Publications of the Department of Mathematics

Department of Mathematics

List of publications of the Department of Mathematics starting 2017

 

The following overview gives a first impression of the diverse publications of the researchers of the department exemplarily for the period from 2017, not only in peer-reviewed journals. A more detailed, complete and topic-specific impression is given by the pages of the individual institutes, research groups and coordinated programs

  1. 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), 10943420211055188. https://doi.org/10.1177/10943420211055188
    2. Beschle, C. A., & 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
    3. Buchfinck, P., Glas, S., & Haasdonk, B. (2022). Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems.
    4. 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
    5. Burbulla, S., Dedner, A., Hörl, M., & Rohde, C. (2022). Dune-MMesh: The Dune Grid Module for Moving Interfaces. J. Open Source Softw., 7(74), 3959. https://doi.org/10.21105/joss.03959
    6. 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
    7. Gander, M., Lunowa, S., & Rohde, C. (2022). Non-overlapping Schwarz Waveform-Relaxation for Nonlinear Advection-Diffusion Equations. SIAM J. Sci. Comput. http://www.uhasselt.be/Documents/CMAT/Preprints/2021/UP2103.pdf
    8. 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 (Eds.), Large-Scale Scientific Computing (pp. 378--386). Springer International Publishing.
    9. 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
    10. 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), 0. https://doi.org/10.3934/ipi.2022018
    11. Keim, J., Munz, C.-D., & Rohde, C. (2022). A Relaxation Model for the Non-Isothermal Navier-Stokes-Korteweg Equations in onfined Domains. In arXiv e-prints. https://doi.org/0.48550/ARXIV.2208.05310
    12. Kröker, I., Oladyshkin, S., & Rybak, I. (2022). Global sensitivity analysis using multi-resolution polynomial chaos expansion for coupled Stokes-Darcy flow problems. Comput. Geosci. (Submitted).
    13. 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
    14. Magiera, J., & Rohde, C. (2022). Analysis and Numerics of Sharp and Diffuse Interface Models for Droplet Dynamics (K. Schulte, C. Tropea, & B. Weigand, Eds.; pp. 67–86). Springer International Publishing. https://doi.org/10.1007/978-3-031-09008-0_4
    15. Melnyk, T., & Rohde, C. (2022). Asymptotic expansion for convection-dominated transport in a thin graph-like junction. In arXiv e-prints. https://doi.org/10.48550/ARXIV.2208.05812
    16. 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
    17. 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
    18. Merkle, R., & Barth, A. (2022). On some distributional properties of subordinated Gaussian random fields. Methodol Comput Appl Probab.
    19. Miller, C. T., Gray, W. G., Kees, C. E., Rybak, I., & Shepherd, B. (2022). Correction to: Modeling Sediment Transport in Three-Phase Surface Water Systems. J. Hydraul. Res. (Submitted).
    20. 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
    21. Santin, G., Karvonen, T., & Haasdonk, B. (2022). Sampling based approximation of linear functionals in reproducing kernel Hilbert spaces. BIT - Numerical Mathematics, 62(1), 279–310. https://doi.org/10.1007/s10543-021-00870-3
    22. Seus, D., Radu, F. A., & Rohde, C. (2022). Towards hybrid two-phase modelling using linear domain decomposition. Numerical Methods for Partial Differential Equations, n/a(n/a), Article n/a. https://doi.org/10.1002/num.22906
    23. 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 (Eds.), Large-Scale Scientific Computing (pp. 402--409). Springer International Publishing.
    24. 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
    25. 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 (Eds.), Large-Scale Scientific Computing (pp. 410--418). Springer International Publishing.
    26. 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), 114103. https://doi.org/10.1063/5.0078983
    27. 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
  2. 2021

    1. Alkämper, M., Magiera, J., & Rohde, C. (2021). An Interface Preserving Moving Mesh in Multiple SpaceDimensions. Computing Research Repository, abs/2112.11956. https://arxiv.org/abs/2112.11956
    2. 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 (Eds.), High Performance Computing in Science and Engineering -- HPCSE 2019 (Vol. 12456, pp. 17--38). Springer. https://doi.org/10.1007/978-3-030-67077-1_2
    3. Altmann, K., & Witt, F. (2021). Toric co-Higgs sheaves. Journal of Pure and Applied Algebra, 225(8), 106634. https://doi.org/10.1016/j.jpaa.2020.106634
    4. 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.
    5. 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 (Eds.), High Performance Computing in Science and Engineering ’19 (pp. 355--371). Springer International Publishing.
    6. 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 (Online First). https://doi.org/10.1177/1094342021990433
    7. Berrett, T. B., Gyorfi, L., & Walk, H. (2021). Strongly universally consistent nonparametric regression and    classification with privatised data. ELECTRONIC JOURNAL OF STATISTICS, 15(1), 2430–2453. https://doi.org/10.1214/21-EJS1845
    8. Brencher, L., & Barth, A. (2021). Scalar conservation laws with stochastic discontinuous flux function. ArXiv E-Prints, ArXiv:2107.00549 Math.NA.
    9. Brencher, L., & Barth, A. (2021). Stochastic conservation laws with discontinuous flux functions: The multidimensional case.
    10. Buchfink, P., Glas, S., & Haasdonk, B. (2021). Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds. https://doi.org/10.48550/arXiv.2112.10815
    11. 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 (Eds.), Numerical Mathematics and Advanced Applications ENUMATH 2019 (Vol. 139). Springer International Publishing. https://doi.org/10.1007/978-3-030-55874-1
    12. 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
    13. 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 Differential Equations Appl., 28(1), Paper No. 2, 38. https://doi.org/10.1007/s00030-020-00665-5
    14. 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
    15. 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), 831--914. https://doi.org/10.1007/s00205-020-01586-4
    16. 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
    17. Eggenweiler, E., Discacciati, M., & Rybak, I. (2021). Analysis of the Stokes-Darcy problem with generalised interface conditions. ESAIM Math. Model. Numer. Anal. (Submitted). https://arxiv.org/abs/2104.02339
    18. 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
    19. Ehring, T., & Haasdonk, B. (2021). Greedy sampling and approximation for realizing feedback control for high dimensional nonlinear systems.
