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. 2023

    1. 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
    2. 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), A49–A73. https://doi.org/10.1137/21M1415005
    3. 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
    4. Miao, Y., Rohde, C., & Tang, H. (2023). Well-posedness for a stochastic Camassa-Holm type equation with higher order nonlinearities. accepted by Stoch. Partial Differ. Equ. Anal. Comput. https://arxiv.org/abs/2105.08607
    5. Seus, D., Radu, F. A., & Rohde, C. (2023). Towards hybrid two-phase modelling using linear domain decomposition. Numer. Methods Partial Differential Equations, 39(1), 622–656. https://doi.org/10.1002/num.22906
    6. 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. (accepted). https://arxiv.org/abs/2106.15556
  2. 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. (2022). Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5. https://doi.org/10.18419/darus-3154
    3. 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
    4. 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
    5. Buchfinck, P., Glas, S., & Haasdonk, B. (2022). Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems.
    6. 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
    7. Burbulla, S., Hörl, M., & Rohde, C. (2022). Flow in Porous Media with Fractures of Varying Aperture. In arXiv e-prints. https://doi.org/10.48550/arXiv.2207.09301
    8. 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
    9. 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
    10. 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
    11. Frank, R., Laptev, A., & Weidl, T. (2022). Schrödinger Operators: Eigenvalues and Lieb–Thirring Inequalities. In Cambridge Studies in Advanced Mathematics (S. 512).
    12. Frank, R. L., Laptev, A., & Weidl, T. (2022). An improved one-dimensional Hardy inequality. https://arxiv.org/abs/2204.00877
    13. 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.
    14. Griesemer, M., & Hofacker, M. (2022). From Short-Range to Contact Interactions in Two-dimensional Many-Body Quantum Systems. Annales Henri Poincaré, 23(8), 2769--2818. https://doi.org/10.1007/s00023-021-01149-7
    15. 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
    16. 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
    17. 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
    18. Horsch, M. T., & Schembera, B. (2022). Documentation of epistemic metadata by a mid-level ontology of cognitive processes. Proc. JOWO 2022.
    19. 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
    20. 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), 121–132. https://doi.org/10.1515/itit-2022-0033
    21. 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
    22. 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.
    23. 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), 28 pp. https://doi.org/10.1016/j.jmaa.2022.126464
    24. 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). https://doi.org/10.21203/rs.3.rs-1742793/v1
    25. 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
    26. Magiera, J., & Rohde, C. (2022). Analysis and Numerics of Sharp and Diffuse Interface Models for Droplet Dynamics. In K. Schulte, C. Tropea, & B. Weigand (Hrsg.), Droplet Dynamics under Extreme Ambient Conditions. Springer International Publishing. https://doi.org/10.1007/978-3-031-09008-0_4
    27. 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
    28. 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
    29. Mel’nyk, 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
    30. 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
    31. 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
    32. Merkle, R., & Barth, A. (2022). On some distributional properties of subordinated Gaussian random fields. Methodol Comput Appl Probab.
    33. Miller, C. T., Gray, W. G., Kees, C. E., Rybak, I., & Shepherd, B. J. (2022). Correction to: Modelling Sediment Transport in Three-Phase Surface Water Systems. J. Hydraul. Res., 0, 1–4. https://doi.org/10.1080/00221686.2022.2107580
    34. Mohammadi, F., Eggenweiler, E., Flemisch, B., Oladyshkin, S., Rybak, I., Schneider, M., & Weishaupt, K. (2022). A Surrogate-Assisted Uncertainty-Aware Bayesian Validation Framework and its Application to Coupling Free Flow and Porous-Medium Flow. Comput. Geosci. (submitted). https://arxiv.org/abs/2106.13639
    35. 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
    36. 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
    37. 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.
    38. 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
    39. 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
    40. 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
    41. 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.
    42. 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
    43. 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
    44. 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
    45. 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
  3. 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. 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), Paper No. 98, 55. 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), 106634. 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), 285–311. 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), 2430–2453. https://doi.org/10.1214/21-EJS1845
    10. Brencher, L., & Barth, A. (2021). Scalar conservation laws with stochastic discontinuous flux function. ArXiv e-prints, arXiv:2107.00549 math.NA.
    11. Brencher, L., & Barth, A. (2021). Stochastic conservation laws with discontinuous flux functions: The multidimensional case.
    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., & 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), 38.
    16. 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
    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), 831--914. 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). 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
    22. Ehring, T., & Haasdonk, B. (2021). Greedy sampling and approximation for realizing feedback control for high dimensional nonlinear systems.
    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), 7439–7447. https://ojs.aaai.org/index.php/AAAI/article/view/16912
    24. 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
    25. 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
    26. 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
    27. 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
    28. 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
    29. 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
    30. Haasdonk, B. (2021). Model Order Reduction, Applications, MOR Software (D. Gruyter, Hrsg.; Bd. 3). De Gruyter. https://doi.org/10.1515/9783110499001
    31. 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
    32. Haasdonk, B., Wenzel, T., Santin, G., & Schmitt, S. (2021). Biomechanical Surrogate Modelling Using Stabilized Vectorial Greedy Kernel Methods.
    33. 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
    34. Hahn, B. N. (2021). Motion compensation strategies in tomography. https://doi.org/10.1007/978-3-030-57784-1_3
    35. Hamm, T., & Steinwart, I. (2021). Adaptive Learning Rates for Support Vector Machines Working on Data with Low Intrinsic Dimension. Ann. Statist.
    36. Hamm, T., & Steinwart, I. (2021). Intrinsic Dimension Adaptive Partitioning for Kernel Methods. Fakultät für Mathematik und Physik, Universität Stuttgart.
    37. 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
    38. 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
    39. 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
    40. 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
    41. 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
    42. 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
    43. 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.
    44. 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), 3785–3803. https://doi.org/10.1080/00036811.2021.1986027
    45. 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
    46. 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
    47. 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
    48. 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), 9641--9674. https://doi.org/10.1002/mma.7167
    49. 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), 4421--4460. https://doi.org/10.3934/dcds.2021042
    50. 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
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  4. 2020

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    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.
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    35. 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), 1094–1121. https://doi.org/10.1093/imanum/drz004
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    62. 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
    63. 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
    64. 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
    65. Ostrowski, L., & Rohde, C. (2020). Compressible multi-component flow in porous media with Maxwell-Stefan diffusion. Math. Meth. Appl. Sci., 1–22. https://doi.org/10.1002/mma.6185
    66. 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
    67. 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
    68. Pelinovsky, D. E., & Schneider, G. (2020). The monoatomic FPU system as a limit of a diatomic FPU system. Appl. Math. Lett., 107, 7.
    69. 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
    70. 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
    71. 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), 6155–6179. https://doi.org/10.1137/19M1242434
    72. 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
    73. 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
    74. Schneider, G. (2020). The KdV approximation for a system with unstable resonances. Math. Methods Appl. Sci., 43(6), 3185--3199.
    75. 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
    76. Steinwart, I. (2020). Reproducing Kernel Hilbert Spaces Cannot Contain all Continuous Functions on a Compact Metric Space. Fakultät für Mathematik und Physik, Universität Stuttgart.
    77. 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
    78. 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
  5. 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. Armiti-Juber, A., & Rohde, C. (2019). On Darcy-and Brinkman-type models for two-phase flow in asymptotically flat domains. Comput. Geosci., 23(2), 285–303. 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), 592–612. 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), 191--217. https://doi.org/10.1017/S0308210518000227
    7. 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
    8. 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
    9. Bianchi, L. A., Blömker, D., & Schneider, G. (2019). Modulation equation and SPDEs on unbounded domains. Commun. Math. Phys., 371(1), 19--54.
    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), 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.
    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. 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.
    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., & 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
    32. 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
    33. 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), 1850044, 20. https://doi.org/10.1142/S2591728518500445
    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. Comput. Geosci., 2(23), 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. 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
    37. 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.
    38. 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
    39. 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
    40. 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
    41. 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
    42. Santin, G., & Haasdonk, B. (2019). Kernel Methods for Surrogate Modelling. University of Stuttgart.
    43. Santin, G., Wittwar, D., & Haasdonk, B. (2019). Sparse approximation of regularized kernel interpolation by greedy algorithms.
    44. Santin, G., & Haasdonk, B. (2019). Kernel Methods for Surrogate Modeling (ArXiv 1907.10556; Nummer 1907.10556). https://arxiv.org/abs/1907.10556
    45. 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
    46. 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
    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. 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), 012110. https://doi.org/10.1063/1.5064694
    51. Steinwart, I. (2019). A Sober Look at Neural Network Initializations. Fakultät für Mathematik und Physik, Universität Stuttgart.
    52. 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
    53. Wenzel, T., Santin, G., & Haasdonk, B. (2019). A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution.
    54. Wittwar, D., Santin, G., & Haasdonk, B. (2019). Part II on matrix valued kernels including analysis.
    55. 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.
    56. 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
    57. 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
  6. 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), 897–912. https://doi.org/10.1177/1094342016684006
    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., 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
    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. In Invited online talk in Department of Mathematics, IIT Roorkee.
    9. 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
    10. 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
    11. 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.
    12. 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
    13. Buchfink, P. (2018). Structure-preserving Model Reduction for Elasticity [Diploma thesis].
    14. 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
    15. 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
    16. 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
    17. 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
    18. 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
    19. 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
    20. 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
    21. 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
    22. Doering, M., Gyorfi, L., & Walk, H. (2018). Rate of Convergence of k-Nearest-Neighbor Classification Rule. JOURNAL OF MACHINE LEARNING RESEARCH, 18.
    23. 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
    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.
    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. 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
    27. 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).
    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
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    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
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    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
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    46. Holicki, T., & Scherer, C. W. (2018). A Swapping Lemma for Switched Systems. IFAC-PapersOnLine, 51(25), 346–352. https://doi.org/10.1016/j.ifacol.2018.11.131
    47. 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
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    50. 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
    51. 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), Paper No. 165, 41. https://doi.org/10.1007/s00526-018-1426-7
    52. 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
    53. 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
    54. 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
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    56. 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), e3095. https://doi.org/10.1002/cnm.3095
    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
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    63. 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
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    65. 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
    66. 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
    67. 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
    68. 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
    69. Rohde, C., & Zeiler, C. (2018). On Riemann solvers and kinetic relations for isothermal two-phase  flows with surface tension. Z. Angew. Math. Phys., 3, 69, Art. 76. https://doi.org/10.1007/s00033-018-0958-1
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    71. 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
    72. Santin, G., Wittwar, D., & Haasdonk, B. (2018). Greedy regularized kernel interpolation (ArXiv preprint 1807.09575; Nummer 1807.09575). University of Stuttgart.
    73. 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
    74. 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
    75. 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
    76. 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.
    77. 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
    78. Seus, D., Pop, I. S., Rohde, C., Mitra, K., & Radu, F. (2018). A linear domain decompostition method for partially saturated flow in porous media. Comput. Methods Appl. Mech. Eng., 333, 331–355. https://doi.org/10.1016/j.cma.2018.01.029
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    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
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    3. Alkämper, M., & Langer, A. (2017). Using DUNE-ACFem for Non-smooth Minimization of Bounded Variation  Functions. Archive of Numerical Software, 5(1), 3--19. https://journals.ub.uni-heidelberg.de/index.php/ans/article/view/27475
    4. Alla, A., Haasdonk, B., & Schmidt, A. (2017). Feedback control of parametrized PDEs via model order reduction and dynamic programming principle. University of Stuttgart. http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1765
    5. Alla, A., Gunzburger, M., Haasdonk, B., & Schmidt, A. (2017). Model order reduction for the control of parametrized partial differential equations via dynamic programming principle. University of Stuttgart.
    6. Alla, A., Schmidt, A., & Haasdonk, B. (2017). Model Order Reduction Approaches for Infinite Horizon Optimal Control  Problems via the HJB Equation. In P. Benner, M. Ohlberger, A. Patera, G. Rozza, & K. Urban (Hrsg.), Model Reduction of Parametrized Systems (S. 333--347). Springer International Publishing. https://doi.org/10.1007/978-3-319-58786-8_21
    7. Barth, A., & Fuchs, F. G. (2017). Uncertainty quantification for linear hyperbolic equations with    stochastic process or random field coefficients. APPLIED NUMERICAL MATHEMATICS, 121, 38–51. https://doi.org/10.1016/j.apnum.2017.06.009
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    27. Feistauer, M., Roskovec, F., & Sändig, A.-M. (2017). Discontinuous Galerkin Method for an Elliptic Problem with Nonlinear  Boundary Conditions in a Polygon. IMA, 00, 1–31. https://doi.org/10.1093/imanum/drx070
    28. Feistauer, M., Bartos, O., Roskovec, F., & Sändig, A.-M. (2017). Analysis of the FEM and DGM for an elliptic problem with a nonlinear  Newton boundary condition. Proceeding of the EQUADIFF 17, 127–136. http://www.iam.fmph.uniba.sk/amuc/ojs/index.php/equadiff/
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    35. Funke, S., Mendel, T., Miller, A., Storandt, S., & Wiebe, M. (2017). Map Simplification with Topology Constraints: Exactly and in Practice. Proceedings of the Ninteenth Workshop on Algorithm Engineering and  Experiments, ALENEX 2017, Barcelona, Spain, Hotel Porta Fira, January  17-18, 2017., 185--196. https://doi.org/10.1137/1.9781611974768.15
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    49. Gutt, R., Kohr, M., Mikhailov, S., & Wendland, W. L. (2017). On the mixed problem for the semilinear Darcy-Forchheimer-Brinkman  systems in Besov spaces on creased Lipschitz domains. Math. Meth. Appl. Sci., 18, 7780–7829. https://doi.org/10.1002/mma.4562
    50. Gutt, R., Kohr, M., Mikhailov, S. E., & Wendland, W. L. (2017). On the mixed problem for the semilinear Darcy-Forchheimer-Brinkman PDE system in Besov spaces on creased Lipschitz domains. MATHEMATICAL METHODS IN THE APPLIED SCIENCES, 40(18), 7780–7829. https://doi.org/10.1002/mma.4562
    51. Haasdonk, B. (2017). Reduced Basis Methods for Parametrized PDEs -- A Tutorial Introduction for Stationary and Instationary Problems. In P. Benner, A. Cohen, M. Ohlberger, & K. Willcox (Hrsg.), Model Reduction and Approximation: Theory and Algorithms (S. 65--136). SIAM, Philadelphia. http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=938
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    53. Hahn, B. N. (2017). Motion Estimation and Compensation Strategies in Dynamic Computerized Tomography. Sensing and Imaging, 18(10), 1–20. https://doi.org/10.1007/s11220-017-0159-6
    54. Hang, H., & Steinwart, I. (2017). A Bernstein-type Inequality for Some Mixing Processes and Dynamical Systems with an Application to Learning. Ann. Statist., 45, 708--743. https://doi.org/10.1214/16-AOS1465
    55. Harbrecht, H., Wendland, W. L., & Zorii, N. (2017). Riesz energy problems for strongly singular kernels. Math. Nachr. https://doi.org/10.1002/mana.201600024
    56. Heil, K., Moroianu, A., & Semmelmann, U. (2017). Killing tensors on tori. J. Geom. Phys., 117, 1--6. https://doi.org/10.1016/j.geomphys.2017.02.010
    57. Hintermüller, M., Rautenberg, C. N., Wu, T., & Langer, A. (2017). Optimal Selection of the Regularization Function in a Weighted Total  Variation Model. Part II: Algorithm, Its Analysis and Numerical Tests. Journal of Mathematical Imaging and Vision, 1--19. https://link.springer.com/article/10.1007/s10851-017-0736-2
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    59. Hänel, A., & Weidl, T. (2017). Spectral asymptotics for the Dirichlet Laplacian with a Neumann window via a Birman-Schwinger analysis of the Dirichlet-to-Neumann operator. Functional Analysis and Operator Theory for Quantum Physics, EMS Series of Congress Reports, J. Dittrich, et al. (Eds.), 315–352.
    60. Höllig, K. V., & Hörner, J. V. (Hrsg.). (2017). Aufgaben und Lösungen zur höheren Mathematik (S. xi, 533 Seiten) [Aufgabensammlung]. Springer Spektrum. http://deposit.d-nb.de/cgi-bin/dokserv?id=86f385b1e03e40a0a23a214a0c3c5f72&prov=M&dok_var=1&dok_ext=htm
    61. Kane, B., Klöfkorn, R., & Gersbacher, C. (2017). hp--Adaptive Discontinuous Galerkin Methods for Porous Media Flow. International Conference on Finite Volumes for Complex Applications, 447--456.
    62. Kane, B. (2017). Using DUNE-FEM for Adaptive Higher Order Discontinuous Galerkin  Methods for Two-phase Flow in Porous Media. Archive of Numerical Software, 5(1), 129--149.
    63. Kohr, M., Medkova, D., & Wendland, W. L. (2017). On the Oseen-Brinkman flow around an (m-1)-dimensional obstacle. Monatshefte für Mathematik, 483, 269–302. https://doi.org/MOFM-D16-00078
    64. Kohr, M., Mikhailov, S., & Wendland, W. L. (2017). Transmission problems for the Navier-Stokes and Darcy-Forchheimer-Brinkman  systems in Lipschitz domains on compact Riemannian mani. J of Mathematical Fluid Mechanics, 19, 203–238.
    65. Kollross, A. (2017). Hyperpolar actions on reducible symmetric spaces. Transformation Groups, 22(1), 207--228. https://doi.org/10.1007/s00031-016-9384-7
    66. Kovarik, H., Ruszkowski, B., & Weidl, T. (2017). Spectral estimates for the Heisenberg Laplacian on cylinders. Functional Analysis and Operator Theory for Quantum Physics, EMS Series of Congress Reports, J. Dittrich, et al. (eds.), 433–446.
    67. Kutter, M., Rohde, C., & Sändig, A.-M. (2017). Well-posedness of a two scale model for liquid phase epitaxy with elasticity. Contin. Mech. Thermodyn., 29(4), 989–1016. https://doi.org/10.1007/s00161-015-0462-1
    68. Köppel, M., Franzelin, F., Kröker, I., Oladyshkin, S., Santin, G., Wittwar, D., Barth, A., Haasdonk, B., Nowak, W., Pflüger, D., & Rohde, C. (2017). Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario. University of Stuttgart. http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1759
    69. Köppel, M., Kröker, I., & Rohde, C. (2017). Intrusive Uncertainty Quantification for Hyperbolic-Elliptic Systems  Governing Two-Phase Flow in Heterogeneous Porous Media. Comput. Geosci., 21, 807–832. https://doi.org/10.1007/s10596-017-9662-z
    70. Köppel, M., Franzelin, F., Kröker, I., Oladyshkin, S., Wittwar, D., Santin, G., Barth, A., Haasdonk, B., Nowak, W., Pflüger, D., & Rohde, C. (2017). Datasets and executables of data-driven uncertainty quantification benchmark in carbon dioxide storage. https://doi.org/10.5281/zenodo.933827
    71. Köppl, T., Santin, G., Haasdonk, B., & Helmig, R. (2017). Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods. University of Stuttgart.
    72. Langer, A. (2017). Automated Parameter Selection in the L1-L2-TV Model for Removing  Gaussian Plus Impulse Noise. Inverse Problems, 33, 41. http://people.ricam.oeaw.ac.at/a.langer/publications/L1L2TVm.pdf
    73. Langer, A. (2017). Automated Parameter Selection for Total Variation Minimization in  Image Restoration. Journal of Mathematical Imaging and Vision, 57, 239--268. https://doi.org/10.1007/s10851-016-0676-2
    74. Maboudi 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
    75. Martini, I., Rozza, G., & Haasdonk, B. (2017). Certified Reduced Basis Approximation for the Coupling of Viscous  and Inviscid Parametrized Flow Models. Journal of Scientific Computing. https://doi.org/10.1007/s10915-017-0430-y
    76. Maz’ya, V., Natroshvili, D., Shargorodsky, E., & Wendland, W. L. (Hrsg.). (2017). Recent Trends in Operator Theory and Partial Differential Equations.  The Roland Duduchava Anniverary Volume (Nr. 258; Nummer 258). Birkhäuser/Springer International.
    77. Minbashian, H., Adibi, H., & Dehghan, M. (2017). On Resolution of Boundary Layers of Exponential Profile with Small  Thickness Using an Upwind Method in IGA.
    78. Minbashian, H. (2017). Wavelet-based Multiscale Methods for Numerical Solution of Hyperbolic  Conservation Laws. Amirkabir University of Technology (Tehran 11/2017 Polytechnic),  Tehran, Iran.
    79. Minbashian, H., Adibi, H., & Dehghan, M. (2017). An adaptive wavelet space-time SUPG method for hyperbolic conservation  laws. Numerical Methods for Partial Differential Equations, 33(6), 2062–2089. https://doi.org/10.1002/num.22180
    80. Minbashian, H., Adibi, H., & Dehghan, M. (2017). An Adaptive Space-Time Shock Capturing Method with High Order Wavelet  Bases for the System of Shallow Water Equations. International Journal of Numerical Methods for Heat & Fluid Flow.
    81. Oesting, M., Schlather, M., & Friederichs, P. (2017). Statistical post-processing of forecasts for extremes using bivariate Brown-Resnick processes with an application to wind gusts. Extremes, 20(2), 309--332. https://doi.org/10.1007/s10687-016-0277-x
    82. Pelinovsky, D., & Schneider, G. (2017). Bifurcations of standing localized waves on periodic graphs. Ann. Henri Poincaré, 18(4), 1185--1211.
    83. Rösinger, C. A., & Scherer, C. W. (2017). Structured Controller Design With Applications to Networked Systems. Proc. 56th IEEE Conf. Decision and Control. https://doi.org/10.1109/CDC.2017.8264365
    84. Santin, G., & Haasdonk, B. (2017). Convergence rate of the data-independent P-greedy algorithm in kernel-based approximation. Dolomites Res. Notes Approx., 10, 68--78. /brokenurl#www.emis.de/journals/DRNA/9-2.html
    85. Santin, G., & Haasdonk, B. (2017). Greedy Kernel Approximation for Sparse Surrogate Modelling. University of Stuttgart.
    86. Santin, G., & Haasdonk, B. (2017). Non-symmetric kernel greedy interpolation.
    87. Schanz, M., Schaaf, L., Dippon, J., Biegger, D., Fritz, P., Alscher, MD., & Kimmel, M. (2017). Renal effects of metallothionein induction by zinc in vitro and in vivo. BMC Nephrol, 2017 Mar 16;18(1), 91. https://doi.org/10.1186/s12882-017-0503-z
    88. Schanz, M., Ketteler, M., Heck, M., Dippon, J., Alscher, MD., & Kimmel, M. (2017). Impact of an in-Hospital Patient Education Program on Choice of Renal Replacement Modality in Unplanned Dialysis Initiation. Kidney & blood pressure research, 42(5), 865–876. https://doi.org/10.1159/000484531
    89. Schmid, J., & Griesemer, M. (2017). Well-posedness of non-autonomous linear evolution equations in              uniformly convex spaces. Math. Nachr., 290(2–3), 435--441. https://doi.org/10.1002/mana.201500052
    90. Schmidt, A., & Haasdonk, B. (2017). Data-driven surrogates of value functions and applications to feedback  control for dynamical systems. University of Stuttgart. http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1742
    91. Schmidt, A., & Haasdonk, B. (2017). Reduced basis approximation of large scale parametric algebraic Riccati  equations. ESAIM: Control, Optimisation and Calculus of Variations. https://doi.org/10.1051/cocv/2017011
    92. Schneider, G., & Uecker, H. (2017). Nonlinear PDEs. A dynamical systems approach. In Grad. Stud. Math. (Bd. 182, S. xiii + 575). Providence, RI: American Mathematical Society (AMS).
    93. Steinwart, I. (2017). A Short Note on the Comparison of Interpolation Widths, Entropy Numbers, and Kolmogorov Widths. J. Approx. Theory, 215, 13--27. https://doi.org/10.1016/j.jat.2016.11.006
    94. Steinwart, I., & Thomann, P. (2017). liquidSVM: A Fast and Versatile SVM Package. Fakultät für Mathematik und Physik, Universität Stuttgart.
    95. Steinwart, I., Sriperumbudur, B. K., & Thomann, P. (2017). Adaptive Clustering Using Kernel Density Estimators. Fakultät für Mathematik und Physik, Universität Stuttgart.
    96. Steinwart, I. (2017). A Short Note on the Comparison of Interpolation Widths, Wntropy Numbers, and Kolmogorov Widths. J. Approx. Theory, 215, 13--27.
    97. Steinwart, I. (2017). Representation of Quasi-Monotone Functionals by Families of Separating Hyperplanes. Math. Nachr., 290, 1859--1883. https://doi.org/10.1002/mana.201500350
    98. Tempel, P., Schmidt, A., Haasdonk, B., & Pott, A. (2017). Application of the Rigid Finite Element Method to the Simulation  of Cable-Driven Parallel Robots. In Computational Kinematics (S. 198--205). Springer International Publishing. https://doi.org/10.1007/978-3-319-60867-9_23
    99. Thomann, P., Steinwart, I., Blaschzyk, I., & Meister, M. (2017). Spatial Decompositions for Large Scale SVMs. In A. Singh & J. Zhu (Hrsg.), Proceedings of Machine Learning Research Volume 54: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics 2017 (S. 1329--1337).
    100. 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
    101. Wirth, J. (2017). Regular singular problems for hyperbolic systems and their              asymptotic integration. In New trends in analysis and interdisciplinary applications (S. 553--561). Birkhäuser/Springer, Cham. https://doi.org/10.1007/978-3-319-48812-7_70
    102. 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.].
    103. Wittwar, D., & Haasdonk, B. (2017). On uncoupled separable matrix-valued kernels. University of Stuttgart.
    104. Wittwar, D., Schmidt, A., & Haasdonk, B. (2017). Reduced Basis Approximation for the Discrete-time Parametric Algebraic  Riccati Equation. University of Stuttgart.
  8. 2016

    1. Alkämper, M., Dedner, A., Klöfkorn, R., & Nolte, M. (2016). The DUNE-ALUGrid Module. Archive of Numerical Software, 4(1), 1--28. https://doi.org/10.11588/ans.2016.1.23252
    2. Alla, A., Schmidt, A., & Haasdonk, B. (2016). Model order reduction approaches for infinite horizon optimal control problems via the HJB equation. University of Stuttgart. https://arxiv.org/abs/1607.02337
    3. Allerhand, L., Gershon., E., & Shaked, U. (2016). Robust state-feedback control of stochastic state-multiplicative discrete-time linear switched systems with dwell time. Int. J. Robust Nonlin., 26(2), 187–200. https://doi.org/10.1002/rnc.3301
    4. Amsallem, D., & Haasdonk, B. (2016). PEBL-ROM: Projection-Error Based Local Reduced-Order Models. AMSES, Advanced Modeling and Simulation in Engineering Sciences, 3(6), Article 6. https://doi.org/10.1186/s40323-016-0059-7
    5. Antoulas, A. C., Haasdonk, B., & Peherstorfer, B. (2016). MORML 2016 Book of Abstracts. University of Stuttgart.
    6. Apprich, C., Höllig, K., Hörner, J., & Reif, U. (2016). Collocation with WEB--Splines. Advances in Computational Mathematics, 42(4), 823--842. https://doi.org/10.1007/s10444-015-9444-x
    7. Barseghyan, D., Exner, P., Kovarik, H., & Weidl, T. (2016). Semiclassical bounds in magnetic bottles. Reviews in Mathematical Physics, 28(1), 1650002. https://doi.org/10.1142/S0129055X16500021
    8. Barth, A., & Stein, A. (2016). Approximation and simulation of infinite-dimensional Lévy processes. http://arxiv.org/abs/1612.05541
    9. Barth, A., Schwab, C., & Sukys, J. (2016). Multilevel Monte Carlo simulation of statistical solutions to  the Navier-Stokes equations. In Monte Carlo and quasi-Monte Carlo methods (Bd. 163, S. 209--227). Springer, Cham. https://doi.org/10.1007/978-3-319-33507-0_8
    10. Barth, A., Bürger, R., Kröker, I., & Rohde, C. (2016). Computational uncertainty quantification for a clarifier-thickener  model with several random perturbations: A hybrid stochastic Galerkin  approach. Computers & Chemical Engineering, 89, 11-- 26. http://dx.doi.org/10.1016/j.compchemeng.2016.02.016
    11. Barth, A., & Kröker, I. (2016). Finite volume methods for hyperbolic partial differential equations  with spatial noise. In Springer Proceedings in Mathematics and Statistics: Bd. submitted. Springer International Publishing.
    12. Barth, A., & Fuchs, F. G. (2016). Uncertainty quantification for hyperbolic conservation laws with  flux coefficients given by spatiotemporal random fields. SIAM J. Sci. Comput., 38(4), A2209--A2231. https://doi.org/10.1137/15M1027723
    13. Barth, A., Moreno-Bromberg, S., & Reichmann, O. (2016). A Non-stationary Model of Dividend Distribution in a Stochastic Interest-Rate  Setting. Comp. Economics, 47(3), 447--472. https://doi.org/10.1007/s10614-015-9502-y
    14. Bastian, P., Engwer, C., Fahlke, J., Geveler, M., Göddeke, D., Iliev, O., Ippisch, O., Milk, R., Mohring, J., Müthing, S., Ohlberger, M., Ribbrock, D., & Turek, S. (2016). Advances Concerning Multiscale Methods and Uncertainty Quantification  in EXA-DUNE. In H.-J. Bungartz, P. Neumann, & W. E. Nagel (Hrsg.), Software for Exascale Computing -- SPPEXA 2013--2015 (S. 25--43). Springer. https://doi.org/10.1007/978-3-319-40528-5_2
    15. Bastian, P., Engwer, C., Fahlke, J., Geveler, M., Göddeke, D., Iliev, O., Ippisch, O., Milk, R., Mohring, J., Müthing, S., Ohlberger, M., Ribbrock, D., & Turek, S. (2016). Hardware-Based Efficiency Advances in the EXA-DUNE Project. In H.-J. Bungartz, P. Neumann, & W. E. Nagel (Hrsg.), Software for Exascale Computing -- SPPEXA 2013--2015 (S. 3--23). Springer. https://doi.org/10.1007/978-3-319-40528-5_1
    16. Baur, U., Benner, P., Haasdonk, B., Himpe, C., Maier, I., & Ohlberger, M. (2016). Comparison of methods for parametric model order reduction of instationary  problems. In P. Benner, A. Cohen, M. Ohlberger, & K. Willcox (Hrsg.), Model Reduction and Approximation for Complex Systems. Birkhäuser Publishing. https://www2.mpi-magdeburg.mpg.de/preprints/2015/MPIMD15-01.pdf
    17. Betancourt, F., & Rohde, C. (2016). Finite-Volume Schemes for Friedrichs Systems with Involutions. App. Math. Comput., 272, Part 2, 420–439. https://doi.org/10.1016/j.amc.2015.03.050
    18. Bhatt, A., & Moore, B. E. (2016). Structure-preserving Exponential Runge-Kutta Methods. SIAM J. Sci Comp.
    19. Bhatt, A. (2016). Structure-preserving Finite Difference Methods for Linearly Damped  Differential Equations. University of Central Florida.
    20. Cavoretto, R., De Marchi, S., De Rossi, A., Perracchione, E., & Santin, G. (2016). Partition of unity interpolation using stable kernel-based techniques. Applied Numerical Mathematics. https://doi.org/10.1016/j.apnum.2016.07.005
    21. Cavoretto, R., De Marchi, S., De Rossi, A., Perracchione, E., & Santin, G. (2016). Approximating basins of attraction for dynamical systems via stable  radial bases. AIP Conf. Proc. https://doi.org/10.1063/1.4952177
    22. Chertock, A., Degond, P., & Neusser, J. (2016). An Asymptotic-Preserving Method for a Relaxation of the Navier-Stokes-Korteweg  Equations. Journal of Computational Physics, 335, 387–403. http://arxiv.org/abs/1512.04228
    23. Colombo, R. M., Guerra, G., & Schleper, V. (2016). The compressible to incompressible limit of 1D Euler equations: the  non-smooth case. Archive for Rational Mechanics and Analysis, 219(2), 701–718. https://doi.org/10.1007/s00205-015-0904-8
    24. Colombo, R. M., LeFloch, P. G., & Rohde, C. (2016). Hyperbolic techniques in Modelling, Analysis and Numerics. Oberwolfach Reports, 13, 1683–1751. https://doi.org/10.4171/OWR/2016/30
    25. Colombo, R. M., Guerra, G., & Schleper, V. (2016). The Compressible to Incompressible Limit of One Dimensional Euler    Equations: The Non Smooth Case. ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS, 219(2), 701–718. https://doi.org/10.1007/s00205-015-0904-8
    26. Dedner, A., & Giesselmann, J. (2016). A posteriori analysis of fully discrete method of lines DG schemes  for systems of conservation laws. SIAM J. Numer. Anal., 54(6), 3523–3549. http://epubs.siam.org/toc/sjnaam/54/6
    27. Diehl, D., Kremser, J., Kröner, D., & Rohde, C. (2016). Numerical solution of Navier-Stokes-Korteweg systems by local discontinuous Galerkin methods in multiple space dimensions. Appl. Math. Comput., 272(2), 309–335. https://doi.org/10.1016/j.amc.2015.09.080
    28. Diehl, D., Kremser, J., Kröner, D., & Rohde, C. (2016). Numerical solution of Navier-Stokes-Korteweg systems by local discontinuous Galerkin methods in multiple space dimensions. Appl. Math. Comput., 272(2), 309–335. https://doi.org/10.1016/j.amc.2015.09.080
    29. Dihlmann, M., & Haasdonk, B. (2016). A REDUCED BASIS KALMAN FILTER FOR PARAMETRIZED PARTIAL DIFFERENTIAL    EQUATIONS. ESAIM-CONTROL OPTIMISATION AND CALCULUS OF VARIATIONS, 22(3), 625–669. https://doi.org/10.1051/cocv/2015019
    30. Dragomirescu, F. I., Eisenschmidt, K., Rohde, C., & Weigand, B. (2016). Perturbation solutions for the finite radially symmetric Stefan problem. INTERNATIONAL JOURNAL OF THERMAL SCIENCES, 104, 386–395. https://doi.org/10.1016/j.ijthermalsci.2016.01.019
    31. Dragomirescu, I., Eisenschmidt, K., Rohde, C., & Weigand, B. (2016). Perturbation solutions for the finite radially symmetric Stefan problem. Inter. J. Thermal Sci., 104, 386–395. https://doi.org/10.1016/j.ijthermalsci.2016.01.019
    32. Dumbser, M., Gassner, G., Rohde, C., & Roller, S. (2016). Preface to the special issue ``Recent Advances in Numerical Methods for    Hyperbolic Partial Differential Equations’’. APPLIED MATHEMATICS AND COMPUTATION, 272(2), 235–236. https://doi.org/10.1016/j.amc.2015.11.023
    33. Düll, W.-P., Kashani, K. S., Schneider, G., & Zimmermann, D. (2016). Attractivity of the Ginzburg-Landau mode distribution for a pattern forming system with marginally stable long modes. J. Differ. Equations, 261(1), 319--339.
    34. Düll, W.-P., Hermann, A., Schneider, G., & Zimmermann, D. (2016). Justification of the 2D NLS equation for a fourth order nonlinear wave equation - quadratic resonances do not matter much in case of analytic initial conditions. J. Math. Anal. Appl., 436(2), 847--867.
    35. Düll, W.-P., Kashani, K. S., & Schneider, G. (2016). The validity of Whitham’s approximation for a Klein-Gordon-Boussinesq model. SIAM J. Math. Anal., 48(6), 4311--4334. https://doi.org/10.1137/16M1071687
    36. Düll, W.-P., Schneider, G., & Wayne, C. E. (2016). Justification of the nonlinear Schrödinger equation for the evolution of gravity driven 2D surface water waves in a canal of finite depth. Arch. Ration. Mech. Anal., 220(2), 543--602. https://doi.org/10.1007/s00205-015-0937-z
    37. Fetzer, M., & Scherer, C. W. (2016). A General Integral Quadratic Constraints Theorem with Applications to a Class of Sampled-Data Systems. SIAM J. Contr. Optim., 54(3), 1105–1125. https://doi.org/10.1137/140985482
    38. Fetzer, M., & Scherer, C. W. (2016). Stability and Performance Analysis on Sobolev Spaces. 55th IEEE Conf. Decision and Control, 7264–7269. https://doi.org/10.1109/CDC.2016.7799390
    39. Fritzen, F., Haasdonk, B., Ryckelynck, D., & Schöps, S. (2016). An algorithmic comparison of the Hyper-Reduction and the Discrete Empirical Interpolation Method for a nonlinear thermal problem [Arxiv Report]. University of Stuttgart. https://arxiv.org/abs/1610.05029
    40. Garmatter, D., Haasdonk, B., & Harrach, B. (2016). A reduced Landweber Method for Nonlinear Inverse Problems. Inverse Problems, 32(3), 1--21. http://dx.doi.org/10.1088/0266-5611/32/3/035001
    41. Gaspoz, F. D., Heine, C.-J., & Siebert, K. G. (2016). Optimal grading of the newest vertex bisection and H-1-stability of the    L-2-projection. IMA JOURNAL OF NUMERICAL ANALYSIS, 36(3), 1217–1241. https://doi.org/10.1093/imanum/drv044
    42. Geveler, M., Reuter, B., Aizinger, V., Göddeke, D., & Turek, S. (2016). Energy efficiency of the simulation of three-dimensional coastal  ocean circulation on modern commodity and mobile processors -- A  case study based on the Haswell and Cortex-A15 microarchitectures. Computer Science -- Research and Development, 31(4), 225–234. https://doi.org/10.1007/s00450-016-0324-5
    43. Giesselmann, J., & Pryer, T. (2016). Reduced relative entropy techniques for a posteriori analysis of  multiphase problems in elastodynamics. IMA J. Numer. Anal., 36(4), 1685-- 1714. http://imajna.oxfordjournals.org/content/36/4/1685
    44. Giesselmann, J. (2016). Relative entropy based error estimates for discontinuous Galerkin  schemes. Bull. Braz. Math. Soc. (N.S.), 47(1), 359--372. https://doi.org/10.1007/s00574-016-0144-z
    45. Giesselmann, J., & Pryer, T. (2016). Reduced relative entropy techniques for a priori analysis of multiphase problems in elastodynamics. BIT Numerical Mathematics, 56, 99-- 127. https://doi.org/10.1007/s10543-015-0560-2
    46. Giesselmann, J., & LeFloch, P. G. (2016). Formulation and convergence of the finite volume method for conservation  laws on spacetimes with boundary. ArXiv. http://arxiv.org/abs/1607.03944
    47. Gilg, S., Pelinovsky, D., & Schneider, G. (2016). Validity of the NLS approximation for periodic quantum graphs. NoDEA, Nonlinear Differ. Equ. Appl., 23(6), 30.
    48. Gisselmann, J., & Pryer, T. (2016). Reduced relative entropy techniques for a posteriori analysis of    multiphase problems in elastodynamics. IMA JOURNAL OF NUMERICAL ANALYSIS, 36(4), 1685–1714. https://doi.org/10.1093/imanum/drv052
    49. Gorodski, C., & Kollross, A. (2016). Some remarks on polar actions. Annals of global analysis and geometry, 49(1), 43–58. https://doi.org/10.1007/s10455-015-9479-8
    50. Guerra, G., & Schleper, V. (2016). A coupling between a 1D compressible-incompressible limit and the 1D    p-system in the non smooth case. BULLETIN OF THE BRAZILIAN MATHEMATICAL SOCIETY, 47(1), 381–396. https://doi.org/10.1007/s00574-016-0146-x
    51. Gutt, R., Kohr, M., Pintea, C., & Wendland, W. L. (2016). On the transmission problems for the Oseen and Brinkman systems on    Lipschitz domains in compact Riemannian manifolds. MATHEMATISCHE NACHRICHTEN, 289(4), 471–484. https://doi.org/10.1002/mana.201400365
    52. Hahn, B. N., & Quinto, E. T. (2016). Detectable singularities from dynamic Radon data. SIAM J. Imaging Sciences, 9(3), 1195–1225. https://doi.org/10.1137/16M1057917
    53. Hahn, B. N. (2016). Null space and resolution in dynamic computerized tomography. Inverse Problems, 32(2), 025006. https://doi.org/10.1088/0266-5611/32/2/025006
    54. Hang, H., Feng, Y., Steinwart, I., & Suykens, J. A. K. (2016). Learning theory estimates with observations from general stationary stochastic processes. Neural Computation, 28, 2853--2889. https://doi.org/10.1162/NECO_a_00870
    55. Harbrecht, H., Wendland, W. L., & Zorii, N. (2016). Rapid solution of minimal Riesz energy problems. Numer. Methods Partial Diff. Equ., 32, 1535–1552.
    56. Holicki, T., & Scherer, C. W. (2016). Controller synthesis for distributed systems over undirected graphs. 55th IEEE Conf. Decision and Control, 5238–5244. https://doi.org/10.1109/CDC.2016.7799071
    57. Hänel, A., & Weidl, T. (2016). Eigenvalue asymptotics for an elastic strip and an elastic plate with a crack. Quarterly Journal of Mechanics and Applied Mathematics, 69(4), 319–352. https://doi.org/10.1093/qjmam/hbw009
    58. Kabil, B., & Rohde, C. (2016). Persistence of undercompressive phase boundaries for isothermal Euler equations including configurational forces and surface tension. Math. Meth. Appl. Sci., 39(18), 5409--5426. https://doi.org/10.1002/mma.3926
    59. Kabil, B., & Rodrigues, M. (2016). Spectral validation of the Whitham equations for periodic waves of  lattice dynamical systems. Journal of Differential Equations, 260(3), 2994–3028. https://doi.org/10.1016/j.jde.2015.10.025
    60. Kohr, M., de Cristoforis, L., Mikhailov, S., & Wendland, W. L. (2016). Integral potential method for transmission problem with Lipschitz interface in R3 for the Stokes and Darcy-Forchheimer-Brinkman PED systems. ZAMP, 67:116, 1–30.
    61. Kohr, M., Lanza de Cristoforis, M., & Wendland, W. L. (2016). On the Robin transmission boundary value problem for the nonlinear  Darcy-Forchheimer-Brinkman and Navier-Stokes system. J. Math. Fluid Mechanics, 18, 293–329.
    62. Kohr, M., Pintea, C., & Wendland, W. L. (2016). Poisson transmission problems for L^infty perturbations of the Stokes  system on Lipschitz domains on compact Riemannian manifolds. J. Dyn. Diff. Equations, DOI 110.1007/s10884-014-9359-0.
    63. Kohr, M., Mikhailov, S. E., & Wendland, W. L. (2016). Transmission problems for the Navier-Stokes and Darcy-Forchheimer-Brinkman  systems in Lipschitz domains on compact Riemannian manifolds. Journal of Mathematical Fluid Dynamics, DOI 10.1007/s 00021-16-0273-6.
    64. Kohr, M., de Cristoforis, M. L., & Wendland, W. L. (2016). On the Robin-Transmission Boundary Value Problems for the Nonlinear    Darcy-Forchheimer-Brinkman and Navier-Stokes Systems. JOURNAL OF MATHEMATICAL FLUID MECHANICS, 18(2), 293–329. https://doi.org/10.1007/s00021-015-0236-3
    65. Köppel, M., & Rohde, C. (2016). Uncertainty Quantification for Two-Phase Flow in Heterogeneous Porous  Media. PAMM Proc. Appl. Math. Mech., 16(1), 749–750. https://doi.org/10.1002/pamm.201610363
    66. List, F., & Radu, F. A. (2016). A study on iterative methods for solving Richards’ equation. COMPUTATIONAL GEOSCIENCES, 20(2), 341–353. https://doi.org/10.1007/s10596-016-9566-3
    67. Magiera, J., Rohde, C., & Rybak, I. (2016). A hyperbolic-elliptic model problem for coupled surface-subsurface  flow. Transp. Porous Media, 114, 425–455. https://doi.org/10.1007/S11242-015-0548-Z
    68. Meister, M., & Steinwart, I. (2016). Optimal Learning Rates for Localized SVMs. J. Mach. Learn. Res., 17, 1–44.
    69. Nguyen Tien, H., Scherer, C. W., Scherpen, J. M. A., & Müller, V. (2016). Linear Parameter Varying Control of Doubly Fed Induction Machines. IEEE Trans. Ind. Electron., 63(1), 216–224. https://doi.org/10.1109/TIE.2015.2465895
    70. Ostrowski, L., Ziegler, B., & Rauhut, G. (2016). Tensor decomposition in potential energy surface representations. The Journal of Chemical Physics, 145(10), 104103. https://doi.org/10.1063/1.4962368
    71. Redeker, M., Pop, I. S., & Rohde, C. (2016). Upscaling of a Tri-Phase Phase-Field Model for Precipitation in Porous  Media. IMA J. Appl. Math., 81(5), 898–939. https://doi.org/10.1093/imamat/hxw023
    72. Redeker, M., & Haasdonk, B. (2016). A POD-EIM reduced two-scale model for precipitation in porous media. MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS, 20(4), 323–344. https://doi.org/10.1080/13873954.2016.1198384
    73. Rossi, E., & Schleper, V. (2016). Convergence of a numerical scheme for a mixed hyperbolic-parabolic  system in two space dimensions. ESAIM Math. Model. Numer. An., 50(2), 475–497. https://doi.org/10.1051/m2an/2015050
    74. Rybak, I., & Magiera, J. (2016). Decoupled schemes for free flow and porous medium systems. In T. D. et al. (Hrsg.), Domain Decomposition Methods in Science and Engineering XXII (Bd. 104, S. 613--621). Springer. https://doi.org/10.1007/978-3-319-18827-0\_54
    75. Santin, G. (2016). Approximation in kernel-based spaces, optimal subspaces and approximation  of eigenfunction [Doctoral School in Mathematical Sciences, University of Padova]. http://paduaresearch.cab.unipd.it/9186/
    76. Santin, G., & Schaback, R. (2016). Approximation of eigenfunctions in kernel-based spaces. ADVANCES IN COMPUTATIONAL MATHEMATICS, 42(4), 973–993. https://doi.org/10.1007/s10444-015-9449-5
    77. Scherer, C. W. (2016). Lossless $H_ınfty$-synthesis for 2D systems (special issue JCW). Syst. Control Lett., 95, 25–35. https://doi.org/10.1016/j.sysconle.2016.02.011
    78. Schleper, V. (2016). A HLL-type Riemann solver for two-phase flow with surface forces  and phase transitions. Appl. Numer. Math., 108, 256–270. https://doi.org/10.1016/j.apnum.2015.12.010
    79. Schmidt, A., & Haasdonk, B. (2016). Reduced basis method for H2 optimal feedback control problems. IFAC-PapersOnLine, 49(8), 327–332. http://dx.doi.org/10.1016/j.ifacol.2016.07.462
    80. Schneider, G. (2016). Validity and non-validity of the nonlinear Schrödinger equation as a model for water waves. In Lectures on the theory of water waves. Papers from the talks given at the Isaac Newton Institute for Mathematical Sciences, Cambridge, UK, July -- August, 2014 (S. 121--139). Cambridge: Cambridge University Press.
    81. Sharanya, V., Sekhar, G. P. R., & Rohde, C. (2016). Bed of polydisperse viscous spherical drops under thermocapillary    effects. ZEITSCHRIFT FUR ANGEWANDTE MATHEMATIK UND PHYSIK, 67(4), Article 4. https://doi.org/10.1007/s00033-016-0699-y
    82. Stein, A. (2016). Exakte Simulation von Optionspreisen und Sensitivitäten unter  stochastischer Volatilität [Master Thesis].
    83. Steinwart, I., Thomann, P., & Schmid, N. (2016). Learning with Hierarchical Gaussian Kernels. Fakultät für Mathematik und Physik, Universität Stuttgart.
    84. Trottemant, E. J., Scherer, C. W., & Mazo, M. (2016). Optimality of robust disturbance-feedback strategies. Int. J. Robust Nonlin., 26(7), 1475–1488. https://doi.org/10.1002/rnc.3360
    85. Trottemant, E. J., Mazo, M., & Scherer, C. W. (2016). Synthesis of Robust Piecewise Affine Output-Feedback Strategies. J. Guid. Control Dynam., 39(7), 1461–1469. https://doi.org/10.2514/1.G001343
    86. Veenman, J., Scherer, C. W., & Köroglu, H. (2016). Robust stability and performance analysis with integral quadratic constraints. Eur. J. Control, 31, 1–32. https://doi.org/10.1016/j.ejcon.2016.04.004
    87. Veenman, J., Lahr, M., & Scherer, C. W. (2016). Robust controller synthesis with unstable weights. 55th IEEE Conf. Decision and Control, 2390–2395. https://doi.org/10.1109/CDC.2016.7798620
  9. 2015

    1. Allerhand, L. I., & Shaked, U. (2015). Soft Controller Switching with Guaranteed $H_ınfty$ Performance. IFAC-PapersOnLine, 48(11), 848–853. https://doi.org/10.1016/j.ifacol.2015.09.296
    2. Allerhand, L. I., Gershon, E., & Shaked, U. (2015). State-feedback Control of Stochastic Discrete-time Linear Switched Systems with Dwell Time. Eur. Control Conf., 452–457. https://doi.org/10.1109/ECC.2015.7330585
    3. Allerhand, L. I. (2015). Stability of adaptive control in the presence of input disturbances and $H_ınfty$ performance. IFAC-PapersOnLine, 48(14), 76–81. https://doi.org/10.1016/j.ifacol.2015.09.437
    4. Amsallem, D., & Haasdonk, B. (2015). PEBL-ROM: Projection-Error Based Local Reduced-Order Models [SimTech Preprint]. University of Stuttgart. http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1436
    5. Amsallem, D., Farhat, C., & Haasdonk, B. (2015). Editorial: Special Issue on Model Reduction. IJNME, International Journal of Numerical Methods in Engineering, 102(5), 931--932. https://doi.org/10.1002/nme.4889
    6. Amsallem, D., Farhat, C., & Haasdonk, B. (2015). Special Issue on Model Reduction. IJNME, International Journal of Numerical Methods in Engineering, 102(5), 931--932. https://doi.org/10.1002/nme.4889
    7. Burkovska, O., Haasdonk, B., Salomon, J., & Wohlmuth, B. (2015). Reduced basis methods for pricing options with the Black-Scholes and Heston model. SIAM journal on Financial Mathematics (SIFIN), 6(1), 685--712. https://doi.org/10.1137/140981216
    8. Cavoretto, R., De Marchi, S., De Rossi, A., Perracchione, E., & Santin, G. (2015). RBF approximation of large datasets by partition of unity and local  stabilization. In J. Vigo-Aguiar (Hrsg.), CMMSE 2015 : Proceedings of the 15th International Conference on  Mathematical Methods in Science and Engineering (S. 317--326).
    9. Chirilus-Bruckner, M., Düll, W.-P., & Schneider, G. (2015). NLS approximation of time oscillatory long waves for equations with quasilinear quadratic terms. Math. Nachr., 288(2–3), 158--166. https://doi.org/10.1002/mana.201200325
    10. De Marchi, S., & Santin, G. (2015). Fast computation of orthonormal basis for RBF spaces through Krylov  space methods. BIT Numerical Mathematics, 55(4), 949--966. https://doi.org/10.1007/s10543-014-0537-6
    11. Dihlmann, M., & Haasdonk, B. (2015). A reduced basis Kalman filter for parametrized partial differential  equations. ESAIM: Control, Optimisation and Calculus of Variations. https://doi.org/10.1051/cocv/2015019
    12. Dihlmann, M. A., & Haasdonk, B. (2015). Certified PDE-constrained parameter optimization using reduced basis surrogate models for evolution problems. COAP, Computational Optimization and Applications, 60(3), 753--787. https://doi.org/DOI: 10.1007/s10589-014-9697-1
    13. do Nascimento, W. N., & Wirth, J. (2015). Wave equations with mass and dissipation. Adv. Differential Equations, 20(7–8), 661--696. http://projecteuclid.org/euclid.ade/1431115712
    14. Garmatter, D., Haasdonk, B., & Harrach, B. (2015). A reduced Landweber Method for Nonlinear Inverse Problems. University of Stuttgart.
    15. Geck, M., & Bonnafe, C. (2015). Hecke algebras with unequal parameters and Vogan’s left cell invariants. Representations of reductive groups. In Honor of the 60th birthday of David A. Vogan, Jr (eds. M. Nevins and P. Trapa), 312, 173--188. https://doi.org/10.1007/978-3-319-23443-4_6
    16. Geck, M. (2015). On Kottwitz’ conjecture for twisted involutions. Journal of Lie Theory, 25(2), 395–429. https://www.heldermann.de/JLT/JLT25/JLT252/jlt25019.htm
    17. Geck, M., & Halls, A. (2015). On the Kazhdan-Lusztig cells in type E8. Mathematics of Computation, 84(296), 3029--3049. https://doi.org/10.1090/mcom/2963
    18. Geck, M. (2015). Eigenvalues of Real Symmetric Matrices. The American Mathematical Monthly, 122(5), 482. https://doi.org/10.4169/amer.math.monthly.122.5.482
    19. Gershon, E., Shaked, U., & Allerhand, L. I. (2015). Stochastic Linear Systems: Robust $H_ınfty$ Control via Vertex-dependent Approach. 23rd Med. Conf. