolds. https://arxiv.org/abs/2312.00724
    9. Burbulla, S., Formaggia, L., Rohde, C., & Scotti, A. (2023). Modeling fracture propagation in poro-elastic media combining phase-field and discrete fracture models. Comput. Methods Appl. Mech. Engrg., 403. https://doi.org/10.1016/j.cma.2022.115699
    10. Burbulla, S., Hörl, M., & Rohde, C. (2023). Flow in Porous Media with Fractures of Varying Aperture. SIAM J. Sci. Comput, 45(4), Article 4. https://doi.org/10.1137/22M1510406
    11. Cancès, E., Herbst, M. F., Kemlin, G., Levitt, A., & Stamm, B. (2023). Numerical stability and efficiency of response property calculations in density functional theory. Letters in Mathematical Physics. https://doi.org/10.1007/s11005-023-01645-3
    12. Cancès, E., Herbst, M. F., Kemlin, G., Levitt, A., & Stamm, B. (2023). Numerical stability and efficiency of response property calculations in density functional theory. Letters in Mathematical Physics, 113(1), Article 1. https://doi.org/10.1007/s11005-023-01645-3
    13. Cerejeiras, P., Ferreira, M., Kähler, U., & Wirth, J. (2023). Global Operator Calculus on Spin Groups. Journal of Fourier Analysis and Applications, 29(3), Article 3. https://doi.org/10.1007/s00041-023-10015-5
    14. Dippon, J., Gwinner, J., Khan, A. A., & Sama, M. (2023). A new regularized stochastic approximation framework for stochastic inverse problems. Nonlinear Anal. Real World Appl., 73, Paper No. 103869, 29. https://doi.org/10.1016/j.nonrwa.2023.103869
    15. Dusson, G., Sigal, I. M., & Stamm, B. (2023). Analysis of the Feshbach-Schur method for the Fourier spectral discretizations of Schrödinger operators. Mathematics of Computation, 92(340), Article 340. https://doi.org/10.1090/mcom/3774
    16. Eggenweiler, E., Nickl, J., & Rybak, I. (2023). Justification of generalized interface conditions for Stokes-Darcy problems. In E. Franck, J. Fuhrmann, V. Michel-Dansac, & L. Navoret (Hrsg.), Finite Volumes for Complex Applications X - Volume 1, Elliptic and Parabolic Problems (S. 275–283). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-40864-9_22
    17. Eggenweiler, E., & Rybak, I. (2023). Higher-order coupling conditions for arbitrary flows in Stokes-Darcy systems. J. Fluid Mech. (submitted).
    18. Fukuizumi, R., Gao, Y., Schneider, G., & Takahashi, M. (2023). Pattern formation in 2D stochastic anisotropic Swift-Hohenberg equation. Interdiscip. Inform. Sci., 29(1), Article 1. https://doi.org/10.4036/iis.2023.a.03
    19. Gander, M. J., Lunowa, S. B., & Rohde, C. (2023). Consistent and Asymptotic-Preserving Finite-Volume Robin Transmission Conditions for Singularly Perturbed Elliptic Equations. In S. C. Brenner, E. Chung, A. Klawonn, F. Kwok, J. Xu, & J. Zou (Hrsg.), Domain Decomposition Methods in Science and Engineering XXVI (S. 443--450). Springer International Publishing.
    20. Gander, M. J., Lunowa, S. B., & Rohde, C. (2023). Non-Overlapping Schwarz Waveform-Relaxation for Nonlinear Advection-Diffusion Equations. SIAM J. Sci. Comput., 45(1), Article 1. https://doi.org/10.1137/21M1415005
    21. Gladbach, P., Jansen, J., & Lienstromberg, C. (2023). Non-Newtonian thin-film equations: global existence of solutions, gradient-flow structure and guaranteed lift-off. https://doi.org/10.48550/ARXIV.2301.10300
    22. Gramlich, D., Holicki, T., Scherer, C. W., & Ebenbauer, C. (2023). A Structure Exploiting SDP Solver for Robust Controller Synthesis. IEEE Control Syst. Lett., 7, 1831–1836. https://doi.org/10.1109/LCSYS.2023.3277314
    23. Gramlich, D., Pauli, P., Scherer, C. W., Allgöwer, F., & Ebenbauer, C. (2023). Convolutional Neural Networks as 2-D systems. https://doi.org/10.48550/ARXIV.2303.03042
    24. Gramlich, D., Scherer, C. W., Häring, H., & Ebenbauer, C. (2023). Synthesis of constrained robust feedback policies and model predictive control. https://doi.org/10.48550/ARXIV.2310.11404
    25. Griesemer, M., & Hofacker, M. (2023). On the weakness of short-range interactions in Fermi gases. Lett. Math. Phys., 113(1), Article 1. https://doi.org/10.1007/s11005-022-01624-0
    26. Györfi, L., Linder, T., & Walk, H. (2023). Lossless Transformations and Excess Risk Bounds in Statistical Inference. Entropy, 25(10), Article 10. https://doi.org/10.3390/e25101394
    27. Haas, T., de Rijk, B., & Schneider, G. (2023). 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., 72(1), Article 1. https://doi.org/10.1512/iumj.2023.72.9297
    28. Haasdonk, B., Kleikamp, H., Ohlberger, M., Schindler, F., & Wenzel, T. (2023). A New Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs. SIAM Journal on Scientific Computing, 45(3), Article 3. https://doi.org/10.1137/22m1493318
    29. Hahn, B., & Wirth, B. (2023). Convex reconstruction of moving particles with inexact motion model. PAMM, 23(2), Article 2. https://doi.org/10.1002/pamm.202300054
    30. Hahn, B. N., Quinto, E. T., & Rigaud, G. (2023). Foreword to special issue of Inverse Problems on modern challenges in imaging. Inverse Problems, 39(3), Article 3. https://doi.org/10.1088/1361-6420/acb569
    31. Hahn, B. N., Rigaud, G., & Schmähl, R. (2023). A class of regularizations based on nonlinear isotropic diffusion for inverse problems. IMA Journal of Numerical Analysis. https://doi.org/10.1093/imanum/drad002
    32. Hewing, L., Gramlich, D., Verhoek, C., Polonio, R., Veenman, J., Ardura, C., Tóth, R., Ebenbauer, C., Scherer, C., & Preda, V. (2023, Juli). Enhancing the Guidance, Navigation and Control of Autonomous Parafoils using Machine Learning Methods. Papers of ESA GNC-ICATT 2023. https://doi.org/10.5270/esa-gnc-icatt-2023-135
    33. Heß, M., & Schneider, G. (2023). A robust way to justify the derivative NLS approximation. Z. Angew. Math. Phys., 74(6), Article 6. https://doi.org/10.1007/s00033-023-02121-7
    34. Hilder, B., de Rijk, B., & Schneider, G. (2023). 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., 22(2), Article 2. https://doi.org/10.1137/22M1502902
    35. Hilder, B., de Rijk, B., & Schneider, G. (2023). Nonlinear stability of periodic roll solutions in the real Ginzburg-Landau equation against $C_ub^m$-perturbations. Comm. Math. Phys., 400(1), Article 1. https://doi.org/10.1007/s00220-022-04619-z
    36. Holicki, T., & Scherer, C. W. (2023). IQC based analysis and estimator design for discrete-time systems affected by impulsive uncertainties. Nonlinear Anal. Hybri., 50, 101399. https://doi.org/10.1016/j.nahs.2023.101399
    37. Holicki, T., & Scherer, C. W. (2023). Input-Output-Data-Enhanced Robust Analysis via Lifting. IFAC-PapersOnLine, 56(2), Article 2. https://doi.org/10.1016/j.ifacol.2023.10.047
    38. Holzmüller, D., Zaverkin, V., Kästner, J., & Steinwart, I. (2023). A Framework and Benchmark for Deep Batch Active Learning for Regression. Journal of Machine Learning Research, 24(164), Article 164. http://jmlr.org/papers/v24/22-0937.html
    39. Hornischer, N. (2023). Model Order Reduction with Dynamically Transformed Modes for Electrophysiological Simulations. GAMM Archive for Students.
    40. Horsch, M., Schembera, B., & DFG, M. (2023). Epistemic metadata in molecular modelling: First-stage case-study report (10 cases). In Inprodat eV, Kaiserslautern, Tech. Rep (Inprodat eV, Kaiserslautern, Tech. Rep). https://www.researchgate.net/profile/Martin-Horsch/publication/366974408_Epistemic_metadata_in_molecular_modelling_First-stage_case-study_report_10_cases/links/63bc41e4a03100368a6645a6/Epistemic-metadata-in-molecular-modelling-First-stage-case-study-report-10-cases.pdf
    41. Jansen, J., Lienstromberg, C., & Nik, K. (2023). Long-Time Behavior and Stability for Quasilinear Doubly Degenerate Parabolic Equations of Higher Order. SIAM Journal on Mathematical Analysis, 55(2), Article 2. https://doi.org/10.1137/22M1491137
    42. Jha, A., John, V., & Knobloch, P. (2023). Adaptive Grids in the Context of Algebraic Stabilizations for Convection-Diffusion-Reaction Equations. SIAM Journal on Scientific Computing, 45(4), Article 4. https://doi.org/10.1137/21m1466360
    43. Jha, A., Nottoli, M., Mikhalev, A., Quan, C., & Stamm, B. (2023). Linear Scaling Computation of Forces for the Domain-Decomposition Linear Poisson--Boltzmann Method. The Journal of Chemical Physics, 158, 104105. https://doi.org/10.1063/5.0141025
    44. Keckstein, S., Dippon, J., Hudelist, G., Koninckx, P., Condous, G., Schroeder, L., & Keckstein, J. (2023). Sonomorphologic Changes in Colorectal Deep Endometriosis: The Long-Term Impact of Age and Hormonal Treatment. Ultraschall in der Medizin - European Journal of Ultrasound, EFirst, Article EFirst. https://doi.org/10.1055/a-2209-5653
    45. Keim, J., Schwarz, A., Chiocchetti, S., Rohde, C., & Beck, A. (2023). A Reinforcement Learning Based Slope Limiter for Two-Dimensional Finite Volume Schemes. https://doi.org/10.13140/RG.2.2.18046.87363
    46. Keim, J., Munz, C.-D., & Rohde, C. (2023). A Relaxation Model for the Non-Isothermal Navier-Stokes-Korteweg Equations in Confined Domains. J. Comput. Phys., 474, 111830. https://doi.org/10.1016/j.jcp.2022.111830
    47. Kharitenko, A., & Scherer, C. (2023). Time-varying Zames–Falb multipliers for LTI Systems are superfluous. Automatica, 147, 110577. https://doi.org/10.1016/j.automatica.2022.110577
    48. Kohr, M., Nistor, V., & Wendland, W. L. (2023). Layer potentials and essentially translation invariant pseudodifferential operators on manifolds with cylindrical ends. In Postpandemic Operator Theory (S. 61–115). Springer-Verlag Berlin. https://doi.org/10.48550/arXiv.2308.06308
    49. Kröker, I., Oladyshkin, S., & Rybak, I. (2023). Global sensitivity analysis using multi-resolution polynomial chaos expansion for coupled Stokes-Darcy flow problems. Comput. Geosci. https://doi.org/10.1007/s10596-023-10236-z
    50. Lienstromberg, C., Schiffer, S., & Schubert, R. (2023). A data-driven approach to viscous fluid mechanics: the              stationary case. Arch. Ration. Mech. Anal., 247(2), Article 2. https://doi.org/10.1007/s00205-023-01849-w
    51. Lienstromberg, C., Schiffer, S., & Schubert, R. (2023). A variational approach to the non-newtonian Navier-Stokes equations. https://doi.org/doi:10.48550/ARXIV.2312.03546
    52. Lienstromberg, C., & Velázquez, J. J. L. (2023). Long-time asymptotics and regularity estimates for weak solutions to a doubly degenerate thin-film equation in the Taylor-Couette setting. arXiv. https://doi.org/10.48550/ARXIV.2203.00075
    53. Maier, D., Reichel, W., & Schneider, G. (2023). Breather solutions for a semilinear Klein-Gordon equation on a periodic metric graph. J. Math. Anal. Appl., 528(2), Article 2. https://doi.org/10.1016/j.jmaa.2023.127520
    54. Meijer, T. J., Holicki, T., Eijnden, S. J. A. M. van den, Scherer, C. W., & Heemels, W. P. M. H. (2023). The Non-Strict Projection Lemma. arXiv. https://doi.org/10.48550/ARXIV.2305.08735
    55. Mel’nyk, T. (2023). Complex Analysis (No. 1; Nummer 1). Springer Cham. https://doi.org/10.1007/978-3-031-39615-1
    56. Mel’nyk, T. A. (2023). Asymptotic analysis of spectral problems in thick junctions with the branched fractal structure. Mathematical Methods in the Applied Sciences, 46(3), Article 3. https://doi.org/10.1002/mma.8692
    57. Merkle, R., & Barth, A. (2023). On Properties and Applications of Gaussian Subordinated Lévy Fields. Methodology and Computing in Applied Probability, 25, 62. https://doi.org/10.1007/s11009-023-10033-2
    58. Miller, C. T., Gray, W. G., Kees, C. E., Rybak, I., & Shepherd, B. J. (2023). Correction to: Modelling Sediment Transport in Three-Phase Surface Water Systems. J. Hydraul. Res., 61, 168–171. https://doi.org/10.1080/00221686.2022.2107580
    59. Mohammadi, F., Eggenweiler, E., Flemisch, B., Oladyshkin, S., Rybak, I., Schneider, M., & Weishaupt, K. (2023). A Surrogate-Assisted Uncertainty-Aware Bayesian Validation Framework and its Application to Coupling Free Flow and Porous-Medium Flow. Comput. Geosci. https://doi.org/10.1007/s10596-023-10228-z
    60. Morato, M. M., Holicki, T., & Scherer, C. W. (2023). Stabilizing Model Predictive Control Synthesis using Integral Quadratic Constraints and Full-Block Multipliers. Int. J. Robust Nonlin. https://doi.org/10.1002/rnc.6952
    61. Nagy, P.-A., & Semmelmann, U. (2023). Eigenvalue estimates for 3-Sasaki structures.
    62. Nottoli, M., Bondanza, M., Mazzeo, P., Cupellini, L., Curutchet, C., Loco, D., Lagardère, L., Piquemal, J., Mennucci, B., & Lipparini, F. (2023). QM/AMOEBA description of properties and dynamics of embedded molecules. WIREs Computational Molecular Science, 13(6), Article 6. https://doi.org/10.1002/wcms.1674
    63. Pelinovsky, D., & Schneider, G. (2023). KP-II approximation for a scalar Fermi-Pasta-Ul system on a 2D square lattice. SIAM J. Appl. Math., 83(1), Article 1. https://doi.org/10.1137/22M1509369
    64. Pes, F., Polack, É., Mazzeo, P., Dusson, G., Stamm, B., & Lipparini, F. (2023). 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, 9720--9726. https://doi.org/10.1021/acs.jpclett.3c02098
    65. Ruan, L., & Rybak, I. (2023). Stokes-Brinkman-Darcy models for coupled free-flow and porous-medium systems. In E. Franck, J. Fuhrmann, V. Michel-Dansac, & L. Navoret (Hrsg.), Finite Volumes for Complex Applications X - Volume 1, Elliptic and Parabolic Problems (S. 365–373). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-40864-9_31
    66. Rösinger, C. A., & Scherer, C. W. (2023). Gain-Scheduling Controller Synthesis for Networked Systems with Full Block Scalings. https://doi.org/10.1109/TAC.2023.3329851
    67. Rösinger, C. A., & Scherer, C. W. (2023). Gain-Scheduling Controller Synthesis for Nested Systems With Full Block Scalings. IEEE Transactions on Automatic Control, 1–16. https://doi.org/10.1109/TAC.2023.3329851
    68. Santin, G., Wenzel, T., & Haasdonk, B. (2023). On the optimality of target-data-dependent kernel greedy interpolation in Sobolev Reproducing Kernel Hilbert Spaces. https://arxiv.org/abs/2307.09811
    69. Schembera, B., Wübbeling, F., Koprucki, T., Biedinger, C., Reidelbach, M., Schmidt, B., Göddeke, D., & Fiedler, J. (2023). Building Ontologies and Knowledge Graphs for Mathematics and its Applications. Proceedings of the Conference on Research Data Infrastructure, 1. https://doi.org/10.52825/cordi.v1i.255
    70. Scherer, C. W. (2023). Robust Exponential Stability and Invariance Guarantees with General Dynamic O’Shea-Zames-Falb Multipliers. https://doi.org/10.48550/ARXIV.2306.00571
    71. Scherer, C. W., Ebenbauer, C., & Holicki, T. (2023). Optimization Algorithm Synthesis based on Integral Quadratic Constraints: A Tutorial. https://doi.org/10.48550/ARXIV.2306.00565
    72. Schwahn, P., Semmelmann, U., & Weingart, G. (2023). Stability of the Non-Symmetric Space $E_7/PSO(8)$.
    73. Seus, D., Radu, F. A., & Rohde, C. (2023). Towards hybrid two-phase modelling using linear domain decomposition. Numer. Methods Partial Differential Equations, 39(1), Article 1. https://doi.org/10.1002/num.22906
    74. Strohbeck, P., Eggenweiler, E., & Rybak, I. (2023). A modification of the Beavers-Joseph condition for arbitrary flows to the fluid-porous interface. Transp. Porous Med., 147(3), Article 3. https://doi.org/10.1007/s11242-023-01919-3
    75. Strohbeck, P., Riethmüller, C., Göddeke, D., & Rybak, I. (2023). Robust and efficient preconditioners for Stokes-Darcy problems. In E. Franck, J. Fuhrmann, V. Michel-Dansac, & L. Navoret (Hrsg.), Finite Volumes for Complex Applications X - Volume 1, Elliptic and Parabolic Problems (S. 375–383). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-40864-9_32
    76. Taha, F. A., Yan, S., & Bitar, E. (2023). A Distributionally Robust Approach to Regret Optimal Control using the Wasserstein Distance. 2023 62nd IEEE Conference on Decision and Control (CDC), 2768–2775. https://doi.org/10.1109/CDC49753.2023.10384311
    77. Theisen, L., & Stamm, B. (2023). A Scalable Two-Level Domain Decomposition Eigensolver for Periodic Schrödinger Eigenstates in Anisotropically Expanding Domains. https://doi.org/10.48550/arXiv.2311.08757
    78. Wendland, W. L. (2023). My relation with GAMM (G. Rundbrief, Hrsg.; No. 1). GAMM Rundbrief. https://www.gamm.org/wp-content/uploads/2024/03/GAMM_1-23_web.pdf
    79. Wenzel, T., Santin, G., & Haasdonk, B. (2023). Analysis of Target Data-Dependent Greedy Kernel Algorithms: Convergence Rates for f -, f · P - and f /P -greedy. Constructive Approximation, 57(1), Article 1. https://doi.org/10.1007/s00365-022-09592-3
    80. Wenzel, T., Santin, G., & Haasdonk, B. (2023). Stability of convergence rates: Kernel interpolation on non-Lipschitz domains (2024 IMA Journal of Numerical Analysis, 44(3):1-22, Hrsg.). https://doi.org/10.1093/imanum/drae014
    81. Zaverkin, V., Holzmüller, D., Bonfirraro, L., & Kästner, J. (2023). Transfer learning for chemically accurate interatomic neural network potentials. Phys. Chem. Chem. Phys., 25(7), Article 7. https://doi.org/10.1039/D2CP05793J
  4. 2022

    1. Agullo, E., Altenbernd, M., Anzt, H., Bautista-Gomez, L., Benacchio, T., Bonaventura, L., Bungartz, H.-J., Chatterjee, S., Ciorba, F. M., DeBardeleben, N., Drzisga, D., Eibl, S., Engelmann, C., Gansterer, W. N., Giraud, L., Göddeke, D., Heisig, M., Jézéquel, F., Kohl, N., … Wohlmuth, B. (2022). Resiliency in numerical algorithm design for extreme scale simulations. The International Journal of High Performance ComputingApplications, 36(2), Article 2. https://doi.org/10.1177/10943420211055188
    2. Assenmacher, O., Bruell, G., & Lienstromberg, C. (2022). Non-Newtonian two-phase thin-film problem: local existence,              uniqueness, and stability. Comm. Partial Differential Equations, 47(1), Article 1. https://doi.org/10.1080/03605302.2021.1957929
    3. Barth, A., & Stein, A. (2022). Numerical analysis for time-dependent advection-diffusion problems with random discontinuous coefficients. ESAIM: M2AN, 56(5), Article 5. https://doi.org/10.1051/m2an/2022054
    4. Benner, P., Burger, M., Göddeke, D., Görgen, C., Himpe, C., Heiland, J., Koprucki, T., Ohlberger, M., Rave, S., Reiselbach, M., Saak, J., Schöbel, A., Tabelow, K., & Weber, M. (2022). Die mathematische Forschungsdateninitiative in der NFDI:  MaRDI (Mathematical Research Data Initiative). GAMM Rundbrief, 2022(1), Article 1.
    5. Berberich, J., Scherer, C. W., & Allgower, F. (2022). Combining Prior Knowledge and Data for Robust Controller Design. IEEE Transactions on Automatic Control, 1--16. https://doi.org/10.1109/tac.2022.3209342
    6. Beschle, C. (2022). Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5. https://doi.org/10.18419/darus-3154
    7. Beschle, C., & Barth, A. (2022). Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5. https://doi.org/10.18419/darus-3154
    8. Beschle, C., & Kovács, B. (2022). Stability and error estimates for non-linear Cahn–Hilliard-type equations on evolving surfaces. Numerische Mathematik, 1--48. https://doi.org/10.1007/s00211-022-01280-5
    9. Boege, T., Fritze, R., Görgen, C., Hanselman, J., Iglezakis, D., Kastner, L., Koprucki, T., Krause, T., Lehrenfeld, C., Polla, S., Reidelbach, M., Riedel, C., Saak, J., Schembera, B., Tabelow, K., & Weber, M. (2022). Research-Data Management Planning in the German Mathematical Community. arXiv. https://doi.org/10.48550/ARXIV.2211.12071
    10. Buchfinck, P., Glas, S., & Haasdonk, B. (2022). Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems.
    11. Burbulla, S., Dedner, A., Hörl, M., & Rohde, C. (2022). Dune-MMesh: The Dune Grid Module for Moving Interfaces. J. Open Source Softw., 7(74), Article 74. https://doi.org/10.21105/joss.03959
    12. Burbulla, S., & Rohde, C. (2022). A finite-volume moving-mesh method for two-phase flow in fracturing porous media. J. Comput. Phys., 111031. https://doi.org/10.1016/j.jcp.2022.111031
    13. Cekić, M., Lefeuvre, T., Moroianu, A., & Semmelmann, U. (2022). Towards Brin’s conjecture on frame flow ergodicity: new progress and perspectives.
    14. Dusson, G., Sigal, I., & Stamm, B. (2022). Analysis of the Feshbach–Schur method for the Fourier spectral discretizations of Schrödinger operators. Mathematics of Computation, 92(339), Article 339. https://doi.org/10.1090/mcom/3774
    15. Echterdiek, F., Kitterer, D., Dippon, J., Ott, M., Paul, G., Latus, J., & Schwenger, V. (2022). Outcome of kidney transplantations from ≥65‐year‐old deceased donors with acute kidney injury. Clinical Transplantation, 36(5), Article 5. https://doi.org/10.1111/ctr.14612
    16. Echterdiek, F., Tilgener, C., Dippon, J., Kitterer, D., Scheder-Bieschin, J., Paul, G., Ott, M., Humke, U., Schwenger, V., & Latus, J. (2022). 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, 407(2), Article 2. https://doi.org/10.1007/s00423-021-02383-7
    17. Eggenweiler, E., Discacciati, M., & Rybak, I. (2022). Analysis of the Stokes-Darcy problem with generalised interface conditions. ESAIM Math. Model. Numer. Anal., 56, 727–742. https://doi.org/10.1051/m2an/2022025
    18. Eggenweiler, E. (2022). Interface conditions for arbitrary flows in Stokes-Darcy systems : derivation, analysis and validation. Universität Stuttgart. https://doi.org/10.18419/OPUS-12573
    19. Fiedler, C., Scherer, C. W., & Trimpe, S. (2022, Dezember). Learning Functions and Uncertainty Sets Using Geometrically Constrained Kernel Regression. 61st IEEE Conf. Decision and Control. https://doi.org/10.1109/cdc51059.2022.9993144
    20. Focks, T., Bamer, F., Markert, B., Wu, Z., & Stamm, B. (2022). Displacement field splitting of defective hexagonal lattices. Physical Review B. https://doi.org/10.1103/PhysRevB.106.014105
    21. Frank, R. L., Laptev, A., & Weidl, T. (2022). An improved one-dimensional Hardy inequality. J. Math. Sci. (N.Y.), 268(3, Problems in mathematical analysis. No. 118), Article 3, Problems in mathematical analysis. No. 118. https://doi.org/10.1007/s10958-022-06199-8
    22. Frank, R., Laptev, A., & Weidl, T. (2022). Schrödinger Operators: Eigenvalues and Lieb–Thirring Inequalities. In Cambridge Studies in Advanced Mathematics (S. 512).
    23. Fukuizumi, R., & Schneider, G. (2022). Interchanging space and time in nonlinear optics modeling and dispersion management models. J. Nonlinear Sci., 32(3), Article 3. https://doi.org/10.1007/s00332-022-09788-8
    24. Gavrilenko, P., Haasdonk, B., Iliev, O., Ohlberger, M., Schindler, F., Toktaliev, P., Wenzel, T., & Youssef, M. (2022). A Full Order, Reduced Order and Machine Learning Model Pipeline for Efficient Prediction of Reactive Flows. In I. Lirkov & S. Margenov (Hrsg.), Large-Scale Scientific Computing (S. 378--386). Springer International Publishing.
    25. Gilg, S., Schneider, G., & Uecker, H. (2022). Nonlinear dynamics of modulated waves on graphene like quantum graphs. Math. Nachr., 295(11), Article 11. https://doi.org/10.1002/mana.202100009
    26. Gramlich, D., Ebenbauer, C., & Scherer, C. W. (2022). Synthesis of Accelerated Gradient Algorithms for Optimization and Saddle Point Problems using Lyapunov functions. Syst. Control Lett., 165. https://arxiv.org/abs/2006.09946
    27. Gramlich, D., Scherer, C. W., & Ebenbauer, C. (2022). Robust Differential Dynamic Programming. 61st IEEE Conf. Decision and Control. https://doi.org/10.1109/cdc51059.2022.9992569
    28. Griesemer, M. (2022). Ground states of atoms and molecules in non-relativistic QED. In The Physics and Mathematics of Elliott Lieb (S. 437--450). EMS Press. https://doi.org/10.4171/90-1/18
    29. Griesemer, M., & Hofacker, M. (2022). From Short-Range to Contact Interactions in Two-dimensional Many-Body Quantum Systems. Annales Henri Poincaré, 23(8), Article 8. https://doi.org/10.1007/s00023-021-01149-7
    30. Haasdonk, B., Kleikamp, H., Ohlberger, M., Schindler, F., & Wenzel, T. (2022). A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs. arXiv. https://doi.org/10.48550/ARXIV.2204.13454
    31. Hahn, B. N., Garrido, M.-L. K., Klingenberg, C., & Warnecke, S. (2022). Using the Navier-Cauchy equation for motion estimation in dynamic imaging. Inverse Problems and Imaging, 0(0), Article 0. https://doi.org/10.3934/ipi.2022018
    32. Hassan, M., Williamson, C., Baptiste, J., Braun, S., Stace, A. J., Besley, E., & Stamm, B. (2022). Manipulating Interactions between Dielectric Particles with Electric Fields : A General Electrostatic Many-Body Framework. Journal of Chemical Theory and Computation, 18(10), Article 10. https://doi.org/10.1021/acs.jctc.2c00008
    33. Hilder, B. (2022). Modulating traveling fronts in a dispersive Swift-Hohenberg equation coupled to an additional conservation law. J. Math. Anal. Appl., 513(2), Article 2. https://doi.org/10.1016/j.jmaa.2022.126224
    34. Hilder, B., & Sharma, U. (2022). Quantitative coarse-graining of Markov chains.
    35. Holicki, T. (2022). A Complete Analysis and Design Framework for Linear Impulsive and Related Hybrid Systems [University of Stuttgart]. https://doi.org/10.18419/opus-12158
    36. Holicki, T., & Scherer, C. W. (2022). A Dynamic S-Procedure for Dynamic Uncertainties. IFAC-PapersOnline, 55(25), Article 25. https://doi.org/10.1016/j.ifacol.2022.09.331
    37. Holicki, T., & Scherer, C. W. (2022). IQC Based Analysis and Estimator Design for Discrete-Time Systems Affected by Impulsive Uncertainties.
    38. Holicki, T., & Scherer, C. W. (2022). Input-Output-Data-Enhanced Robust Analysis via Lifting.
    39. Holzmüller, D., & Steinwart, I. (2022). Training two-layer ReLU networks with gradient descent is inconsistent. Journal of Machine Learning Research, 23(181), Article 181. http://jmlr.org/papers/v23/20-830.html
    40. Hornischer, N. (2022). Model Order Reduction with Transformed Modes for Electrophysiological Simulations [Bathesis].
    41. Horsch, M. T., & Schembera, B. (2022). Documentation of epistemic metadata by a mid-level ontology of cognitive processes. Proc. JOWO 2022.
    42. Hsiao, G. C., Sánchez-Vizuet, T., & Wendland, W. L. (2022). A Boundary-Field Formulation for Elastodynamic Scattering. Journal of Elasticity. https://doi.org/10.1007/s10659-022-09964-7
    43. Hägele, D., Schulz, C., Beschle, C., Booth, H., Butt, M., Barth, A., Deussen, O., & Weiskopf, D. (2022). Uncertainty Visualization: Fundamentals and Recent Developments. it - Information Technology, 64(4–5), Article 4–5. https://doi.org/10.1515/itit-2022-0033
    44. Hägele, D., Schulz, C., Beschle, C., Booth, H., Butt, M., Barth, A., Deussen, O., & Weiskopf, D. (2022). Uncertainty visualization : Fundamentals and recent developments. Information Technology, 64(4–5), Article 4–5. https://doi.org/10.1515/itit-2022-0033
    45. Jung, K., Schembera, B., & Gärtner, M. (2022). Best of Both Worlds? Mapping Process Metadata in Digital Humanities and Computational Engineering. Metadata and Semantic Research, 199--205. https://doi.org/10.1007/978-3-030-98876-0_17
    46. Kharitenko, A., & Scherer, C. W. (2022). On the exactness of a stability test for Lur’e systems with slope-restricted nonlinearities.
    47. Klink, M. (2022). Time Error Estimators and Adaptive Time-stepping Schemes [Bathesis].
    48. Klumpp, M., & Schneider, G. (2022). The Schrödinger approximation for the Helmholtz equation if the refractive index is a step function. Wave Motion, 110, Paper No. 102891, 6. https://doi.org/10.1016/j.wavemoti.2022.102891
    49. Klumpp, M., & Schneider, G. (2022). A note on the validity of the Schrödinger approximation for the Helmholtz equation. J. Appl. Anal., 28(1), Article 1. https://doi.org/10.1515/jaa-2021-2058
    50. Kohr, M., Mikhailov, S. E., & Wendland, W. L. (2022). On some mixed-transmission problems for the anisotropic Stokes and Navier-Stokes systems in Lipschitz domains with transversal interfaces. JMAA, 516(1, 126464), Article 1, 126464. https://doi.org/10.1016/j.jmaa.2022.126464
    51. Kohr, M., Mikhailov, S. E., & Wendland, W. L. (2022). Non-homogeneous Dirichlet-transmission problems for the anisotropic Stokes and Navier-Stokes systems in Lipschitz domains with transversal interfaces. Calc. Var. Partial Differential Equations, 61, Paper No. 198 (2022) 47 pp.
    52. Lienstromberg, C., Pernas-Casta\ no, T., & Velázquez, J. J. L. (2022). Analysis of a two-fluid Taylor-Couette flow with one              non-Newtonian fluid. J. Nonlinear Sci., 32(2), Article 2. https://doi.org/10.1007/s00332-021-09750-0
    53. Lienstromberg, C., Pernas-Casta\no, T., & Velázquez, J. J. L. (2022). Analysis of a two-fluid Taylor-Couette flow with one non-Newtonian fluid. J. Nonlinear Sci., 32(2), Article 2. https://doi.org/10.1007/s00332-021-09750-0
    54. Lienstromberg, C., Schiffer, S., & Schubert, R. (2022). A data-driven approach to viscous fluid mechanics -- the stationary case. https://doi.org/10.48550/ARXIV.2207.00324
    55. Magiera, J., & Rohde, C. (2022). A molecular–continuum multiscale model for inviscid liquid–vapor flow with sharp interfaces. J. Comput. Phys., 111551. https://doi.org/10.1016/j.jcp.2022.111551
    56. Magiera, J., & Rohde, C. (2022). Analysis and Numerics of Sharp and Diffuse Interface Models for Droplet Dynamics (K. Schulte, C. Tropea, & B. Weigand, Hrsg.; S. 67–86). Springer International Publishing. https://doi.org/10.1007/978-3-031-09008-0_4
    57. Maier, B., Göddeke, D., Huber, F., Klotz, T., Röhrle, O., & Schulte, M. (2022). OpenDiHu: An Efficient and Scalable Framework for Biophysical Simulations of the Neuromuscular System.
    58. Massa, F., Ostrowski, L., Bassi, F., & Rohde, C. (2022). An artificial Equation of State based Riemann solver for a discontinuous Galerkin discretization of the incompressible Navier–Stokes equations. J. Comput. Phys., 110705. https://doi.org/10.1016/j.jcp.2021.110705
    59. Mehl, L., Beschle, C., Barth, A., & Bruhn, A. (2022). Replication Data for: An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation. https://doi.org/10.18419/darus-2890
    60. Mel’nyk, T., & Klevtsovskiy, A. V. (2022). Asymptotic expansion for the solution of a convection-diffusion problem in a thin graph-like junction. Asymptotic Analysis, 130(3–4), Article 3–4. https://doi.org/10.3233/ASY-221761
    61. Merkle, R., & Barth, A. (2022). Subordinated Gaussian Random Fields in Elliptic Partial Differential Equations. Stoch PDE: Anal Comp. https://doi.org/10.1007/s40072-022-00246-w
    62. Merkle, R., & Barth, A. (2022). On some distributional properties of subordinated Gaussian random fields. Methodol Comput Appl Probab.
    63. Merkle, R., & Barth, A. (2022). Multilevel Monte Carlo estimators for elliptic PDEs with Lévy-type diffusion coefficient. BIT Numer Math. https://doi.org/10.1007/s10543-022-00912-4
    64. Mikhalev, A., Nottoli, M., & Stamm, B. (2022). Linearly scaling computation of ddPCM solvation energy and forces using the fast multipole method. The Journal of Chemical Physics, 157(11), Article 11. https://doi.org/10.1063/5.0104536
    65. Nitzsche, M., Albers, H., Kluth, T., & Hahn, B. (2022). Compensating model imperfections during image reconstruction via Resesop. International Journal on Magnetic Particle Imaging, Vol 8 No 1 Suppl 1 (2022). https://doi.org/10.18416/IJMPI.2022.2203062
    66. Nottoli, M., Mikhalev, A., Stamm, B., & Lipparini, F. (2022). Coarse-Graining ddCOSMO through an Interface between Tinker and the ddX Library. The Journal of Physical Chemistry B, 126(43), Article 43. https://doi.org/10.1021/acs.jpcb.2c04579
    67. Rettberg, J., Wittwar, D., Buchfink, P., Brauchler, A., Ziegler, P., Fehr, J., & Haasdonk, B. (2022). Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar. https://doi.org/10.48550/arXiv.2203.10061
    68. Rösinger, C. A., & Scherer, C. W. (2022). Gain-Scheduling Controller Synthesis for Networked Systems with Full Block Scalings.
    69. Santin, G., Karvonen, T., & Haasdonk, B. (2022). Sampling based approximation of linear functionals in reproducing kernel Hilbert spaces. BIT Numerical Mathematics, 62(1), Article 1. https://doi.org/10.1007/s10543-021-00870-3
    70. Scherer, C. (2022). Dissipativity and Integral Quadratic Constraints, Tailored computational robustness tests for complex interconnections. IEEE Control Systems Magazine, 42(3), Article 3. https://arxiv.org/abs/2105.07401
    71. Scherer, C. W. (2022). Dissipativity, Convexity and Tight O\textquotesingleShea-Zames-Falb Multipliers for Safety Guarantees. IFAC-PapersOnLine, 55(30), Article 30. https://doi.org/10.1016/j.ifacol.2022.11.044
    72. Schneider, G., & Winter, M. (2022). The amplitude system for a simultaneous short-wave Turing  and long-wave Hopf instability. Discrete Contin. Dyn. Syst. Ser. S, 15(9), Article 9. https://doi.org/10.3934/dcdss.2021119
    73. Schneider, G., & Winter, M. (2022). The amplitude system for a imultaneous short-wave Turing and long-wave Hopf instability. Discrete Contin. Dyn. Syst. Ser. S, 15(9), Article 9. https://doi.org/10.3934/dcdss.2021119
    74. Shuva, S., Buchfink, P., Röhrle, O., & Haasdonk, B. (2022). Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue. In I. Lirkov & S. Margenov (Hrsg.), Large-Scale Scientific Computing (S. 402--409). Springer International Publishing.
    75. Stamm, B., & Theisen, L. (2022). A Quasi-Optimal Factorization Preconditioner for Periodic Schrödinger Eigenstates in Anisotropically Expanding Domains. SIAM Journal on Numerical Analysis, 60(5), Article 5. https://doi.org/10.1137/21m1456005
    76. von Wolff, L., & Pop, I. S. (2022). Upscaling of a Cahn–Hilliard Navier–Stokes model with precipitation and dissolution in a thin strip. Journal of Fluid Mechanics, 941, A49--. https://doi.org/DOI: 10.1017/jfm.2022.308
    77. Wenzel, T., Kurz, M., Beck, A., Santin, G., & Haasdonk, B. (2022). Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows. In I. Lirkov & S. Margenov (Hrsg.), Large-Scale Scientific Computing (S. 410--418). Springer International Publishing.
    78. Wenzel, T., Santin, G., & Haasdonk, B. (2022). Stability of convergence rates: Kernel interpolation on non-Lipschitz domains. arXiv. https://doi.org/10.48550/ARXIV.2203.12532
    79. Wenzel, T., Santin, G., & Haasdonk, B. (2022). Analysis of Target Data-Dependent Greedy Kernel Algorithms: Convergence Rates for f-, \$\$f \backslashcdot P\$\$- and f/P-Greedy. Constructive Approximation. https://doi.org/10.1007/s00365-022-09592-3
    80. Wirth, J., & Sebih, M. E. (2022). On a wave equation with singular dissipation. Mathematische Nachrichten, 295(8), Article 8. https://doi.org/10.1002/mana.202000076
    81. Zaverkin, V., Holzmüller, D., Schuldt, R., & Kästner, J. (2022). Predicting properties of periodic systems from cluster data: A case study of liquid water. The Journal of Chemical Physics, 156(11), Article 11. https://doi.org/10.1063/5.0078983
    82. Zaverkin, V., Holzmüller, D., Steinwart, I., & Kästner, J. (2022). Exploring chemical and conformational spaces by batch mode deep active learning. Digital Discovery, 1, 605–620. https://doi.org/10.1039/D₂DD00034B
    83. Zinßer, M., Braun, B., Helder, T., Magorian Friedlmeier, T., Pieters, B., Heinlein, A., Denk, M., Göddeke, D., & Powalla, M. (2022). 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. https://doi.org/10.1557/s43580-022-00321-3
  5. 2021

    1. Wittwar, D., & Haasdonk, B. (o. J.). Convergence rates for matrix P-greedy variants. In Numerical mathematics and advanced applications---ENUMATH              2019 (Bd. 139, S. 1195--1203). Springer, Cham. https://doi.org/10.1007/978-3-030-55874-1\_119
    2. Alonso-Orán, D., Rohde, C., & Tang, H. (2021). A local-in-time theory for singular SDEs with applications to fluid models with transport noise. J. Nonlinear Sci., 31(6), Article 6. https://doi.org/doi.org/10.1007/s00332-021-09755-9
    3. Altenbernd, M., Dreier, N.-A., Engwer, C., & Göddeke, D. (2021). Towards Local-Failure Local-Recovery in PDE Frameworks: The Case of Linear Solvers. In T. Kozubek, P. Arbenz, J. Jaros, L. Ríha, J. Sístek, & P. Tichý (Hrsg.), High Performance Computing in Science and Engineering -- HPCSE 2019 (Bd. 12456, S. 17--38). Springer. https://doi.org/10.1007/978-3-030-67077-1_2
    4. Altmann, K., & Witt, F. (2021). Toric co-Higgs sheaves. Journal of Pure and Applied Algebra, 225(8), Article 8. https://doi.org/10.1016/j.jpaa.2020.106634
    5. Barth, A., & Merkle, R. (2021). Multilevel Monte Carlo estimators for elliptic PDEs with Lévy-type diffusion coefficient. ArXiv e-prints, arXiv:2108.05604 math.NA.
    6. Beck, A., Dürrwächter, J., Kuhn, T., Meyer, F., Munz, C.-D., & Rohde, C. (2021). Uncertainty Quantification in High Performance Computational Fluid Dynamics. In W. E. Nagel, D. H. Kröner, & M. M. Resch (Hrsg.), High Performance Computing in Science and Engineering ’19 (S. 355--371). Springer International Publishing.
    7. Benacchio, T., Bonaventura, L., Altenbernd, M., Cantwell, C. D., Düben, P. D., Gillard, M., Giraud, L., Göddeke, D., Raffin, E., Teranishi, K., & Wedi, N. (2021). Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction. The International Journal of High Performance Computing Applications, 35(4), Article 4. https://doi.org/10.1177/1094342021990433
    8. Benguria, R. D., Cianchi, A., Maz’ya, V. G., Davies, E. B., Takhtajan, L. A., Tretter, C., Yafaev, D., & und weitere. (2021). Partial differential equations, spectral theory, and mathematical physics—the Ari Laptev anniversary volume. In P. Exner, R. L. Frank, F. Gesztesy, H. Holden, & T. Weidl (Hrsg.), EMS Series of Congress Reports. EMS Press, Berlin. https://doi.org/10.4171/ECR/18
    9. Berrett, T. B., Gyorfi, L., & Walk, H. (2021). Strongly universally consistent nonparametric regression and    classification with privatised data. ELECTRONIC JOURNAL OF STATISTICS, 15(1), Article 1. https://doi.org/10.1214/21-EJS1845
    10. Brencher, L., & Barth, A. (2021). Stochastic conservation laws with discontinuous flux functions: The multidimensional case.
    11. Brencher, L., & Barth, A. (2021). Scalar conservation laws with stochastic discontinuous flux function. ArXiv e-prints, arXiv:2107.00549 math.NA.
    12. Buchfink, P., Glas, S., & Haasdonk, B. (2021). Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds. https://doi.org/10.48550/arXiv.2112.10815
    13. Buchfink, P., & Haasdonk, B. (2021). Experimental Comparison of Symplectic and Non-symplectic Model Order Reduction an Uncertainty Quantification Problem. In F. J. Vermolen & C. Vuik (Hrsg.), Numerical Mathematics and Advanced Applications ENUMATH 2019 (Bd. 139). Springer International Publishing. https://doi.org/10.1007/978-3-030-55874-1
    14. Cleyton, R., Moroianu, A., & Semmelmann, U. (2021). Metric connections with parallel skew-symmetric torsion. Adv. Math., 378, 107519, 50. https://doi.org/10.1016/j.aim.2020.107519
    15. de Rijk, B., & Sandstede, B. (2021). Diffusive stability against nonlocalized perturbations of              planar wave trains in reaction-diffusion systems. J. Differential Equations, 274, 1223--1261. https://doi.org/10.1016/j.jde.2020.10.027
    16. de Rijk, B., & Schneider, G. (2021). Global existence and decay in multi-component reaction-diffusion-advection systems with different velocities: oscillations in time and frequency. NoDEA, Nonlinear Differ. Equ. Appl., 28(1), Article 1.
    17. Düll, W.-P. (2021). Validity of the nonlinear Schrödinger approximation for the two-dimensional water wave problem with and without surface tension in the arc length formulation. Arch. Ration. Mech. Anal., 239(2), Article 2. https://doi.org/10.1007/s00205-020-01586-4
    18. Dürrwächter, J., Meyer, F., Kuhn, T., Beck, A., Munz, C.-D., & Rohde, C. (2021). A high-order stochastic Galerkin code for the compressible Euler and Navier-Stokes equations. Computers & Fluids, 228, 1850044, 20. https://doi.org/10.1016/j.compfluid.2021.105039
    19. Echterdiek, F., Kitterer, D., Dippon, J., Paul, G., Schwenger, V., & Latus, J. (2021). Impact of cardiopulmonary resuscitation on outcome of kidney transplantations from braindead donors aged ≥65 years. Clin Transplant., 2021 Aug 13:, e14452. https://doi.org/10.1111/ctr.14452
    20. Eggenweiler, E., & Rybak, I. (2021). Effective coupling conditions for arbitrary flows in Stokes-Darcy systems. Multiscale Model. Simul., 19, 731–757. https://doi.org/10.1137/20M1346638
    21. Ehring, T., & Haasdonk, B. (2021). Greedy sampling and approximation for realizing feedback control for high dimensional nonlinear systems.
    22. Ehring, T., & Haasdonk, B. (2021). Feedback control for a coupled soft tissue system by kernel surrogates. Coupled Problems 2021, IS11, Article IS11. https://doi.org/10.23967/coupled.2021.026
    23. Fiedler, C., Scherer, C. W., & Trimpe, S. (2021). Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), Article 8. https://ojs.aaai.org/index.php/AAAI/article/view/16912
    24. Fiedler, C., Scherer, C. W., & Trimpe, S. (2021). Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees. 60th IEEE Conf. Decision and Control, 5122–5129. https://arxiv.org/abs/2105.03397
    25. Freiberg, U., & Kohl, S. (2021). Box dimension of fractal attractors and their numerical computation. COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 95. https://doi.org/10.1016/j.cnsns.2020.105615
    26. Gander, M., Lunowa, S., & Rohde, C. (2021). Consistent and asymptotic-preserving finite-volume domain decomposition methods for singularly perturbed elliptic equations. Domain Decomposition Methods in Science and Engineering XXVI. http://www.uhasselt.be/Documents/CMAT/Preprints/2021/UP2103.pdf
    27. Geck, M. (2021). Generalised Gelfand-Graev representations in bad characteristic? Transformation Groups, 26(1), Article 1. https://doi.org/10.1007/s00031-020-09575-3
    28. Giesselmann, J., Meyer, F., & Rohde, C. (2021). Error control for statistical solutions of hyperbolic systems of conservation laws. Calcolo, 58(2), Article 2. https://doi.org/10.1007/s10092-021-00417-6
    29. Girardi, G., & Wirth, J. (2021). Decay Estimates for a Klein-Gordon Model with Time-Periodic Coeffizients. In M. Cicognani, D. del Santo, A. Parmeggiani, & M. Reissig (Hrsg.), Anomalies in Partial Differential Equations (Bd. 43). Springer. https://doi.org/10.1007/978-3-030-61346-4_14
    30. Haasdonk, B., Hamzi, B., Santin, G., & Wittwar, D. (2021). Kernel methods for center manifold approximation and a weak              data-based version of the center manifold theorem. Phys. D, 427, Paper No. 133007, 14. https://doi.org/10.1016/j.physd.2021.133007
    31. Haasdonk, B. (2021). Model Order Reduction, Applications, MOR Software (D. Gruyter, Hrsg.; Bd. 3). De Gruyter. https://doi.org/10.1515/9783110499001
    32. Haasdonk, B., Ohlberger, M., & Schindler, F. (2021). An adaptive model hierarchy for data-augmented training of kernel models for reactive flow. arXiv. https://doi.org/10.48550/ARXIV.2110.12388
    33. Haasdonk, B., Wenzel, T., Santin, G., & Schmitt, S. (2021). Biomechanical Surrogate Modelling Using Stabilized Vectorial Greedy Kernel Methods.
    34. Hahn, B. N. (2021). Motion compensation strategies in tomography. https://doi.org/10.1007/978-3-030-57784-1_3
    35. Hahn, B. N., Kienle-Garrido, M. L., & Quinto, E. T. (2021). Microlocal properties of dynamic Fourier integral operators. https://doi.org/10.1007/978-3-030-57784-1_4
    36. Hamm, T., & Steinwart, I. (2021). Adaptive Learning Rates for Support Vector Machines Working on Data with Low Intrinsic Dimension. Ann. Statist.
    37. Hamm, T., & Steinwart, I. (2021). Intrinsic Dimension Adaptive Partitioning for Kernel Methods. Fakultät für Mathematik und Physik, Universität Stuttgart.
    38. Hang, H., & Steinwart, I. (2021). Optimal Learning with Anisotropic Gaussian SVMs. Appl. Comput. Harmon. Anal., 55, Article 55. https://doi.org/10.1016/j.acha.2021.06.004
    39. Hilder, B. (2021). Nonlinear stability of fast invading fronts in a Ginzburg–Landau equation with an additional conservation law. Nonlinearity, 34(8), Article 8. https://doi.org/10.1088/1361-6544/abd612
    40. Holicki, T., & Scherer, C. W. (2021). Robust Gain-Scheduled Estimation with Dynamic D-Scalings. IEEE Trans. Autom. Control. https://doi.org/10.1109/TAC.2021.3052751
    41. Holicki, T., & Scherer, C. W. (2021). Algorithm Design and Extremum Control: Convex Synthesis due to Plant Multiplier Commutation. Proc. 60th IEEE Conf. Decision and Control, 3249–3256. https://doi.org/10.1109/CDC45484.2021.9683012
    42. Holicki, T., Scherer, C. W., & Trimpe, S. (2021). Controller Design via Experimental Exploration with Robustness Guarantees. IEEE Control Syst. Lett., 5(2), Article 2. https://doi.org/10.1109/LCSYS.2020.3004506
    43. Holicki, T., & Scherer, C. W. (2021). Revisiting and Generalizing the Dual Iteration for Static and Robust Output-Feedback Synthesis. Int. J. Robust Nonlin., 1–33. https://doi.org/10.1002/rnc.5547
    44. Holzmüller, D., & Pflüger, D. (2021). Fast Sparse Grid Operations Using the Unidirectional Principle: A Generalized and Unified Framework. In H.-J. Bungartz, J. Garcke, & D. Pflüger (Hrsg.), Sparse Grids and Applications - Munich 2018 (S. 69--100). Springer International Publishing.
    45. Hsiao, G. C., & Wendland, W. L. (2021). On the propagation of acoustic waves in a thermo-electro-magneto-elastic solid. Applicable Analysis, 101 (2022)(0), Article 0. https://doi.org/10.1080/00036811.2021.1986027
    46. Hsiao, G. C., & Wendland, W. L. (2021). Boundary integral equations. In Applied Mathematical Sciences (Bd. 164, S. xx+783). Springer, Cham. https://doi.org/10.1007/978-3-030-71127-6
    47. Aufgaben und Lösungen zur Höheren Mathematik 1. (2021). In K. V. Höllig & J. V. Hörner (Hrsg.), Springer eBook Collection (3rd ed. 2021.). https://doi.org/10.1007/978-3-662-63181-2
    48. Jentsch, T., & Weingart, G. (2021). Jacobi relations on naturally reductive spaces. ANNALS OF GLOBAL ANALYSIS AND GEOMETRY, 59(1), Article 1. https://doi.org/10.1007/s10455-020-09740-7
    49. Kohr, M., Mikhailov, S. E., & Wendland, W. L. (2021). Layer potential theory for the anisotropic Stokes system with variable L∞ symmetrically elliptic tensor coefficient. Math. Methods Appl. Sci., 44(12), Article 12. https://doi.org/10.1002/mma.7167
    50. Kohr, M., Mikhailov, S. E., & Wendland, W. L. (2021). Dirichlet and transmission problems for anisotropic Stokes and Navier-Stokes systems with L∞ tensor coefficient under relaxed ellipticity condition. Discrete Contin. Dyn. Syst., 41(9), Article 9. https://doi.org/10.3934/dcds.2021042
    51. Kohr, M., Mikhailov, S. E., & Wendland, W. L. (2021). Layer potential theory for the anisotropic Stokes system with variable L∞ symmetrically elliptic tensor coeffici. Math. Methods Appl. Sci., 44(12), Article 12. https://doi.org/10.1002/mma.7167
    52. Kollross, A. (2021). Polar actions on Damek-Ricci spaces. Differential Geometry and its Applications, 76, 101753. https://doi.org/10.1016/j.difgeo.2021.101753
    53. Krämer, A., Maier, B., Rau, T., Huber, F., Klotz, T., Ertl, T., Göddeke, D., Mehl, M., Reina, G., & Röhrle, O. (2021). Multi-physics multi-scale HPC simulations of skeletal muscles. In W. E. Nagel, D. H. Kröner, & M. M. Resch (Hrsg.), High Performance Computing in Science and Engineering ’20: Transactions of the High Performance Computing Center, Stuttgart(HLRS) 2020. https://doi.org/10.1007/978-3-030-80602-6_13
    54. Kühnert, J., Göddeke, D., & Herschel, M. (2021, Juli). Provenance-integrated parameter selection and optimization in numerical simulations. 13th International Workshop on Theory and Practice ofProvenance (TaPP 2021). https://www.usenix.org/conference/tapp2021/presentation/kühnert
    55. Lang, R. (2021). On the eigenvalues of the non-self-adjoint Robin Laplacian on bounded domains and compact quantum graphs. [Dissertation, Universität Stuttgart]. https://doi.org/10.18419/opus-11428
    56. Leiteritz, R., Buchfink, P., Haasdonk, B., & Pflüger, D. (2021). Surrogate-data-enriched Physics-Aware Neural Networks.
    57. Magiera, J. (2021). A Molecular--Continuum Multiscale Solver for Liquid--Vapor Flow. Small Collaboration: Advanced Numerical Methods for Nonlinear Hyperbolic Balance Laws and Their Applications (hybrid meeting), 41. https://doi.org/10.14760/OWR-2021-41
    58. Magiera, J. (2021). A Molecular--Continuum Multiscale Solver for Liquid--Vapor Flow: Modeling and Numerical Simulation [Ph.D. Thesis]. https://doi.org/10.18419/opus-11797
    59. Makogin, V., Oesting, M., Rapp, A., & Spodarev, E. (2021). Long range dependence for stable random processes. J. Time Series Anal., 42(2), Article 2. https://doi.org/10.1111/jtsa.12560
    60. Mehl, L., Beschle, C., Barth, A., & Bruhn, A. (2021). 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), 140--152. https://doi.org/10.1007/978-3-030-75549-2_12
    61. Mel’nyk, T. (2021). 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, 19(05), Article 05. https://doi.org/10.1142/S0219530520500219
    62. Michalowsky, S., Scherer, C., & Ebenbauer, C. (2021). Robust and structure exploiting optimisation algorithms: An integral quadratic constraint approach. International Journal of Control, 94(11), Article 11. https://doi.org/10.1080/00207179.2020.1745286
    63. Nonnenmacher, M., Reeb, D., & Steinwart, I. (2021). Which Minimizer Does My Neural Network Converge To? In N. Oliver, F. Pérez-Cruz, S. Kramer, J. Read, & J. A. Lozano (Hrsg.), Joint European Conference on Machine Learning and Knowledge Discovery in Databases (S. 87--102). Springer International Publishing. https://doi.org/10.1007/978-3-030-86523-8_6
    64. Osorno, M., Schirwon, M., Kijanski, N., Sivanesapillai, R., Steeb, H., & Göddeke, D. (2021). A cross-platform, high-performance SPH toolkit for image-based flow simulations on the pore scale of porous media. Computer Physics Communications, 267(108059), Article 108059. https://doi.org/10.1016/j.cpc.2021.108059
    65. Rohde, C., & Tang, H. (2021). On the stochastic Dullin-Gottwald-Holm equation: global existence and wave-breaking phenomena. NoDEA Nonlinear Differential Equations Appl., 28(1), Article 1. https://doi.org/10.1007/s00030-020-00661-9
    66. Rohde, C., & Tang, H. (2021). On a stochastic Camassa-Holm type equation with higher order nonlinearities. J. Dynam. Differential Equations, 33, 1823–1852. https://doi.org/10.1007/s10884-020-09872-1
    67. Rohde, C., & Von Wolff, L. (2021). A ternary Cahn–Hilliard–Navier–Stokes model for two-phase flow with precipitation and dissolution. Mathematical Models and Methods in Applied Sciences, 31(01), Article 01. https://doi.org/10.1142/S0218202521500019
    68. Rybak, I., Schwarzmeier, C., Eggenweiler, E., & Rüde, U. (2021). Validation and calibration of coupled porous-medium and free-flow problems using pore-scale resolved models. Comput. Geosci., 25, 621–635. https://doi.org/10.1007/s10596-020-09994-x
    69. Rörich, A., Werthmann, T. A., Göddeke, D., & Grasedyck, L. (2021). Bayesian inversion for electromyography using low-rank tensor formats. Inverse Problems, 37(5), Article 5. https://doi.org/10.1088/1361-6420/abd85a
    70. Santin, G., & Haasdonk, B. (2021). Kernel methods for surrogate modeling. In P. Benner, W. Schilders, S. Grivet-Talocia, A. Quarteroni, G. Rozza, & L. M. Silveira (Hrsg.), Model Order Reduction: Bd. 1: System-and Data-Driven Methods and Algorithms (S. 311–354). de Gruyter.
    71. Scherer, C., & Ebenbauer, C. (2021). Convex Synthesis of Accelerated Gradient Algorithms. SIAM J. Contr. Optim. (to appear). https://arxiv.org/abs/2102.06520
    72. Schmalfuss, J., Riethmüller, C., Altenbernd, M., Weishaupt, K., & Göddeke, D. (2021). Partitioned coupling vs. monolithic block-preconditioning approaches for solving Stokes-Darcy systems. Proceedings of the International Conference on Computational Methods for Coupled Problems in Science and Engineering (COUPLED PROBLEMS). https://doi.org/10.23967/coupled.2021.043
    73. Schricker, S., Monje, DC., Dippon, J., Kimmel, M., Alscher, MD., & Schanz, M. (2021). Physician-guided, hybrid genetic testing exerts promising effects on health-related behavior without compromising quality of life. Sci Rep., 2021 Apr 19;11(1), 8494. https://doi.org/10.1038/s41598-021-87821-8
    74. Stauch, G., Fritz, P., Rokai, R., Sediqi, A., Firooz, H., Voelker, HU., Weinhara, M., Mollin, J., Soudah, B., Dalquen, P., Brinckmann, F., & Dippon, J. (2021). The Importance of Clinical Data for the Diagnosis of Breast Tumours in North Afghanistan. Int. Jounal Breast Cancer, Jul 30;2021, 6625239. https://doi.org/10.1155/2021/6625239
    75. Steinwart, I., & Fischer, S. (2021). A Closer Look at Covering Number Bounds for Gaussian Kernels. J. Complexity, 62, 101513. https://doi.org/10.1016/j.jco.2020.101513
    76. Steinwart, I., & Ziegel, J. F. (2021). Strictly proper kernel scores and characteristic kernels on compact spaces. Appl. Comput. Harmon. Anal., 51, 510--542. https://doi.org/10.1016/j.acha.2019.11.005
    77. Veenman, J., Scherer, C. W., Ardura, C., Bennani, S., Preda, V., & Girouart, B. (2021). IQClab: A new IQC based toolbox for robustness analysis and control design. IFAC-PapersOnline, 54(8), Article 8. https://doi.org/10.1016/j.ifacol.2021.08.583
    78. von Wolff, L. (2021). The Dune-Phasefield Module release 1.0. DaRUS. https://doi.org/10.18419/darus-1634
    79. Von Wolff, L., Weinhardt, F., Class, H., Hommel, J., & Rohde, C. (2021). Investigation of Crystal Growth in Enzymatically Induced Calcite Precipitation by Micro-Fluidic Experimental Methods and Comparison with Mathematical Modeling. Transport in Porous Media, 137(2), Article 2. https://doi.org/10.1007/s11242-021-01560-y
    80. Wagner, A., Eggenweiler, E., Weinhardt, F., Trivedi, Z., Krach, D., Lohrmann, C., Jain, K., Karadimitriou, N., Bringedal, C., Voland, P., Holm, C., Class, H., Steeb, H., & Rybak, I. (2021). Permeability estimation of regular porous structures: a benchmark for comparison of methods. Transp. Porous Med., 138, 1–23. https://doi.org/10.1007/s11242-021-01586-2
    81. Wenzel, T., Santin, G., & Haasdonk, B. (2021). A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability and uniform point distribution.
    82. Wenzel, T., Santin, G., & Haasdonk, B. (2021). Universality and Optimality of Structured Deep Kernel Networks. arXiv. https://doi.org/10.48550/ARXIV.2105.07228
    83. Wenzel, T., Santin, G., & Haasdonk, B. (2021). Analysis of target data-dependent greedy kernel algorithms: Convergence rates for $f$-, $f P$- and $f/P$-greedy. arXiv. https://doi.org/10.48550/ARXIV.2105.07411
    84. Wenzel, T., Santin, G., & Haasdonk, B. (2021). Analysis of target data-dependent greedy kernel algorithms: Convergence rates for f-, f P- and f/P-greedy. arXiv. https://doi.org/10.48550/ARXIV.2105.07411
    85. Zaverkin, V., Kästner, J., Holzmüller, D., & Steinwart, I. (2021). Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments. J. Chem. Theory Comput. https://doi.org/10.1021/acs.jctc.1c00527
  6. 2020

    1. Alla, A., Haasdonk, B., & Schmidt, A. (2020). Feedback control of parametrized PDEs via model order              reduction and dynamic programming principle. Adv. Comput. Math., 46(1), Article 1. https://doi.org/10.1007/s10444-020-09744-8
    2. Armiti-Juber, A., & Rohde, C. (2020). On the well-posedness of a nonlinear fourth-order extension of Richards’ equation. J. Math. Anal. Appl., 487(2), Article 2. https://doi.org/10.1016/j.jmaa.2020.124005
    3. Barberis, M. L., Moroianu, A., & Semmelmann, U. (2020). Generalized vector cross products and Killing forms on negatively curved manifolds. Geom. Dedicata, 205, 113--127. https://doi.org/10.1007/s10711-019-00467-9
    4. Barreau, M., Scherer, C. W., Gouaisbaut, F., & Seuret, A. (2020). Integral Quadratic Constraints on Linear Infinite-dimensional Systems for Robust Stability Analysis. IFAC World Congress.
    5. Barth, A., & Merkle, R. (2020). Subordinated Gaussian Random Fields in Elliptic Partial Differential Equations. ArXiv e-prints, arXiv:2011.09311 math.NA.
    6. Barth, A., & Merkle, R. (2020). Subordinated Gaussian Random Fields. ArXiv e-prints, arXiv:2012.06353 math.PR.
    7. Bastian, P., Altenbernd, M., Dreier, N.-A., Engwer, C., Fahlke, J., Fritze, R., Geveler, M., Göddeke, D., Iliev, O., Ippisch, O., Mohring, J., Müthing, S., Ohlberger, M., Ribbrock, D., Shegunov, N., & Turek, S. (2020). Exa-Dune - Flexible PDE Solvers, Numerical Methods and Applications. In H.-J. Bungartz, S. Reiz, B. Uekermann, P. Neumann, & W. E. Nagel (Hrsg.), Software for Exascale Computing -- SPPEXA 2016--2019 (S. 225--269). Springer. https://doi.org/10.1007/978-3-030-47956-5_9
    8. Baumstark, S., Schneider, G., & Schratz, K. (2020). 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 (S. 37--48). Cham: Birkhäuser.
    9. Baumstark, S., Schneider, G., Schratz, K., & Zimmermann, D. (2020). Effective slow dynamics models for a class of dispersive systems. J. Dyn. Differ. Equations, 32(4), Article 4.
    10. Beck, A., Dürrwächter, J., Kuhn, T., Meyer, F., Munz, C.-D., & Rohde, C. (2020). $hp$-Multilevel Monte Carlo methods for uncertainty quantification of compressible flows. SIAM J. Sci. Comput., 42(4), Article 4. https://doi.org/10.1137/18M1210575
    11. Berberich, J., Koch, A., Scherer, C. W., & Allgöwer, F. (2020). Robust data-driven state-feedback design. 2020 American Control Conference (ACC), 1532–1538. https://doi.org/10.23919/acc45564.2020.9147320
    12. Berre, I., Boon, W. M., Flemisch, B., Fumagalli, A., Gläser, D., Keilegavlen, E., Scotti, A., Stefansson, I., Tatomir, A., Brenner, K., Burbulla, S., Devloo, P., Duran, O., Favino, M., Hennicker, J., Lee, I.-H., Lipnikov, K., Masson, R., Mosthaf, K., … Zulian, P. (2020). Verification benchmarks for single-phase flow in three-dimensional fractured porous media.
    13. Bitter, A. (2020). Virtual levels of multi-particle quantum systems and their implications for the Efimov effect [Dissertation, Universität Stuttgart]. https://doi.org/10.18419/opus-11315
    14. Blanke, S. E., Hahn, B. N., & Wald, A. (2020). Inverse problems with inexact forward operator: iterative regularization and application in dynamic imaging. Inverse Problems, 36(12), Article 12. https://doi.org/10.1088/1361-6420/abb5e1
    15. Brehler, M., Schirwon, M., Krummrich, P. M., & Göddeke, D. (2020). Simulation of Nonlinear Signal Propagation in Multimode Fibers on Multi-GPU Systems. Communications in Nonlinear Science and Numerical Simulation, 84, 105150. https://doi.org/10.1016/j.cnsns.2019.105150
    16. Brencher, L., & Barth, A. (2020). Hyperbolic Conservation Laws with Stochastic Discontinuous Flux Functions. International Conference on Finite Volumes for Complex Applications, 265--273.
    17. Bringedal, C., Von Wolff, L., & Pop, I. S. (2020). Phase Field Modeling of Precipitation and Dissolution Processes in Porous Media: Upscaling and Numerical Experiments. Multiscale Modeling &amp$\mathsemicolon$ Simulation, 18(2), Article 2. https://doi.org/10.1137/19m1239003
    18. Brinker, J., & Wirth, J. (2020). Gelfand Triples for the Kohn–Nirenberg Quantization on Homogeneous Lie Groups. In Advances in Harmonic Analysis and Partial Differential Equations. (S. 51–97). Birkhäuser. https://doi.org/10.1007/978-3-030-58215-9_3
    19. Buchfink, P., Haasdonk, B., & Rave, S. (2020). PSD-Greedy Basis Generation for Structure-Preserving Model Order Reduction of Hamiltonian Systems. In P. Frolkovič, K. Mikula, & D. Ševčovič (Hrsg.), Proceedings of the Conference Algoritmy 2020 (S. 151--160). Vydavateľstvo SPEKTRUM. http://www.iam.fmph.uniba.sk/amuc/ojs/index.php/algoritmy/article/view/1577/829
    20. Burbulla, S., & Rohde, C. (2020). A fully conforming finite volume approach to two-phase flow in fractured porous media. In R. Klöfkorn, E. Keilegavlen, F. A. Radu, & J. Fuhrmann (Hrsg.), Finite Volumes for Complex Applications IX - Methods, Theoretical Aspects, Examples (S. 547–555). Springer International Publishing. https://doi.org/10.1007/978-3-030-43651-3_51
    21. de Rijk, B., & Schneider, G. (2020). Global Existence and Decay in Nonlinearly Coupled Reaction-Diffusion-Advection Equations with Different Velocities. J. Differential Equations, 268(7), Article 7. https://doi.org/10.1016/j.jde.2019.09.056
    22. Díaz-Ramos, J. C., Domínguez-Vázquez, M., & Kollross, A. (2020). On homogeneous manifolds whose isotropy actions are polar. manuscripta mathematica, 161(1), Article 1. https://doi.org/10.1007/s00229-018-1077-1
    23. Eggenweiler, E., & Rybak, I. (2020). Unsuitability of the Beavers-Joseph interface condition for filtration problems. J. Fluid Mech., 892, A10. http://dx.doi.org/10.1017/jfm.2020.194
    24. Eggenweiler, E., & Rybak, I. (2020). Interface conditions for arbitrary flows in coupled porous-medium and free-flow systems. In R. Klöfkorn, E. Keilegavlen, F. Radu, & J. Fuhrmann (Hrsg.), Finite Volumes for Complex Applications IX - Methods, Theoretical Aspects, Examples (Bd. 323, S. 345--353). Springer International Publishing. https://doi.org/10.1007/978-3-030-43651-3_31
    25. Escher, J., Knopf, P., Lienstromberg, C., & Matioc, B.-V. (2020). Stratified periodic water waves with singular density              gradients. Ann. Mat. Pura Appl. (4), 199(5), Article 5. https://doi.org/10.1007/s10231-020-00950-1
    26. IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart,  Germany, May 22-25, 2018: MORCOS 2018. (2020). In J. Fehr & B. Haasdonk (Hrsg.), IUTAM Bookseries. Springer.
    27. Fischer, S., & Steinwart, I. (2020). Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm. J. Mach. Learn. Res., 205, Article 205.
    28. Fischer, S., & Steinwart, I. (2020). Sobolev norm learning rates for regularized least-squares algorithms. J. Mach. Learn. Res., 21(205), Article 205. http://jmlr.org/papers/v21/19-734.html
    29. Geck, M. (2020). Green functions and Glauberman degree-divisibility. Annals of Mathematics, 192(1), Article 1. https://doi.org/10.4007/annals.2020.192.1.4
    30. Geck, M. (2020). Computing Green functions in small characteristic. Journal of Algebra, 561, 163--199. https://doi.org/10.1016/j.jalgebra.2019.12.016
    31. Geck, M. (2020). ChevLie: Constructing Lie algebras and Chevalley groups. Journal of Software for Algebra and Geometry, 10(1), Article 1. https://doi.org/10.2140/jsag.2020.10.41
    32. Geck, M. (2020). On Jacob’s construction of the rational canonical form of a matrix. The Electronic Journal of Linear Algebra, 36(36), Article 36. https://doi.org/10.13001/ela.2020.5055
    33. Geck, M., & Malle, G. (2020). The character theory of finite groups of Lie type. A guided tour. In Cambridge Studies in Advanced Mathematics (Bd. 187, S. ix+394). Cambridge University Press. https://doi.org/10.1017/9781108779081
    34. Advances in Harmonic Analysis and Partial Differential Equations. (2020). In V. Georgiev, T. Ozawa, M. Ruzhansky, & J. Wirth (Hrsg.), Trends in Mathematics. Birkhäuser. https://doi.org/10.1007/978-3-030-58215-9
    35. Gerstenberger, J. T., Burbulla, S., & Kröner, D. (2020). Discontinuous Galerkin method for incompressible two-phase flows. In R. Klöfkorn, E. Keilegavlen, F. A. Radu, & J. Fuhrmann (Hrsg.), Finite Volumes for Complex Applications IX - Methods, Theoretical Aspects, Examples (S. 675–683). Springer International Publishing.
    36. Giesselmann, J., Meyer, F., & Rohde, C. (2020). An a posteriori error analysis based on non-intrusive spectral projections for systems of random conservation laws. In A. Bressan, M. Lewicka, D. Wang, & Y. Zheng (Hrsg.), Hyperbolic Problems: Theory, Numerics, Applications. Proceedings of the Seventeenth International Conference on Hyperbolic Problems 2018 (Bd. 10, S. 449–456). AIMS Series on Applied Mathematics. https://www.aimsciences.org/fileAIMS/cms/news/info/upload//c0904f1f-97d5-451f-b068-25f1612b6852.pdf
    37. Giesselmann, J., Meyer, F., & Rohde, C. (2020). A posteriori error analysis and adaptive non-intrusive numerical schemes for systems of random conservation laws. BIT Numer. Math. https://doi.org/10.1007/s10543-019-00794-z
    38. Giesselmann, J., Meyer, F., & Rohde, C. (2020). A posteriori error analysis for random scalar conservation laws using the Stochastic Galerkin method. IMA J. Numer. Anal., 40(2), Article 2. https://doi.org/10.1093/imanum/drz004
    39. Ginoux, N., Habib, G., Pilca, M., & Semmelmann, U. (2020). An Obata-type characterisation of Calabi metrics on line bundles. North-West. Eur. J. Math., 6, 119--136, i.
    40. Giraud, L., Rüde, U., & Stals, L. (2020). Resiliency in Numerical Algorithm Design for Extreme Scale Simulations (Dagstuhl Seminar 20101). Dagstuhl Reports, 10(3), Article 3. https://doi.org/10.4230/DagRep.10.3.1
    41. Griesemer, M., Hofacker, M., & Linden, U. (2020). From short-range to contact interactions in the 1d Bose gas. Math. Phys. Anal. Geom., 23(2), Article 2. https://doi.org/10.1007/s11040-020-09344-4
    42. Grunert, D., Fehr, J., & Haasdonk, B. (2020). Well-scaled, a-posteriori error estimation for model order reduction of large second-order mechanical systems. ZAMM, 100(8), Article 8. https://doi.org/10.1002/zamm.201900186
    43. Göddeke, D., Schirwon, M., & Borg, N. (2020). Smartphone-Apps im Mathematikstudium. https://doi.org/10.18419/darus-1147
    44. Haas, T., de Rijk, B., & Schneider, G. (2020). MODULATION EQUATIONS NEAR THE ECKHAUS BOUNDARY: THE KdV EQUATION. SIAM JOURNAL ON MATHEMATICAL ANALYSIS, 52(6), Article 6. https://doi.org/10.1137/19M1266873
    45. Haas, T., & Schneider, G. (2020). Failure of the N-wave interaction approximation without imposing    periodic boundary conditions. ZAMM-ZEITSCHRIFT FUR ANGEWANDTE MATHEMATIK UND MECHANIK, 100(6), Article 6. https://doi.org/10.1002/zamm.201900230
    46. Haasdonk, B., Hamzi, B., Santin, G., & Wittwar, D. (2020). Greedy kernel methods for center manifold approximation. In Spectral and high order methods for partial differential              equations---ICOSAHOM 2018 (Bd. 134, S. 95--106). Springer, Cham. https://doi.org/10.1007/978-3-030-39647-3\_6
    47. Hilder, B. (2020). Modulating traveling fronts for the Swift-Hohenberg equation in the case of an additional conservation law. Journal of Differential Equations, 269(5), Article 5. https://doi.org/10.1016/j.jde.2020.03.033
    48. Hitz, T., Keim, J., Munz, C.-D., & Rohde, C. (2020). A parabolic relaxation model for the Navier-Stokes-Korteweg equations. J. Comput. Phys., 421, 109714. https://doi.org/10.1016/j.jcp.2020.109714
    49. Holicki, T., & Scherer, C. W. (2020). Output-Feedback Synthesis for a Class of Aperiodic Impulsive Systems. IFAC-PapersOnline, 53(2), Article 2. https://doi.org/10.1016/j.ifacol.2020.12.981
    50. Holzmüller, D., & Steinwart, I. (2020). Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent. Fakultät für Mathematik und Physik, Universität Stuttgart.
    51. Holzmüller, D., & Steinwart, I. (2020). Training two-layer ReLU networks with gradient descent is inconsistent. arXiv:2002.04861. https://arxiv.org/abs/2002.04861
    52. Häufle, D. F. B., Wochner, I., Holzmüller, D., Driess, D., Günther, M., & Schmitt, S. (2020). Muscles Reduce Neuronal Information Load : Quantification of Control Effort in Biological vs. Robotic Pointing and Walking. Frontiers In Robotics and AI, 7, 77. https://doi.org/10.3389/frobt.2020.00077
    53. Jentsch, T., & Weingart, G. (2020). RIEMANNIAN AND KAHLERIAN NORMAL COORDINATES. ASIAN JOURNAL OF MATHEMATICS, 24(3), Article 3.
    54. Kennedy, J. B., & Lang, R. (2020). On the eigenvalues of quantum graph Laplacians with large complex δ couplings. Portugaliae Mathematica. A Journal of the Portuguese Mathematical Society, 77(2), Article 2.
    55. Koch, T., Gläser, D., Weishaupt, K., Ackermann, S., Beck, M., Becker, B., Burbulla, S., Class, H., Coltman, E., Emmert, S., Fetzer, T., Grüninger, C., Heck, K., Hommel, J., Kurz, T., Lipp, M., Mohammadi, F., Scherrer, S., Schneider, M., … Flemisch, B. (2020). 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. https://doi.org/10.1016/j.camwa.2020.02.012
    56. Kohr, M., Mikhailov, S. E., & Wendland, W. L. (2020). 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, 65(1), Article 1. https://doi.org/10.1080/17476933.2019.1631293
    57. Kollross, A. (2020). Octonions, triality, the exceptional Lie algebra F4 and polar actions on the Cayley hyperbolic plane. International Journal of Mathematics, 31(07), Article 07. https://doi.org/10.1142/s0129167x20500512
    58. Lienstromberg, C., & Müller, S. (2020). Local strong solutions to a quasilinear degenerate              fourth-order thin-film equation. NoDEA Nonlinear Differential Equations Appl., 27(2), Article 2. https://doi.org/10.1007/s00030-020-0619-x
    59. Magiera, J., Ray, D., Hesthaven, J. S., & Rohde, C. (2020). Constraint-aware neural networks for Riemann problems. J. Comput. Phys., 409(109345), Article 109345. https://doi.org/10.1016/j.jcp.2020.109345
    60. Maier, D. (2020). Construction of breather solutions for nonlinear Klein-Gordon equations    on periodic metric graphs. JOURNAL OF DIFFERENTIAL EQUATIONS, 268(6), Article 6. https://doi.org/10.1016/j.jde.2019.09.035
    61. Maier, D. (2020). BREATHER SOLUTIONS ON DISCRETE NECKLACE GRAPHS. OPERATORS AND MATRICES, 14(3), Article 3. https://doi.org/10.7153/oam-2020-14-48
    62. Michalowsky, S., Scherer, C., & Ebenbauer, C. (2020). Robust and structure exploiting optimisation algorithms : an integral quadratic constraint approach. International Journal of Control, 2020, 1–24. https://doi.org/10.1080/00207179.2020.1745286
    63. Minorics, L. A. (2020). Spectral asymptotics for Krein-Feller operators with respect to V-variable Cantor measures. Forum Mathematicum, 32(1), Article 1. https://doi.org/10.1515/forum-2018-0188
    64. Nagy, P.-A., & Semmelmann, U. (2020). Conformal Killing forms in Kaehler geometry.
    65. Naveira, A. M., & Semmelmann, U. (2020). Conformal Killing forms on nearly Kähler manifolds. Differential Geom. Appl., 70, 101628, 9. https://doi.org/10.1016/j.difgeo.2020.101628
    66. Oesting, M., & Schnurr, A. (2020). Ordinal patterns in clusters of subsequent extremes of regularly varying time series. Extremes, 23(4), Article 4. https://doi.org/10.1007/s10687-020-00391-2
    67. Oladyshkin, S., Mohammadi, F., Kroeker, I., & Nowak, W. (2020). Bayesian(3)Active Learning for the Gaussian Process Emulator Using    Information Theory. ENTROPY, 22(8), Article 8. https://doi.org/10.3390/e22080890
    68. Ostrowski, L., Massa, F. C., & Rohde, C. (2020). A phase field approach to compressible droplet impingement. In G. Lamanna, S. Tonini, G. E. Cossali, & B. Weigand (Hrsg.), Droplet Interactions and Spray Processes (S. 113–126). Springer International Publishing. https://doi.org/10.1007/978-3-030-33338-6_9
    69. Ostrowski, L., & Rohde, C. (2020). Compressible multicomponent flow in porous media with Maxwell-Stefan diffusion. Math. Meth. Appl. Sci., 43(7), Article 7. https://doi.org/10.1002/mma.6185
    70. Ostrowski, L., & Rohde, C. (2020). Phase field modelling for compressible droplet impingement. In A. Bressan, M. Lewicka, D. Wang, & Y. Zheng (Hrsg.), Hyperbolic Problems: Theory, Numerics, Applications. Proceedings of the Seventeenth International Conference on Hyperbolic Problems 2018 (Bd. 10, S. 586–593). AIMS Series on Applied Mathematics. https://www.aimsciences.org/fileAIMS/cms/news/info/upload//c0904f1f-97d5-451f-b068-25f1612b6852.pdf
    71. Pelinovsky, D. E., & Schneider, G. (2020). The monoatomic FPU system as a limit of a diatomic FPU system. Appl. Math. Lett., 107, 7.
    72. Polyakova, A. P., Svetov, I. E., & Hahn, B. N. (2020). The Singular Value Decomposition of the Operators of the Dynamic Ray Transforms Acting on 2D Vector Fields. In Y. D. Sergeyev & D. E. Kvasov (Hrsg.), Numerical Computations: Theory and Algorithms (S. 446--453). Springer International Publishing. https://doi.org/10.1007/978-3-030-40616-5_42
    73. Rigaud, G., & Hahn, B. N. (2020). Reconstruction algorithm for 3D Compton scattering imaging with incomplete data. Inverse Problems in Science and Engineering, 29(7), Article 7. https://doi.org/10.1080/17415977.2020.1815723
    74. Rohde, C., & von Wolff, L. (2020). Homogenization of non-local Navier-Stokes-Korteweg equations for compressible liquid-vapour flow in porous media. SIAM J. Math. Anal., 52(6), Article 6. https://doi.org/10.1137/19M1242434
    75. Rybak, I., & Metzger, S. (2020). A dimensionally reduced Stokes-Darcy model for fluid flow in fractured porous media. Appl. Math. Comp., 384. https://doi.org/10.1016/j.amc.2020.125260
    76. Rösinger, C. A., & Scherer, C. W. (2020). Lifting to Passivity for $H_2$-Gain-Scheduling Synthesis with Full Block Scalings. IFAC-PapersOnline, 53(2), Article 2. https://doi.org/10.1016/j.ifacol.2020.12.570
    77. Rösinger, C. A., & Scherer, C. W. (2020). A Flexible Synthesis Framework of Structured Controllers for Networked Systems. IEEE Trans. Control Netw. Syst., 7(1), Article 1. https://doi.org/10.1109/TCNS.2019.2914411
    78. Schneider, G. (2020). The KdV approximation for a system with unstable resonances. Math. Methods Appl. Sci., 43(6), Article 6.
    79. Semmelmann, U., Wang, C., & Wang, M. Y.-K. (2020). On the linear stability of nearly Kähler 6-manifolds. Ann. Global Anal. Geom., 57(1), Article 1. https://doi.org/10.1007/s10455-019-09686-5
    80. Stein, A., & Barth, A. (2020). A Multilevel Monte Carlo Algorithm for Parabolic Advection-Diffusion Problems with Discontinuous Coefficients. In B. Tuffin & P. L’Ecuyer (Hrsg.), Monte Carlo and Quasi-Monte Carlo Methods (Bd. 324, S. 445--466). Springer International Publishing. https://doi.org/10.1007/978-3-030-43465-6_22
    81. Steinwart, I. (2020). Reproducing Kernel Hilbert Spaces Cannot Contain all Continuous Functions on a Compact Metric Space. Fakultät für Mathematik und Physik, Universität Stuttgart.
    82. Tielen, R., Möller, M., Göddeke, D., & Vuik, C. (2020). p-multigrid methods and their comparison to h-multigrid methods in Isogeometric Analysis. Computer Methods in Applied Mechanics and Engineering, 372, 113347. https://doi.org/10.1016/j.cma.2020.113347
    83. Vonica, A., Bhat, N., Phan, K., Guo, J., Iancu, L., Weber, J. A., Karger, A., Cain, J. W., Wang, E. C. E., DeStefano, G. M., O’Donnell-Luria, A. H., Christiano, A. M., Riley, B., Butler, S. J., & Luria, V. (2020). Apcdd1 is a dual BMP/Wnt inhibitor in the developing nervous system and skin. Developmental Biology, 464(1), Article 1. https://doi.org/10.1016/j.ydbio.2020.03.015
  7. 2019

    1. Ammann, B., Kröncke, K., Weiss, H., & Witt, F. (2019). Holonomy rigidity for Ricci-flat metrics. Math. Z., 291(1–2), Article 1–2. https://doi.org/10.1007/s00209-018-2084-3
    2. Armiti-Juber, A., & Rohde, C. (2019). On Darcy-and Brinkman-type models for two-phase flow in asymptotically flat domains. Comput. Geosci., 23(2), Article 2. https://doi.org/10.1007/s10596-018-9756-2
    3. Armiti-Juber, A., & Rohde, C. (2019). Existence of weak solutions for a nonlocal pseudo-parabolic model for Brinkman two-phase flow in asymptotically flat porous media. J. Math. Anal. Appl., 477(1), Article 1. https://doi.org/10.1016/j.jmaa.2019.04.049
    4. Baggio, G., Zampieri, S., & Scherer, C. W. (2019). Gramian Optimization with Input-Power Constraints. 58th IEEE Conf. Decision and Control, 5686–5691. https://doi.org/10.1109/CDC40024.2019.9029169
    5. Bauer, R., Cummings, P., & Schneider, G. (2019). 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 (S. 123--138). Cham: Birkhäuser.
    6. Bauer, R., Düll, W.-P., & Schneider, G. (2019). The Korteweg--de Vries, Burgers and Whitham limits for a spatially periodic Boussinesq model. Proc. Roy. Soc. Edinburgh Sect. A, 149(1), Article 1. https://doi.org/10.1017/S0308210518000227
    7. Bhatt, A., Fehr, J., Grunert, D., & Haasdonk, B. (2019). A Posteriori Error Estimation in Model Order Reduction of Elastic Multibody Systems with Large Rigid Motion. In J. Fehr & B. Haasdonk (Hrsg.), IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22–25, 2018. Springer. https://doi.org/DOI:10.1007/978-3-030-21013-7_7
    8. Bhatt, A., Fehr, J., & Haasdonk, B. (2019). Model order reduction of an elastic body under large rigid motion. Proceedings of ENUMATH 2017, Lect. Notes Comput. Sci. Eng.,(126), Article 126. https://doi.org/10.1007/978-3-319-96415-7\_23
    9. Bianchi, L. A., Blömker, D., & Schneider, G. (2019). Modulation equation and SPDEs on unbounded domains. Commun. Math. Phys., 371(1), Article 1.
    10. Brünnette, T., Santin, G., & Haasdonk, B. (2019). Greedy Kernel Methods for Accelerating Implicit Integrators for Parametric ODEs. In F. A. Radu, K. Kumar, I. Berre, J. M. Nordbotten, & I. S. Pop (Hrsg.), Numerical Mathematics and Advanced Applications - ENUMATH 2017 (S. 889--896). Springer International Publishing.
    11. Buchfink, P., Bhatt, A., & Haasdonk, B. (2019). Symplectic Model Order Reduction with Non-Orthonormal Bases. Mathematical and Computational Applications, 24(2), Article 2. https://doi.org/10.3390/mca24020043
    12. Carlberg, K., Brencher, L., Haasdonk, B., & Barth, A. (2019). Data-Driven Time Parallelism via Forecasting. SIAM Journal on Scientific Computing, 41(3), Article 3. https://doi.org/10.1137/18M1174362
    13. Chirilus-Bruckner, M., Maier, D., & Schneider, G. (2019). Diffusive stability for periodic metric graphs. Math. Nachr., 292(6), Article 6.
    14. Colombo, R. M., LeFloch, P. G., Rohde, C., & Trivisa, K. (2019). Nonlinear Hyperbolic Problems: Modeling, Analysis, and Numerics. Oberwohlfach Rep., 16, Article 16. https://www.ems-ph.org/journals/show_issue.php?issn=1660-8933&vol=16&iss=2
    15. Conlon, R., Degeratu, A., & Rochon, F. (2019). Quasi-asymptotically conical Calabi-Yau manifolds. Geom. Topol., 23(1), Article 1. https://doi.org/10.2140/gt.2019.23.29
    16. Defant, A., Mastyo, M., Sánchez-Pérez, E. A., & Steinwart, I. (2019). Translation invariant maps on function spaces over locally compact groups. J. Math. Anal. Appl., 470, 795--820. https://doi.org/10.1016/j.jmaa.2018.10.033
    17. Denzel, A., Haasdonk, B., & Kästner, J. (2019). Gaussian Process Regression for Minimum Energy Path Optimization and Transition State Search. J. Phys. Chem. A, 123(44), Article 44. https://doi.org/10.1021/acs.jpca.9b08239
    18. Engelke, S., de Fondeville, R., & Oesting, M. (2019). Extremal behaviour of aggregated data with an application to downscaling. Biometrika, 106(1), Article 1. https://doi.org/10.1093/biomet/asy052
    19. Farooq, M., & Steinwart, I. (2019). Learning Rates for Kernel-Based Expectile Regression. Mach. Learn., 108, 203--227. https://doi.org/10.1007/s10994-018-5762-9
    20. Föll, R., Haasdonk, B., Hanselmann, M., & Ulmer, H. (2019). Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation. https://openreview.net/forum?id=BkgosiRcKm
    21. Geck, M. (2019). Eigenvalues and Polynomial Equations. The American Mathematical Monthly, 126(10), Article 10. https://doi.org/10.1080/00029890.2019.1651168
    22. Griesemer, M., & Linden, U. (2019). Spectral theory of the Fermi polaron. Ann. Henri Poincaré, 20(6), Article 6. https://doi.org/10.1007/s00023-019-00796-1
    23. Gyorfi, L., Henze, N., & Walk, H. (2019). The Limit Distribution Of The Maximum Probability Nearest-Neighbour Ball. Journal of Applied Probability, 56(2), Article 2. https://doi.org/10.1017/jpr.2019.37
    24. Györfi, L., & Walk, H. (2019). Nearest neighbor based conformal prediction. Annales de l’ISUP, 63(2–3), Article 2–3. https://hal.science/hal-03603867
    25. Hahn, B. N., & Kienle Garrido, M.-L. (2019). An efficient reconstruction approach for a class of dynamic imaging operators. Inverse Problems, 35(9), Article 9. https://doi.org/10.1088/1361-6420/ab178b
    26. Hansmann, M., Kohler, M., & Walk, H. (2019). On the strong universal consistency of local averaging regression    estimates (vol 71, pg 1233, 2019). ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, 71(5), Article 5. https://doi.org/10.1007/s10463-018-0687-4
    27. Heil, K., & Jentsch, T. (2019). A special class of symmetric Killing 2-tensors. JOURNAL OF GEOMETRY AND PHYSICS, 138, 103–123. https://doi.org/10.1016/j.geomphys.2018.12.009
    28. Holicki, T., & Scherer, C. W. (2019). A Homotopy Approach for Robust Output-Feedback Synthesis. Proc. 27th. Med. Conf. Control Autom., 87–93. https://doi.org/10.1109/MED.2019.8798536
    29. Holicki, T., & Scherer, C. W. (2019). Stability Analysis and Output-Feedback Synthesis of Hybrid Systems Affected by Piecewise Constant Parameters via Dynamic Resetting Scalings. Nonlinear Anal. Hybri., 34, 179–208. https://doi.org/10.1016/j.nahs.2019.06.003
    30. Homma, Y., & Semmelmann, U. (2019). The Kernel of the Rarita-Schwinger Operator on Riemannian Spin Manifolds. Comm. Math. Phys., 370(3), Article 3. https://doi.org/10.1007/s00220-019-03324-8
    31. Höllig, K., & Hörner, J. (2019). Aufgaben und Lösungen zur Höheren Mathematik. - 1. [Aufgabensammlung]. In Aufgaben und Lösungen zur Höheren Mathematik ; 1 (2. Auflage, Bd. 1, S. x, 235 Seiten). Springer Spektrum.
    32. Kluth, T., Hahn, B. N., & Brandt, C. (2019). Spatio-temporal concentration reconstruction using motion priors in magnetic particle imaging. Proc. Int. Workshop Magnetic Particle Imaging.
    33. Kohr, M., Mikhailov, S. E., & Wendland, W. L. (2019). Newtonian and Single Layer Potentials for the Stokes System with L∞ Coefficients and the Exterior Dirichlet Problem. In S. Rogosin & A. O. Celebi (Hrsg.), Analysis as a Life: Dedicated to Heinrich Begehr on the Occasion of his 80th Birthday (S. 237--260). Springer International Publishing. https://doi.org/10.1007/978-3-030-02650-9_12
    34. Kohr, M., & Wendland, W. L. (2019). 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, 131, Article 131. https://doi.org/10.1016/j.matpur.2019.04.002
    35. Kuhn, T., Dürrwächter, J., Meyer, F., Beck, A., Rohde, C., & Munz, C.-D. (2019). Uncertainty quantification for direct aeroacoustic simulations of cavity flows. J. Theor. Comput. Acoust., 27(1), Article 1. https://doi.org/10.1142/S2591728518500445
    36. Köppel, M., Franzelin, F., Kröker, I., Oladyshkin, S., Santin, G., Wittwar, D., Barth, A., Haasdonk, B., Nowak, W., Pflüger, D., & Rohde, C. (2019). Comparison of data-driven uncertainty quantification methods for  a carbon dioxide storage benchmark scenario. Comput. Geosci., 2(23), Article 23. https://doi.org/10.1007/s10596-018-9785-x
    37. Mazzeo, R., Swoboda, J., Weiss, H., & Witt, F. (2019). Asymptotic geometry of the Hitchin metric. Commun. Math. Phys., 367(1), Article 1. https://doi.org/10.1007/s00220-019-03358-y
    38. Miller, C. T., Gray, W. G., Kees, C. E., Rybak, I. V., & Shepherd, B. J. (2019). Modeling sediment transport in three-phase surface water systems. J. Hydraul. Res., 57. https://doi.org/10.1080/00221686.2019.1581673
    39. Mücke, N., & Steinwart, I. (2019). Empirical Risk Minimization in the Interpolating Regime with Application to Neural Network Learning. Fakultät für Mathematik und Physik, Universität Stuttgart.
    40. Oesting, M., Schlather, M., & Schillings, C. (2019). Sampling sup-normalized spectral functions for Brown-Resnick processes. Stat, 8, e228, 11. https://doi.org/10.1002/sta4.228
    41. Ostrowski, L., & Massa, F. (2019). An incompressible-compressible approach for droplet impact. In G. Cossali & S. Tonini (Hrsg.), Proceedings of the DIPSI Workshop 2019: Droplet ImpactPhenomena & Spray Investigations, Bergamo, Italy, 17th May 2019 (S. 18–21). Università degli studi di Bergamo. https://doi.org/10.6092/DIPSI2019_pp18-21
    42. Rösinger, C. A., & Scherer, C. W. (2019). A Flexible Synthesis Framework of Structured Controllers for Networked Systems. IEEE Trans. Control Netw. Syst., 7(1), Article 1. https://doi.org/10.1109/TCNS.2019.2914411
    43. Rösinger, C. A., & Scherer, C. W. (2019). A Scalings Approach to $H_2$-Gain-Scheduling Synthesis without Elimination. IFAC-PapersOnLine, 52(28), Article 28. https://doi.org/10.1016/j.ifacol.2019.12.347
    44. Santin, G., & Haasdonk, B. (2019). Kernel Methods for Surrogate Modelling. University of Stuttgart.
    45. Santin, G., & Haasdonk, B. (2019). Kernel Methods for Surrogate Modeling (ArXiv No. 1907.10556; Nummer 1907.10556). https://arxiv.org/abs/1907.10556
    46. Santin, G., Wittwar, D., & Haasdonk, B. (2019). Sparse approximation of regularized kernel interpolation by greedy algorithms.
    47. Schanz, M., Wasser, C., Allgaeuer, S., Schricker, S., Dippon, J., Alscher, MD., & Kimmel, M. (2019). Urinary TIMP-2·IGFBP7-guided randomized controlled intervention trial to prevent acute kidney injury in the emergency department. Transplant., 2019 Nov 1;34(11), 1902–1909. https://doi.org/10.1093/ndt/gfy186
    48. Schmidt, A., Wittwar, D., & Haasdonk, B. (2019). Rigorous and effective a-posteriori error bounds for nonlinear problems -- Application to RB methods. Advances in Computational Mathematics. https://doi.org/10.1007/s10444-019-09730-9
    49. Schricker, S., Heider, T., Schanz, M., Dippon, J., Alscher, MD., Weiss, H., Mettang, T., & Kimmel, M. (2019). Strong Associations Between Inflammation, Pruritus and Mental Health in Dialysis Patients. Acta Derm Venereol., 2019 May 1;99(6), 524–529. https://doi.org/10.2340/00015555-3128
    50. Semmelmann, U., & Weingart, G. (2019). The standard Laplace operator. Manuscripta Math., 158(1–2), Article 1–2. https://doi.org/10.1007/s00229-018-1023-2
    51. Seus, D., Radu, F. A., & Rohde, C. (2019). A linear domain decomposition method for two-phase flow in porous media. Numerical Mathematics and Advanced Applications ENUMATH 2017, 603–614. https://doi.org/10.1007/978-3-319-96415-7_55
    52. Sharanya, V., Sekhar, G. P. R., & Rohde, C. (2019). Surfactant-induced migration of a spherical droplet in non-isothermal Stokes flow. Physics of Fluids, 31(1), Article 1. https://doi.org/10.1063/1.5064694
    53. Steinwart, I. (2019). A Sober Look at Neural Network Initializations. Fakultät für Mathematik und Physik, Universität Stuttgart.
    54. Steinwart, I. (2019). Convergence Types and Rates  in Generic Karhunen-Loève Expansions with Applications to Sample Path Properties. Potential Anal., 51, 361--395. https://doi.org/10.1007/s11118-018-9715-5
    55. Wenzel, T., Santin, G., & Haasdonk, B. (2019). A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution.
    56. Wittwar, D., & Haasdonk, B. (2019). Greedy Algorithms for Matrix-Valued Kernels. In F. A. Radu, K. Kumar, I. Berre, J. M. Nordbotten, & I. S. Pop (Hrsg.), Numerical Mathematics and Advanced Applications ENUMATH 2017 (S. 113--121). Springer International Publishing.
    57. Wittwar, D., Santin, G., & Haasdonk, B. (2019). Part II on matrix valued kernels including analysis.
    58. Zhang, R., Kyriss, T., Dippon, J., Boedeker, E., & Friedel, G. (2019). Preoperative serum lactate dehydrogenase level as a predictor of major omplications following thoracoscopic lobectomy: a propensity-adjusted analysis. European Journal of Cardio-Thoracic Surgery, 56(2), Article 2. https://doi.org/10.1093/ejcts/ezz027
    59. Zhang, R., Dippon, J., & Friedel, G. (2019). Refined risk stratification for thoracoscopic lobectomy or segmentectomy. Journal of Thoracic Disease, 11(1), Article 1. https://doi.org/10.21037/jtd.2018.12.44
    60. Zhang R, Dippon J, F. G. (2019). Refined risk stratification for thoracoscopic lobectomy or segmentectomy. Dis., J Thorac, 2019 Jan;11(1), :222-230. https://doi.org/10.21037/jtd.2018.12.44
  8. 2018

    1. Afkham, B. M., Bhatt, A., Haasdonk, B., & Hesthaven, J. S. (2018). Symplectic Model-Reduction with a Weighted Inner Product.
    2. Altenbernd, M., & Göddeke, D. (2018). Soft fault detection and correction for multigrid. The International Journal of High Performance Computing Applications, 32(6), Article 6. https://doi.org/10.1177/1094342016684006
    3. Barth, A., & Kröker, I. (2018). Finite Volume Methods for Hyperbolic Partial Differential Equations with Spatial Noise. In C. Klingenberg & M. Westdickenberg (Hrsg.), Theory, Numerics and Applications of Hyperbolic Problems I (S. 125--135). Springer International Publishing.
    4. Barth, A., & Stein, A. (2018). A Study of Elliptic Partial Differential Equations with Jump Diffusion    Coefficients. SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 6(4), Article 4. https://doi.org/10.1137/17M1148888
    5. Barth, A., & Stein, A. (2018). Approximation and simulation of infinite-dimensional Levy processes. STOCHASTICS AND PARTIAL DIFFERENTIAL EQUATIONS-ANALYSIS AND COMPUTATIONS, 6(2), Article 2. https://doi.org/10.1007/s40072-017-0109-2
    6. Barth, A., & Stüwe, T. (2018). Weak convergence of Galerkin approximations of stochastic partial  differential equations driven by additive Lévy noise. Math. Comput. Simulation, 143, 215--225. https://doi.org/10.1016/j.matcom.2017.03.007
    7. Bhatt, A., Fehr, J., Grunert, D., & Haasdonk, B. (2018). A Posteriori Error Estimation in Model Order Reduction of Elastic Multibody Systems with Large Rigid Motion. In J. Fehr & B. Haasdonk (Hrsg.), IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22–25, 2018. Springer. https://doi.org/DOI:10.1007/978-3-030-21013-7_7
    8. Bhatt, A., & Haasdonk, B. (2018). Certified and structure-preserving model order reduction of EMBS. In RAMSA 2017, New Delhi.
    9. Bhatt, A., Haasdonk, B., & Moore, B. E. (2018). Structure-preserving Integration and Model Order Reduction. In Invited online talk in Department of Mathematics, IIT Roorkee.
    10. Blaschzyk, I., & Steinwart, I. (2018). Improved Classification Rates under Refined Margin Conditions. Electron. J. Stat., 12, 793--823. https://doi.org/10.1214/18-EJS1406
    11. Bradley, C. P., Emamy, N., Ertl, T., Göddeke, D., Hessenthaler, A., Klotz, T., Krämer, A., Krone, M., Maier, B., Mehl, M., Tobias, R., & Röhrle, O. (2018). Enabling Detailed, Biophysics-Based Skeletal Muscle Models on HPC Systems. Frontiers in Physiology, 9(816), Article 816. https://doi.org/10.3389/fphys.2018.00816
    12. Brehler, M., Schirwon, M., Göddeke, D., & Krummrich, P. (2018, Juli). Modeling the Kerr-Nonlinearity in Mode-Division Multiplexing Fiber  Transmission Systems on GPUs. Proceedings of Advanced Photonics 2018.
    13. Brünnette, T., Santin, G., & Haasdonk, B. (2018). Greedy kernel methods for accelerating implicit integrators for parametric ODEs. Proc. ENUMATH 2017.
    14. Buchfink, P. (2018). Structure-preserving Model Reduction for Elasticity [Diploma thesis].
    15. Chalons, C., Magiera, J., Rohde, C., & Wiebe, M. (2018). A finite-volume tracking scheme for two-phase compressible flow. Springer Proc. Math. Stat., 309--322. https://doi.org/10.1007/978-3-319-91545-6_25
    16. De Marchi, S., Iske, A., & Santin, G. (2018). Image reconstruction from scattered Radon data by weighted positive  definite kernel functions. Calcolo, 55(1), Article 1. https://doi.org/10.1007/s10092-018-0247-6
    17. de Rijk, B. (2018). Spectra and stability of spatially periodic pulse patterns              II: the critical spectral curve. SIAM J. Math. Anal., 50(2), Article 2. https://doi.org/10.1137/17M1127594
    18. de Rijk, B., & Sandstede, B. (2018). Diffusive stability against nonlocalized perturbations of              planar wave trains in reaction-diffusion systems. J. Differential Equations, 265(10), Article 10. https://doi.org/10.1016/j.jde.2018.07.011
    19. Degeratu, A., & Mazzeo, R. (2018). Fredholm theory for elliptic operators on quasi-asymptotically conical spaces. Proc. Lond. Math. Soc. (3), 116(5), Article 5. https://doi.org/10.1112/plms.12105
    20. Devroye, L., Gyorfi, L., Lugosi, G., & Walk, H. (2018). A nearest neighbor estimate of the residual variance. ELECTRONIC JOURNAL OF STATISTICS, 12(1), Article 1. https://doi.org/10.1214/18-EJS1438
    21. Dibak, C., Haasdonk, B., Schmidt, A., Dürr, F., & Rothermel, K. (2018). Enabling interactive mobile simulations through distributed reduced models. Pervasive and Mobile Computing, Elsevier BV, 45, 19--34. https://doi.org/10.1016/j.pmcj.2018.02.002
    22. Doelman, A., Rademacher, J., de Rijk, B., & Veerman, F. (2018). Destabilization Mechanisms of Periodic Pulse Patterns Near a Homoclinic Limit. SIAM J. Appl. Dyn. Syst., 17(2), Article 2. https://doi.org/10.1137/17M1122840
    23. Doering, M., Gyorfi, L., & Walk, H. (2018). Rate of Convergence of k-Nearest-Neighbor Classification Rule. JOURNAL OF MACHINE LEARNING RESEARCH, 18.
    24. Düll, W.-P. (2018). On the mathematical description of time-dependent surface water waves. Jahresber. Dtsch. Math.-Ver., 120(2), Article 2. https://doi.org/10.1365/s13291-017-0173-6
    25. Düll, W.-P., & Heß, M. (2018). Existence of long time solutions and validity of the nonlinear Schrödinger approximation for a quasilinear dispersive equation. J. Differential Equations, 264(4), Article 4. https://doi.org/10.1016/j.jde.2017.10.031
    26. Düll, W.-P., Hilder, B., & Schneider, G. (2018). Analysis of the embedded cell method in 1D for the numerical homogenization of metal-ceramic composite materials. J. Appl. Anal., 24(1), Article 1.
    27. Düll, W.-P., Hilder, B., & Schneider, G. (2018). Analysis of the embedded cell method in 1D for the numerical homogenization of metal-ceramic composite materials. J. Appl. Anal., 24(1), Article 1. https://doi.org/10.1515/jaa-2018-0007
    28. Dürrwächter, J., Kuhn, T., Meyer, F., Schlachter, L., & Schneider, F. (2018). A hyperbolicity-preserving discontinuous stochastic Galerkin scheme  for uncertain hyperbolic systems of equations. Journal of Computational and Applied Mathematics, 112602. https://doi.org/10.1016/j.cam.2019.112602
    29. Engwer, C., Altenbernd, M., Dreier, N.-A., & Göddeke, D. (2018, März). A high-level C++ approach to manage local errors, asynchrony and  faults in an MPI application. Proceedings of the 26th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2018).
    30. Escher, J., & Lienstromberg, C. (2018). Travelling waves in dilatant non-Newtonian thin films. J. Differential Equations, 264(3), Article 3. https://doi.org/10.1016/j.jde.2017.10.015
    31. Fechter, S., Munz, C.-D., Rohde, C., & Zeiler, C. (2018). Approximate Riemann solver for compressible liquid vapor flow with  phase transition and surface tension. Comput. & Fluids, 169, 169–185. http://dx.doi.org/10.1016/j.compfluid.2017.03.026
    32. Fehr, J., Grunert, D., Bhatt, A., & Haasdonk, B. (2018). A Sensitivity Study of Error Estimation in Reduced Elastic Multibody Systems. Proceedings of MATHMOD 2018, Vienna, Austria.
    33. Fritz, P., Dippon, J., Müller, S., Goletz, S., Trautmann, C., Pappas, X., Ott, G., Brauch, H., Schwab, M., Winter, S., Mürdter, T., Brinkmann, F., Faisst, S., Rössle, S., Gerteis, A., & Friedel, G. (2018). Is Mistletoe Treatment Beneficial in Invasive Breast Cancer? A New Approach to an Unresolved Problem. Anticancer research, 38(3), Article 3. https://doi.org/10.21873/anticanres.12388
    34. Fritzen, F., Haasdonk, B., Ryckelynck, D., & Schöps, S. (2018). An algorithmic comparison of the Hyper-Reduction and the Discrete  Empirical Interpolation Method for a nonlinear thermal problem. Math. Comput. Appl. 2018, 23(1), Article 1. https://doi.org/doi:10.3390/mca23010008
    35. Geck, M. (2018). A first guide to the character theory of finite groups of Lie type. Local Representation Theory and Simple Groups (eds. R. Kessar, G. Malle, D. Testerman), 63--106. https://doi.org/10.4171/185-1/3
    36. Geck, M. (2018). On the values of unipotent characters in bad characteristic. Rendiconti del Seminario Matematico della Università di Padova, 141, 37--63. https://doi.org/10.4171/rsmup/14
    37. Georgiev, V., & Wirth, J. (2018). Zero resonances for localised potentials. Journal of Mathematical Physics, 59(7), Article 7. https://doi.org/10.1063/1.5027717
    38. Giesselmann, J., Kolbe, N., Lukacova-Medvidova, M., & Sfakianakis, N. (2018). Existence and uniqueness of global classical solutions to a two species  cancer invasion haptotaxis model. Accepted for publication in Discrete Contin. Dyn. Syst. Ser. B. https://arxiv.org/abs/1704.08208
    39. Gimperlein, H., Meyer, F., Özdemir, C., Stark, D., & Stephan, E. P. (2018). Boundary elements with mesh refinements for the wave equation. Numer. Math., 139(4), Article 4. https://doi.org/10.1007/s00211-018-0954-6
    40. Gimperlein, H., Meyer, F., Özdemir, C., & Stephan, E. P. (2018). Time domain boundary elements for dynamic contact problems. Computer Methods in Applied Mechanics and Engineering, 333, 147–175. https://doi.org/10.1016/j.cma.2018.01.025
    41. Griesemer, M., & Wünsch, A. (2018). On the domain of the Nelson Hamiltonian. J. Math. Phys., 59(4), Article 4. https://doi.org/10.1063/1.5018579
    42. Griesemer, M., & Linden, U. (2018). Stability of the two-dimensional Fermi polaron. Lett. Math. Phys., 108(8), Article 8. https://doi.org/10.1007/s11005-018-1055-2
    43. Guo, Y., & Scherer, C. W. (2018). Robust Gain-Scheduled Controller Design with a Hierarchical Structure. IFAC-PapersOnline, 51(25), Article 25. https://doi.org/10.1016/j.ifacol.2018.11.110
    44. Haasdonk, B., Hamzi, B., Santin, G., & Wittwar, D. (2018). Greedy Kernel Methods for Center Manifold Approximation (ArXiv No. 1810.11329; Nummer 1810.11329).
    45. Haasdonk, B., & Santin, G. (2018). Greedy Kernel Approximation for Sparse Surrogate Modeling. In W. Keiper, A. Milde, & S. Volkwein (Hrsg.), Reduced-Order Modeling (ROM) for Simulation and Optimization: Powerful Algorithms as Key Enablers for Scientific Computing (S. 21--45). Springer International Publishing. https://doi.org/10.1007/978-3-319-75319-5_2
    46. Haesaert, S., Weiland, S., & Scherer, C. W. (2018). A separation theorem for guaranteed $H_2$ performance through matrix inequalities. Automatica, 96, 306–313. https://doi.org/10.1016/j.automatica.2018.07.002
    47. Hang, H., Steinwart, I., Feng, Y., & Suykens, J. A. K. (2018). Kernel Density Estimation for Dynamical Systems. J. Mach. Learn. Res., 19, 1--49.
    48. Harbrecht, H., Wendland, W. L., & Zorii, N. (2018). Minimal energy problems for strongly singular Riesz kernels. Math. Nachr., 291, Article 291. https://doi.org/10.1002/mana.201600024
    49. Holicki, T., & Scherer, C. W. (2018). A Swapping Lemma for Switched Systems. IFAC-PapersOnLine, 51(25), Article 25. https://doi.org/10.1016/j.ifacol.2018.11.131
    50. Holicki, T., & Scherer, C. W. (2018). Output-Feedback Gain-Scheduling Synthesis for a Class of Switched Systems via Dynamic Resetting $D$-Scalings. 57th IEEE Conf. Decision and Control, 6440–6445. https://doi.org/10.1109/CDC.2018.8619128
    51. Hsiao, G. C., Steinbach, O., & Wendland, W. L. (2018). Boundary Element Methods: Foundation and Error Analysis. Encyclopedia of Computational Mechanics Second Edition, 62. https://doi.org/10.1002/9781119176817.ecm2007
    52. Kohler, M., Krzyzak, A., Tent, R., & Walk, H. (2018). Nonparametric quantile estimation using importance sampling. ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, 70(2), Article 2. https://doi.org/10.1007/s10463-016-0595-4
    53. Kohr, M., & Wendland, W. L. (2018). Variational approach for the Stokes and Navier-Stokes systems with nonsmooth coefficients in Lipschitz domains on compact Riemannian manifolds. Calc. Var. Partial Differential Equations, 57(6), Article 6. https://doi.org/10.1007/s00526-018-1426-7
    54. Kohr, M., & Wendland, W. L. (2018). Layer Potentials and Poisson Problems for the Nonsmooth Coefficient Brinkman System in Sobolev and Besov Spaces. Journal of Mathematical Fluid Mechanics, 4(20), Article 20. https://doi.org/10.1007/s00021-018-0394-1
    55. Kovar\’ık, H., Ruszkowski, B., & Weidl, T. (2018). Melas-type bounds for the Heisenberg Laplacian on bounded domains. Journal of Spectral Theory, 8(2), Article 2. https://doi.org/10.4171/jst/200
    56. Kraemer, B., Scharpf, M., Keckstein, S., Dippon, J., Tsaousidis, C., Brunecker, K., Enderle, MD., Neugebauer, A., Nuessle, D., Fend, F., Brucker, S., Taran, FA., Kommoss, S., & Rothmund, R. (2018). A prospective randomized experimental study to investigate the peritoneal adhesion formation after waterjet injection and argon plasma coagulation (HybridAPC) in a rat model. Arch Gynecol Obstet., 2018, Apr;297(4), 961–967. https://doi.org/10.1007/s00404-018-4661-4
    57. Köppel, M., Martin, V., Jaffré, J., & Roberts, J. E. (2018). A Lagrange multiplier method for a discrete fracture model for flow  in porous media. (submitted). https://hal.archives-ouvertes.fr/hal-01700663v2
    58. Köppel, M., Martin, V., & Roberts, J. E. (2018). A stabilized Lagrange multiplier finite-element method for flow in  porous media with fractures. (submitted). https://hal.archives-ouvertes.fr/hal-01761591
    59. Köppl, T., Santin, G., Haasdonk, B., & Helmig, R. (2018). Numerical modelling of a peripheral arterial stenosis using dimensionally  reduced models and kernel methods. International Journal for Numerical Methods in Biomedical Engineering, 0(ja), Article ja. https://doi.org/10.1002/cnm.3095
    60. Langer, A. (2018). Overlapping domain decomposition methods for total variation denoising. http://people.ricam.oeaw.ac.at/a.langer/publications/DDfTV.pdf
    61. Langer, A. (2018). Locally adaptive total variation for removing mixed Gaussian-impulse  noise. International Journal of Computer Mathematics, 19. https://www.tandfonline.com/doi/abs/10.1080/00207160.2018.1438603
    62. Langer, A. (2018). Investigating the influence of box-constraints on the solution of  a total variation model via an efficient primal-dual method. Journal of Imaging, 4, 1. http://www.mdpi.com/2313-433X/4/1/12
    63. Maboudi Afkham, B., & Hesthaven, J. S. (2018). Structure-Preserving Model-Reduction of Dissipative Hamiltonian Systems. Journal of Scientific Computing, 1–19. https://doi.org/10.1007/s10915-018-0653-6
    64. Magiera, J., & Rohde, C. (2018). A particle-based multiscale solver for compressible liquid-vapor flow. Springer Proc. Math. Stat., 291--304. https://doi.org/10.1007/978-3-319-91548-7_23
    65. Oesting, M. (2018). Equivalent representations of max-stable processes via $\ell^p$-norms. J. Appl. Probab., 55(1), Article 1. https://doi.org/10.1017/jpr.2018.5
    66. Oesting, M., Bel, L., & Lantuéjoul, C. (2018). Sampling from a max-stable process conditional on a homogeneous functional with an application for downscaling climate data. Scand. J. Stat., 45(2), Article 2. https://doi.org/10.1111/sjos.12299
    67. Oesting, M., Schlather, M., & Zhou, C. (2018). Exact and fast simulation of max-stable processes on a compact set using the normalized spectral representation. Bernoulli, 24(2), Article 2. https://doi.org/10.3150/16-BEJ905
    68. Oesting, M., & Stein, A. (2018). Spatial modeling of drought events using max-stable processes. Stoch. Env. Res. Risk A., 32(1), Article 1. https://doi.org/10.1007/s00477-017-1406-z
    69. Oesting, M., & Strokorb, K. (2018). Efficient simulation of Brown-Resnick processes based on variance reduction of Gaussian processes. Adv. in Appl. Probab., 50(4), Article 4. https://doi.org/10.1017/apr.2018.54
    70. Raja Sekhar, G. P., Sharanya, V., & Rohde, C. (2018). Effect of surfactant concentration and interfacial slip on the flow  past a viscous drop at low surface Péclet number. International Journal of Multiphase Flow, 107, 82–103. http://arxiv.org/abs/1609.03410
    71. Rigaud, G., & Hahn, B. N. (2018). 3D Compton scattering imaging and contour reconstruction for a class of Radon transforms. Inverse Problems, 34(7), Article 7. https://doi.org/10.1088/1361-6420/aabf0b
    72. Rohde, C., & Zeiler, C. (2018). On Riemann solvers and kinetic relations for isothermal two-phase  flows with surface tension. Z. Angew. Math. Phys., 3, Article 3. https://doi.org/10.1007/s00033-018-0958-1
    73. Rohde, C. (2018). Fully resolved compressible two-phase flow : modelling, analytical and numerical issues. In M. Bulicek, E. Feireisl, & M. Pokorný (Hrsg.), New trends and results in mathematical description of fluid flows (S. 115–181). Birkhäuser. https://doi.org/10.1007/978-3-319-94343-5
    74. Ruiz, P. A., Freiberg, U. R., & Kigami, J. (2018). Completely symmetric resistance forms on the stretched Sierpinski gasket. JOURNAL OF FRACTAL GEOMETRY, 5(3), Article 3. https://doi.org/10.4171/JFG/61