    20. 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), 7439–7447. https://ojs.aaai.org/index.php/AAAI/article/view/16912
    21. Frank, R. L., Laptev, A., & Weidl, T. (2021). Lieb-Thirring Inequalities.
    22. 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
    23. Gander, M., Lunowa, S., & Rohde, C. (2021). Non-overlapping Schwarz Waveform-Relaxation for Nonlinear Advection-Diffusion Equations. http://www.uhasselt.be/Documents/CMAT/Preprints/2021/UP2103.pdf
    24. 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
    25. Geck, M. (2021). Generalised Gelfand-Graev representations in bad characteristic? Transformation Groups, 26(1), 305--326. https://doi.org/10.1007/s00031-020-09575-3
    26. Giesselmann, J., Meyer, F., & Rohde, C. (2021). Error control for statistical solutions of hyperbolic systems              of conservation laws. Calcolo, 58(2), Paper No. 23, 29. https://doi.org/10.1007/s10092-021-00417-6
    27. 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 (Eds.), Anomalies in Partial Differential Equations (Vol. 43). Springer. https://doi.org/10.1007/978-3-030-61346-4_14
    28. 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
    29. Haasdonk, B. (2021). Model Order Reduction, Applications, MOR Software (D. Gruyter, Ed.; Vol. 3). De Gruyter. https://doi.org/10.1515/9783110499001
    30. Haasdonk, B., Ohlberger, M., & Schindler, F. (2021). An adaptive model hierarchy for data-augmented training of kernel models for reactive flow.
    31. Haasdonk, B., Wenzel, T., Santin, G., & Schmitt, S. (2021). Biomechanical Surrogate Modelling Using Stabilized Vectorial Greedy Kernel Methods.
    32. 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
    33. Hahn, B. N. (2021). Motion compensation strategies in tomography. https://doi.org/10.1007/978-3-030-57784-1_3
    34. Hamm, T., & Steinwart, I. (2021). Adaptive Learning Rates for Support Vector Machines Working on Data with Low Intrinsic Dimension. Ann. Statist., 49, 3153--3180. https://doi.org/10.1214/21-AOS2078
    35. Hamm, T., & Steinwart, I. (2021). Intrinsic Dimension Adaptive Partitioning for Kernel Methods. Fakultät für Mathematik und Physik, Universität Stuttgart.
    36. Hang, H., & Steinwart, I. (2021). Optimal Learning with Anisotropic Gaussian SVMs. Appl. Comput. Harmon. Anal., 55, 337–367. https://doi.org/10.1016/j.acha.2021.06.004
    37. Hilder, B. (2021). Nonlinear stability of fast invading fronts in a Ginzburg–Landau equation with an additional conservation law. Nonlinearity, 34(8), 5538--5575. https://doi.org/10.1088/1361-6544/abd612
    38. 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
    39. Holicki, T., Scherer, C. W., & Trimpe, S. (2021). Controller Design via Experimental Exploration with Robustness Guarantees. IEEE Control Syst. Lett., 5(2), 641–646. https://doi.org/10.1109/LCSYS.2020.3004506
    40. Holicki, T., & Scherer, C. W. (2021). Robust Gain-Scheduled Estimation with Dynamic D-Scalings. IEEE Trans. Autom. Control, 66(11), 5592–5598. https://doi.org/10.1109/TAC.2021.3052751
    41. 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
    42. Hsiao, G. C., & Wendland, W. L. (2021). Boundary integral equations. In Applied Mathematical Sciences (Vol. 164, p. xx+783). Springer, Cham. https://doi.org/10.1007/978-3-030-71127-6
    43. Aufgaben und Lösungen zur Höheren Mathematik 1. (2021). In K. V. Höllig & J. V. Hörner (Eds.), Springer eBook Collection (3rd ed. 2021.). https://doi.org/10.1007/978-3-662-63181-2
    44. Jentsch, T., & Weingart, G. (2021). Jacobi relations on naturally reductive spaces. ANNALS OF GLOBAL ANALYSIS AND GEOMETRY, 59(1), 109–156. https://doi.org/10.1007/s10455-020-09740-7
    45. Kohr, M., Mikhailov, S. E., & Wendland, W. L. (2021). Dirichlet and transmission problems for anisotropic Stokes              and Navier-Stokes systems with $L_ınfty$ tensor              coefficient under relaxed ellipticity condition. Discrete Contin. Dyn. Syst., 41(9), 4421--4460. https://doi.org/10.3934/dcds.2021042
    46. 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), 9641--9674. https://doi.org/10.1002/mma.7167
    47. 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
    48. 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 (Eds.), 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
    49. Krämer, A., Maier, B., Rau, T., Huber, F., Klotz, T., Ertl, T., Göddeke, D., Mehl, M., Reina, G., & Röhrle, O. (2021). High Performance Computing in Science and Engineering 20 (W. E. Nagel, D. H. Kröner, & M. M. Resch, Eds.). Springer. https://doi.org/10.1007/978-3-030-80602-6_13
    50. Kühnert, J., Göddeke, D., & Herschel, M. (2021, July). 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
    51. 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
    52. Leiteritz, R., Buchfink, P., Haasdonk, B., & Pflüger, D. (2021). Surrogate-data-enriched Physics-Aware Neural Networks.
    53. 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
    54. Magiera, J., & Rohde, C. (2021). Analysis and Numerics of Sharp and Diffuse Interface Models for Droplet Dynamics. In K. Schulte, C. Tropea, & B. Weigand (Eds.), Droplet Dynamics under Extreme Ambient Conditions. Springer.
    55. 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
    56. Makogin, V., Oesting, M., Rapp, A., & Spodarev, E. (2021). Long range dependence for stable random processes. J. Time Series Anal., 42(2), 161--185. https://doi.org/10.1111/jtsa.12560
    57. Massa, F., Ostrowski, L., Bassi, F., & Rohde, C. (2021). 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
    58. 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 (Eds.), Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 87--102). Springer International Publishing. https://doi.org/10.1007/978-3-030-86523-8_6
    59. 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
    60. Rohde, C., & von Wolff, L. (2021). A Ternary Cahn-Hilliard-Navier-Stokes model for two phase flow with precipitation and dissolution. Math. Models Methods Appl. Sci., 31(1), 1--35. https://doi.org/10.1142/S0218202521500019
    61. 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), Paper No. 5, 34. https://doi.org/10.1007/s00030-020-00661-9
    62. Rörich, A., Werthmann, T. A., Göddeke, D., & Grasedyck, L. (2021). Bayesian inversion for electromyography using low-rank tensor formats. Inverse Problems, 37(5), 055003. https://doi.org/10.1088/1361-6420/abd85a
    63. Rörich, A., Werthmann, T. A., Göddeke, D., & Grasedyck, L. (2021). Bayesian inversion for electromyography using low-rank tensor formats. Inverse Problems, 37(5), 055003. https://doi.org/10.1088/1361-6420/abd85a
    64. 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 (Eds.), Model Order Reduction: Vol. 1: System-and Data-Driven Methods and Algorithms (pp. 311–354). de Gruyter.
    65. 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
    66. 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
    67. 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
    68. 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
    69. 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
    70. Strohbeck, P., Eggenweiler, E., & Rybak, I. (2021). Determination of the Beavers-Joseph slip coefficient for coupled Stokes/Darcy problems. Adv. Water Res. (Submitted). https://arxiv.org/abs/2106.15556
    71. 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), 69--74. https://doi.org/10.1016/j.ifacol.2021.08.583
    72. 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. Transp. Porous Media, 137(2), 327--343. https://doi.org/10.1007/s11242-021-01560-y
    73. 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
    74. Wenzel, T., Santin, G., & Haasdonk, B. (2021). Universality and Optimality of Structured Deep Kernel Networks. arXiv. https://doi.org/10.48550/ARXIV.2105.07228
    75. 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
    76. Wenzel, T., Santin, G., & Haasdonk, B. (2021). A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability and uniform point distribution. Journal of Approximation Theory, 262, 105508. https://doi.org/10.1016/j.jat.2020.105508
    77. Wittwar, D., & Haasdonk, B. (n.d.). Convergence rates for matrix P-greedy variants. In Numerical mathematics and advanced applications---ENUMATH              2019 (Vol. 139, pp. 1195--1203). Springer, Cham. https://doi.org/10.1007/978-3-030-55874-1\_119
  3. 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), Paper No. 9, 28. https://doi.org/10.1007/s10444-020-09744-8
    2. 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
    3. Barreau, M., Scherer, C. W., Gouaisbaut, F., & Seuret, A. (2020). Integral Quadratic Constraints on Linear Infinite-dimensional Systems for Robust Stability Analysis. IFAC-PapersOnline, 53(2), 7752–7757. https://www.sciencedirect.com/science/article/pii/S2405896320321297
    4. Barth, A., & Merkle, R. (2020). Subordinated Gaussian Random Fields in Elliptic Partial Differential Equations. ArXiv E-Prints, ArXiv:2011.09311 Math.NA.
    5. Barth, A., & Merkle, R. (2020). Subordinated Gaussian Random Fields. ArXiv E-Prints, ArXiv:2012.06353 Math.PR.
    6. 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 (Eds.), Software for Exascale Computing -- SPPEXA 2016--2019 (pp. 225--269). Springer. https://doi.org/10.1007/978-3-030-47956-5_9
    7. Baumstark, S., Schneider, G., Schratz, K., & Zimmermann, D. (2020). Effective Slow Dynamics Models for a Class of Dispersive Systems. Journal of Dynamics and Differential Equations, 32(4), 1867--1899. https://doi.org/10.1007/s10884-019-09791-w
    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 (pp. 37--48). Cham: Birkhäuser.
    9. 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), B1067–B1091. https://doi.org/10.1137/18M1210575
    10. 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
    11. 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.
    12. 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
    13. 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), 124001. https://doi.org/10.1088/1361-6420/abb5e1
    14. Brencher, L., & Barth, A. (2020). Hyperbolic Conservation Laws with Stochastic Discontinuous Flux Functions. International Conference on Finite Volumes for Complex Applications, 265--273.
    15. 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. (pp. 51–97). Birkhäuser. https://doi.org/10.1007/978-3-030-58215-9_3
    16. 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 (Eds.), Proceedings of the Conference Algoritmy 2020 (pp. 151--160). Vydavateľstvo SPEKTRUM. http://www.iam.fmph.uniba.sk/amuc/ojs/index.php/algoritmy/article/view/1577/829
    17. 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), 3392--3448. https://doi.org/10.1016/j.jde.2019.09.056
    18. Díaz-Ramos, J. C., Domínguez-Vázquez, M., & Kollross, A. (2020). On homogeneous manifolds whose isotropy actions are polar. Manuscripta Mathematica, 161(1), 15--34. https://doi.org/10.1007/s00229-018-1077-1
    19. 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 (Eds.), Finite Volumes for Complex Applications IX - Methods, Theoretical Aspects, Examples (Vol. 323, pp. 345–353). Springer International Publishing. https://doi.org/10.1007/978-3-030-43651-3_31
    20. IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart,  Germany, May 22-25, 2018: MORCOS 2018. (2020). In J. Fehr & B. Haasdonk (Eds.), IUTAM Bookseries. Springer.
    21. Fischer, S., & Steinwart, I. (2020). Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm. J. Mach. Learn. Res., 205, 1--38.
    22. Fischer, S. (2020). Some new bounds on the entropy numbers of diagonal operators. J. Approx. Theory, 251, 105343. https://doi.org/10.1016/j.jat.2019.105343
    23. Geck, M. (2020). Green functions and Glauberman degree-divisibility. Annals of Mathematics, 192(1), 229–249. https://doi.org/10.4007/annals.2020.192.1.4
    24. Geck, M. (2020). On Jacob’s construction of the rational canonical form of a matrix. The Electronic Journal of Linear Algebra, 36(36), 177--182. https://doi.org/10.13001/ela.2020.5055
    25. Geck, M., & Malle, G. (2020). The character theory of finite groups of Lie type. A guided tour. In Cambridge Studies in Advanced Mathematics (Vol. 187, p. ix+394). Cambridge University Press. https://doi.org/10.1017/9781108779081
    26. Geck, M. (2020). ChevLie: Constructing Lie algebras and Chevalley groups. Journal of Software for Algebra and Geometry, 10(1), 41--49. https://doi.org/10.2140/jsag.2020.10.41
    27. 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
    28. Advances in Harmonic Analysis and Partial Differential Equations. (2020). In V. Georgiev, T. Ozawa, M. Ruzhansky, & J. Wirth (Eds.), Trends in Mathematics. Birkhäuser. https://doi.org/10.1007/978-3-030-58215-9
    29. Giesselmann, J., Meyer, F., & Rohde, C. (2020). A posteriori error analysis and adaptive non-intrusive numerical schemes for systems of random conservation laws. BIT Numerical Mathematics, 60(3), 619–649. https://doi.org/10.1007/s10543-019-00794-z
    30. 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.
    31. Giraud, L., Rüde, U., & Stals, L. (2020). Resiliency in Numerical Algorithm Design for Extreme Scale Simulations (Dagstuhl Seminar 20101). Dagstuhl Reports, 10(3), 1--57. https://doi.org/10.4230/DagRep.10.3.1
    32. Griesemer, M., Hofacker, M., & Linden, U. (2020). From short-range to contact interactions in the 1d Bose gas. Math. Phys. Anal. Geom., 23(2), Paper No. 19, 28. https://doi.org/10.1007/s11040-020-09344-4
    33. 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), e201900186. https://doi.org/10.1002/zamm.201900186
    34. Haas, T., de Rijk, B., & Schneider, G. (2020). MODULATION EQUATIONS NEAR THE ECKHAUS BOUNDARY: THE KdV EQUATION. SIAM JOURNAL ON MATHEMATICAL ANALYSIS, 52(6), 5389–5421. https://doi.org/10.1137/19M1266873
    35. 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
    36. 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 (Vol. 134, pp. 95--106). Springer, Cham. https://doi.org/10.1007/978-3-030-39647-3\_6
    37. 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), 4353--4380. https://doi.org/10.1016/j.jde.2020.03.033
    38. Hilder, B., Peletier, M. A., Sharma, U., & Tse, O. (2020). An inequality connecting entropy distance, Fisher Information and large deviations. Stochastic Processes and Their Applications, 130(5), 2596--2638. https://doi.org/10.1016/j.spa.2019.07.012
    39. Holicki, T., & Scherer, C. W. (2020). Output-Feedback Synthesis for a Class of Aperiodic Impulsive Systems. IFAC-PapersOnline, 53(2), 7299–7304. https://doi.org/10.1016/j.ifacol.2020.12.981
    40. Holzmüller, D., & Steinwart, I. (2020). Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent. Fakultät für Mathematik und Physik, Universität Stuttgart.
    41. Jentsch, T., & Weingart, G. (2020). RIEMANNIAN AND KAHLERIAN NORMAL COORDINATES. ASIAN JOURNAL OF MATHEMATICS, 24(3), 369–416.
    42. 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), 133–161.
    43. 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
    44. 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), 109–140. https://doi.org/10.1080/17476933.2019.1631293
    45. Kollross, A. (2020). Octonions, triality, the exceptional Lie algebra F4 and polar actions on the Cayley hyperbolic plane. International Journal of Mathematics, 31(07), 2050051. https://doi.org/10.1142/s0129167x20500512
    46. Maier, D. (2020). BREATHER SOLUTIONS ON DISCRETE NECKLACE GRAPHS. OPERATORS AND MATRICES, 14(3), 767–776. https://doi.org/10.7153/oam-2020-14-48
    47. Maier, D. (2020). Construction of breather solutions for nonlinear Klein-Gordon equations    on periodic metric graphs. JOURNAL OF DIFFERENTIAL EQUATIONS, 268(6), 2491–2509. https://doi.org/10.1016/j.jde.2019.09.035
    48. 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
    49. Minorics, L. A. (2020). Spectral asymptotics for Krein-Feller operators with respect to V-variable Cantor measures. Forum Mathematicum, 32(1), 121–138. https://doi.org/10.1515/forum-2018-0188
    50. 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
    51. Oesting, M., & Schnurr, A. (2020). Ordinal patterns in clusters of subsequent extremes of regularly varying time series. Extremes, 23(4), 521--545. https://doi.org/10.1007/s10687-020-00391-2
    52. 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
    53. Pelinovsky, D. E., & Schneider, G. (2020). The monoatomic FPU system as a limit of a diatomic FPU system. Appl. Math. Lett., 107, 7.
    54. 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 (Eds.), Numerical Computations: Theory and Algorithms (pp. 446--453). Springer International Publishing. https://doi.org/10.1007/978-3-030-40616-5_42
    55. Rigaud, G., & Hahn, B. N. (2020). Reconstruction algorithm for 3D Compton scattering imaging with incomplete data. Inverse Problems in Science and Engineering, 29(7), 967--989. https://doi.org/10.1080/17415977.2020.1815723
    56. 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
    57. Rösinger, C. A., & Scherer, C. W. (2020). Lifting to Passivity for $H_2$-Gain-Scheduling Synthesis with Full Block Scalings. IFAC-PapersOnline, 53(2), 7292–7298. https://doi.org/10.1016/j.ifacol.2020.12.570
    58. Schneider, G. (2020). The KdV approximation for a system with unstable resonances. Math. Methods Appl. Sci., 43(6), 3185--3199.
    59. Semmelmann, U., Wang, C., & Wang, M. Y.-K. (2020). On the linear stability of nearly Kähler 6-manifolds. Ann. Global Anal. Geom., 57(1), 15--22. https://doi.org/10.1007/s10455-019-09686-5
    60. Steinwart, I. (2020). Reproducing Kernel Hilbert Spaces Cannot Contain all Continuous Functions on a Compact Metric Space. Fakultät für Mathematik und Physik, Universität Stuttgart.
    61. 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
    62. 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), 71--87. https://doi.org/10.1016/j.ydbio.2020.03.015
  4. 2019

    1. Ammann, B., Kröncke, K., Weiss, H., & Witt, F. (2019). Holonomy rigidity for Ricci-flat metrics. Math. Z., 291(1–2), 303--311. https://doi.org/10.1007/s00209-018-2084-3
    2. 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
    3. 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. (2019). Exa-Dune -- Flexible PDE Solvers, Numerical Methods and Applications.
    4. 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 (pp. 123--138). Cham: Birkhäuser.
    5. Bauer, R., Düll, W.-P., & Schneider, G. (2019). The Korteweg-de Vries, Burgers and Whitham limits for a spatially periodic Boussinesq model. Proc. R. Soc. Edinb., Sect. A, Math., 149(1), 191--217.
    6. 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
    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 (Eds.), 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. Bianchi, L. A., Blömker, D., & Schneider, G. (2019). Modulation equation and SPDEs on unbounded domains. Commun. Math. Phys., 371(1), 19--54.
    9. Brehler, M., Schirwon, M., Krummrich, P. M., & Göddeke, D. (2019). Simulation of Nonlinear Signal Propagation in Multimode Fibers on Multi-GPU Systems. Communications in Nonlinear Science and Numerical Simulation. https://doi.org/10.1016/j.cnsns.2019.105150
    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 (Eds.), Numerical Mathematics and Advanced Applications - ENUMATH 2017 (pp. 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), 43. 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), B466–B496. 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), 1246--1259.
    14. Colombo, R. M., LeFloch, P. G., Rohde, C., & Trivisa, K. (2019). Nonlinear Hyperbolic Problems: Modeling, Analysis, and Numerics. Oberwohlfach Rep., 16, 1419–1497. 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), 29--100. 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), 9600--9611. 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), 127--144. 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), 933--935. 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), 1931--1967. https://doi.org/10.1007/s00023-019-00796-1
    23. Hahn, B. N., & Kienle Garrido, M.-L. (2019). An efficient reconstruction approach for a class of dynamic imaging operators. Inverse Problems, 35(9), 094005. https://doi.org/10.1088/1361-6420/ab178b
    24. 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), 1265–1269. https://doi.org/10.1007/s10463-018-0687-4
    25. 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
    26. 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
    27. 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
    28. Homma, Y., & Semmelmann, U. (2019). The Kernel of the Rarita-Schwinger Operator on Riemannian Spin Manifolds. Comm. Math. Phys., 370(3), 853--871. https://doi.org/10.1007/s00220-019-03324-8
    29. Aufgaben und Lösungen zur Höheren Mathematik 1. (2019). In K. V. Höllig & J. V. Hörner (Eds.), SpringerLink. Bücher (2. Auflage, Vol. 1). https://doi.org/10.1007/978-3-662-58445-3
    30. 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.
    31. Kohr, M., Mikhailov, S. E., & Wendland, W. L. (2019). 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), 109–140. https://doi.org/10.1080/17476933.2019.1631293
    32. 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, 17–63. https://doi.org/10.1016/j.matpur.2019.04.002
    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 Analysis as a life (pp. 237--260). Birkhäuser/Springer, Cham. https://doi.org/10.1007/978-3-030-02650-9\_12
    34. 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. Computational Geosciences, 23(2), 339--354. https://doi.org/10.1007/s10596-018-9785-x
    35. Mazzeo, R., Swoboda, J., Weiss, H., & Witt, F. (2019). Asymptotic geometry of the Hitchin metric. Commun. Math. Phys., 367(1), 151--191. https://doi.org/10.1007/s00220-019-03358-y
    36. 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.
    37. 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
    38. Ostrowski, L., & Massa, F. (2019). An incompressible-compressible approach for droplet impact. In G. Cossali & S. Tonini (Eds.), Proceedings of the DIPSI Workshop 2019: Droplet ImpactPhenomena & Spray Investigations, Bergamo, Italy, 17th May 2019 (pp. 18–21). Università degli studi di Bergamo. https://doi.org/10.6092/DIPSI2019_pp18-21
    39. Rösinger, C. A., & Scherer, C. W. (2019). A Flexible Synthesis Framework of Structured Controllers for Networked Systems. IEEE Trans. Control Netw. Syst., 7(1), 6–18. https://doi.org/10.1109/TCNS.2019.2914411
    40. Rösinger, C. A., & Scherer, C. W. (2019). A Scalings Approach to $H_2$-Gain-Scheduling Synthesis without Elimination. IFAC-PapersOnLine, 52(28), 50–57. https://doi.org/10.1016/j.ifacol.2019.12.347
    41. Santin, G., & Haasdonk, B. (2019). Kernel Methods for Surrogate Modelling. University of Stuttgart.
    42. Santin, G., Wittwar, D., & Haasdonk, B. (2019). Sparse approximation of regularized kernel interpolation by greedy algorithms.
    43. Santin, G., & Haasdonk, B. (2019). Kernel Methods for Surrogate Modeling (ArXiv 1907.10556; Issue 1907.10556). https://arxiv.org/abs/1907.10556
    44. 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
    45. 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
    46. Schneider, G. (2019). The Zakharov limit of Klein-Gordon-Zakharov like systems in case of analytic solutions. Applicable Analysis. https://doi.org/10.1080/00036811.2019.1695785
    47. 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
    48. Semmelmann, U., & Weingart, G. (2019). The standard Laplace operator. Manuscripta Math., 158(1–2), 273--293. https://doi.org/10.1007/s00229-018-1023-2
    49. 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
    50. Steinwart, I. (2019). A Sober Look at Neural Network Initializations. Fakultät für Mathematik und Physik, Universität Stuttgart.
    51. 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
    52. Wenzel, T., Santin, G., & Haasdonk, B. (2019). A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution.
    53. Wittwar, D., Santin, G., & Haasdonk, B. (2019). Part II on matrix valued kernels including analysis.
    54. 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 (Eds.), Numerical Mathematics and Advanced Applications ENUMATH 2017 (pp. 113--121). Springer International Publishing.
    55. 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), 294–300. https://doi.org/10.1093/ejcts/ezz027
    56. 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
  5. 2018

    1. Afkham, B. M., Bhatt, A., Haasdonk, B., & Hesthaven, J. S. (2018). Symplectic Model-Reduction with a Weighted Inner Product.
    2. Babak, M. Afkham., Bhatt, A., Haasdonk, B., & Hesthaven, J. S. (2018). Symplectic Model-Reduction with a Weighted Inner Product.
    3. Barth, A., & Stein, A. (2018). A Study of Elliptic Partial Differential Equations with Jump Diffusion    Coefficients. SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 6(4), 1707–1743. https://doi.org/10.1137/17M1148888
    4. 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
    5. Barth, A., & Stein, A. (2018). Approximation and simulation of infinite-dimensional Levy processes. STOCHASTICS AND PARTIAL DIFFERENTIAL EQUATIONS-ANALYSIS AND COMPUTATIONS, 6(2), 286–334. https://doi.org/10.1007/s40072-017-0109-2
    6. Bhatt, A., Fehr, J., & Hassdonk, B. (2018). Model Order Reduction of an Elastic Body under Large Rigid Motion. Proceedings of ENUMATH 2017, Voss, Norway.
    7. Bhatt, A., & Haasdonk, B. (2018). Certified and structure-preserving model order reduction of EMBS. In RAMSA 2017, New Delhi.
    8. Bhatt, A., Haasdonk, B., & Moore, B. E. (2018). Structure-preserving Integration and Model Order Reduction.
    9. Blaschzyk, I., & Steinwart, I. (2018). Improved classification rates under refined margin conditions. ELECTRONIC JOURNAL OF STATISTICS, 12(1), 793–823. https://doi.org/10.1214/18-EJS1406
    10. Brehler, M., Schirwon, M., Göddeke, D., & Krummrich, P. (2018, July). Modeling the Kerr-Nonlinearity in Mode-Division Multiplexing Fiber  Transmission Systems on GPUs. Proceedings of Advanced Photonics 2018.
    11. Brünnette, T., Santin, G., & Haasdonk, B. (2018). Greedy kernel methods for accelerating implicit integrators for parametric  ODEs. Proceedings of ENUMATH 2017. http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1767
    12. Buchfink, P. (2018). Structure-preserving Model Reduction for Elasticity [Diploma thesis].
    13. De Marchi, S., Iske, A., & Santin, G. (2018). Image reconstruction from scattered Radon data by weighted positive  definite kernel functions. Calcolo, 55(1), 2. https://doi.org/10.1007/s10092-018-0247-6
    14. de Rijk, B. (2018). Spectra and stability of spatially periodic pulse patterns II: the critical spectral curve. SIAM J. Math. Anal., 50(2), 1958--2019. https://doi.org/10.1137/17M1127594
    15. de Rijk, B., & Sandstede, B. (2018). Diffusive stability against nonlocalized perturbations of planar wave trains in reaction-diffusion systems. J. Differential Equations, 265(10), 5315--5351. https://doi.org/10.1016/j.jde.2018.07.011
    16. Degeratu, A., & Mazzeo, R. (2018). Fredholm theory for elliptic operators on quasi-asymptotically conical spaces. Proc. Lond. Math. Soc. (3), 116(5), 1112--1160. https://doi.org/10.1112/plms.12105
    17. Devroye, L., Gyorfi, L., Lugosi, G., & Walk, H. (2018). A nearest neighbor estimate of the residual variance. ELECTRONIC JOURNAL OF STATISTICS, 12(1), 1752–1778. https://doi.org/10.1214/18-EJS1438
    18. 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
    19. 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), 1833--1890. https://doi.org/10.1137/17M1122840
    20. Doering, M., Gyorfi, L., & Walk, H. (2018). Rate of Convergence of k-Nearest-Neighbor Classification Rule. JOURNAL OF MACHINE LEARNING RESEARCH, 18.
    21. Dreier, N.-A., Altenbernd, M., Engwer, C., & Göddeke, D. (2018, March). A high-level C++ approach to manage local errors, asynchrony and  faults in an MPI application. Proceedings of 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2018).
    22. 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), 2598--2632. https://doi.org/10.1016/j.jde.2017.10.031
    23. 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), 71--80.
    24. 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), 71--80. https://doi.org/10.1515/jaa-2018-0007
    25. Düll, W.-P. (2018). On the mathematical description of time-dependent surface water waves. Jahresber. Dtsch. Math.-Ver., 120(2), 117--141. https://doi.org/10.1365/s13291-017-0173-6
    26. Engwer, C., Altenbernd, M., Dreier, N.-A., & G�ddeke, D. (2018, March). 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).
    27. Engwer, C., Altenbernd, M., Dreier, N.-A., & Göddeke, D. (2018, March). 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).
    28. 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
    29. 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.
    30. 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), 1585–1593. https://doi.org/10.21873/anticanres.12388
    31. 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
    32. 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
    33. 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
    34. Georgiev, V., & Wirth, J. (2018). Zero resonances for localised potentials. Journal of Mathematical Physics, 59(7), 071502. https://doi.org/10.1063/1.5027717
    35. 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
    36. Gimperlein, H., Meyer, F., �zdemir, 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
    37. Gimperlein, H., Meyer, F., �zdemir, C., Stark, D., & Stephan, E. P. (2018). Boundary elements with mesh refinements for the wave equation. Numer. Math., (accepted). https://arxiv.org/abs/1801.09736
    38. Griesemer, M., & Wünsch, A. (2018). On the domain of the Nelson Hamiltonian. J. Math. Phys., 59(4), 042111, 21. https://doi.org/10.1063/1.5018579
    39. Griesemer, M., & Linden, U. (2018). Stability of the two-dimensional Fermi polaron. Lett. Math. Phys., 108(8), 1837--1849. https://doi.org/10.1007/s11005-018-1055-2
    40. Guo, Y., & Scherer, C. W. (2018). Robust Gain-Scheduled Controller Design with a Hierarchical Structure. IFAC-PapersOnline, 51(25), 228–233. https://doi.org/10.1016/j.ifacol.2018.11.110
    41. Haasdonk, B., Hamzi, B., Santin, G., & Wittwar, D. (2018). Greedy Kernel Methods for Center Manifold Approximation (ArXiv 1810.11329; Issue 1810.11329).
    42. Haasdonk, B., & Santin, G. (2018). Greedy Kernel Approximation for Sparse Surrogate Modeling. In W. Keiper, A. Milde, & S. Volkwein (Eds.), Reduced-Order Modeling (ROM) for Simulation and Optimization: Powerful Algorithms as Key Enablers for Scientific Computing (pp. 21--45). Springer International Publishing. https://doi.org/10.1007/978-3-319-75319-5_2
    43. 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
    44. Hang, H., Steinwart, I., Feng, Y., & Suykens, J. A. K. (2018). Kernel Density Estimation for Dynamical Systems. J. Mach. Learn. Res., 19, 1--49.
    45. Harbrecht, H., Wendland, W. L., & Zorii, N. (2018). Minimal energy problems for strongly singular Riesz kernels. Mathematische Nachrichten, 291, 55–85. https://doi.org/10.1002/mana.201600024
    46. 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
    47. 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
    48. Kohler, M., Krzyzak, A., Tent, R., & Walk, H. (2018). Nonparametric quantile estimation using importance sampling. ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, 70(2), 439–465. https://doi.org/10.1007/s10463-016-0595-4
    49. 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), 1921–1965. https://doi.org/10.1007/s00021-018-0394-1
    50. 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. Calculus of Variations and Partial Differential Equations, 57:165. https://doi.org/10.1007/s00526-018-1426-7
    51. Kovar\’ık, H., Ruszkowski, B., & Weidl, T. (2018). Melas-type bounds for the Heisenberg Laplacian on bounded domains. Journal of Spectral Theory, 8(2), 413--434. https://doi.org/10.4171/jst/200
    52. 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
    53. Kuhn, T., Dürrwächter, J., Beck, A., Munz, C.-D., Meyer, F., & Rohde, C. (2018). Uncertainty Quantification for Direct Aeroacoustic Simulations of  Cavity Flows: Vol. (submitted). http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1891
    54. 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, 34(8), e3095. https://doi.org/10.1002/cnm.3095
    55. K�ppel, 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
    56. K�ppel, 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
    57. 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
    58. 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
    59. Langer, A. (2018). Overlapping domain decomposition methods for total variation denoising. http://people.ricam.oeaw.ac.at/a.langer/publications/DDfTV.pdf
    60. 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
    61. Meyer, F., Schlachter, L., & Schneider, F. (2018). A hyperbolicity-preserving discontinuous stochastic Galerkin scheme  for uncertain hyperbolic systems of equations. https://arxiv.org/abs/1805.10177
    62. Miller, C. T., Gray, W. G., Kees, C. E., Rybak, I. V., & Shepherd, B. J. (2018). Modeling sediment transport in three-phase surface water systems. J. Hydraul. Res. (Accepted).
    63. 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), 382--404. https://doi.org/10.1111/sjos.12299
    64. 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), 1497--1530. https://doi.org/10.3150/16-BEJ905
    65. Oesting, M., & Strokorb, K. (2018). Efficient simulation of Brown-Resnick processes based on variance reduction of Gaussian processes. Adv. in Appl. Probab., 50(4), 1155--1175. https://doi.org/10.1017/apr.2018.54
    66. Oesting, M., & Stein, A. (2018). Spatial modeling of drought events using max-stable processes. Stoch. Env. Res. Risk A., 32(1), 63--81. https://doi.org/10.1007/s00477-017-1406-z
    67. Oesting, M. (2018). Equivalent representations of max-stable processes via $\ell^p$-norms. J. Appl. Probab., 55(1), 54--68. https://doi.org/10.1017/jpr.2018.5
    68. 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 P�clet number. Erscheint Bei Int. J. Multiph. Flow. http://arxiv.org/abs/1609.03410
    69. Rigaud, G., & Hahn, B. N. (2018). 3D Compton scattering imaging and contour reconstruction for a class of Radon transforms. Inverse Problems, 34(7), 075004. https://doi.org/10.1088/1361-6420/aabf0b
    70. Rohde, C., & Zeiler, C. (2018). On Riemann Solvers and Kinetic Relations for Isothermal Two-Phase  Flows with Surface Tension. Z. Angew. Math. Phys., 69:76. https://doi.org/10.1007/s00033-018-0958-1
    71. Rohde, C. (2018). Fully resolved compressible two-phase flow : modelling, analytical and numerical issues. In M. Bulicek, E. Feireisl, & M. Pokorný (Eds.), New trends and results in mathematical description of fluid flows (pp. 115–181). Birkhäuser. https://doi.org/10.1007/978-3-319-94343-5
    72. Ruiz, P. A., Freiberg, U. R., & Kigami, J. (2018). Completely symmetric resistance forms on the stretched Sierpinski gasket. JOURNAL OF FRACTAL GEOMETRY, 5(3), 227–277. https://doi.org/10.4171/JFG/61
    73. Santin, G., Wittwar, D., & Haasdonk, B. (2018). Greedy regularized kernel interpolation (ArXiv Preprint 1807.09575; Issue 1807.09575). University of Stuttgart.
    74. Scherer, C. W., & Veenman, J. (2018). Stability analysis by dynamic dissipation inequalities: On merging frequency-domain techniques with time-domain conditions. Syst. Control Lett., 121, 7–15. https://doi.org/10.1016/j.sysconle.2018.08.005
    75. Scherer, C. W., & Holicki, T. (2018). An IQC theorem for relations: Towards stability analysis of data-integrated systems. IFAC-PapersOnline, 51(25), 390–395. https://doi.org/10.1016/j.ifacol.2018.11.138
    76. Schmidt, A., & Haasdonk, B. (2018). Data-driven surrogates of value functions and applications to feedback control for dynamical systems. http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1766
    77. Schmidt, A., Wittwar, D., & Haasdonk, B. (2018). Rigorous and effective a-posteriori error bounds for nonlinear problems -- Application to RB methods [SimTech Preprint]. University of Stuttgart.
    78. Schmidt, A., & Haasdonk, B. (2018). Reduced basis approximation of large scale parametric algebraic Riccati equations. ESAIM: Control, Optimisation and Calculus of Variations, 24(1), 129--151. https://doi.org/10.1051/cocv/2017011
    79. Seus, D., Mitra, K., Pop, I. S., Radu, F. A., & Rohde, C. (2018). A linear domain decomposition method for partially saturated flow  in porous media. Comp. Methods in Appl. Mech. Eng, 333, 331--355. https://doi.org/10.1016/j.cma.2018.01.029
    80. Sharanya, V., Sekhar, G. P. R., & Rohde, C. (2018). The low surface Péclet number regime for surfactant-laden viscous droplets: Influence of surfactant concentration, interfacial slip effects and cross migration. Int. J. of Multiph. Flow, 107, 82–103. https://doi.org/10.1016/j.ijmultiphaseflow.2018.05.008
    81. Wittwar, D., Santin, G., & Haasdonk, B. (2018). Interpolation with uncoupled separable matrix-valued kernels. ArXiv E-Prints.
    82. Wittwar, D., & Haasdonk, B. (2018). Greedy Algorithms for Matrix-Valued Kernels. University of Stuttgart. http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1773
    83. Zhang, R., Kyriss, T., Dippon, J., Hansen, M., Boedeker, E., & Friedel, G. (2018). American Society of Anesthesiologists physical status facilitates risk stratification of elderly patients undergoing thoracoscopic lobectomy. European Journal of Cardio-Thoracic Surgery, 53(5), 973–979. https://doi.org/10.1093/ejcts/ezx436
    84. Zhang, R., Kyriss, T., Dippon, J., Ciupa, S., Boedeker, E., & Friedel, G. (2018). Impact of comorbidity burden on morbidity following horacoscopic lobectomy: a propensity-matched analysis. J Thorac Dis., 2018 Mar;10(3), 1806–1814. https://doi.org/10.21037/jtd.2018.02.62
  6. 2017

    1. Afkham, B., & Hesthaven, J. (2017). Structure Preserving Model Reduction of Parametric Hamiltonian Systems. SIAM Journal on Scientific Computing, 39(6), A2616–A2644. https://doi.org/10.1137/17M1111991
    2. Alkämper, M., & Klöfkorn, R. (2017). Distributed Newest Vertex Bisection. Journal of Parallel and Distributed Computing, 104, 1–11. http://dx.doi.org/10.1016/j.jpdc.2016.12.003
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    110. Thomann, P., Steinwart, I., Blaschzyk, I., & Meister, M. (2017). Spatial Decompositions for Large Scale SVMs. In A. Singh & J. Zhu (Eds.), Proceedings of Machine Learning Research Volume 54: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics 2017 (pp. 1329--1337).
    111. Wirth, J. (2017). On t-dependent hyperbolic systems. Part 2. J. Math. Anal. Appl., 448(1), 293--318. https://doi.org/10.1016/j.jmaa.2016.11.026
    112. Wirth, J. (2017). Regular singular problems for hyperbolic systems and their              asymptotic integration. In New trends in analysis and interdisciplinary applications (pp. 553--561). Birkhäuser/Springer, Cham. https://doi.org/10.1007/978-3-319-48812-7_70
    113. Wittwar, D., Santin, G., & Haasdonk, B. (2017). Interpolation with uncoupled separable matrix-valued kernels. [ArXiv preprint 1807.09111, Accepted for publications in Dolomites Res. Notes Approx.].
    114. Wittwar, D., & Haasdonk, B. (2017). On uncoupled separable matrix-valued kernels. University of Stuttgart.
    115. Wittwar, D., Schmidt, A., & Haasdonk, B. (2017). Reduced Basis Approximation for the Discrete-time Parametric Algebraic Riccati Equation. University of Stuttgart.
  7. 2015

    1. Kutter, M. (2015). A two scale model for liquid phase epitaxy with elasticity [University of Stuttgart]. http://elib.uni-stuttgart.de/opus/volltexte/2015/9833/
  8. 2012

    1. Feistauer, M., & Sändig, A.-M. (2012). Graded mesh refinement and error estimates of higher order for DGFE  solutions of elliptic boundary value problems in polygons. Numerical Methods for Partial Differential Equations, 28(4), 1124--1151. https://doi.org/10.1002/num.20668
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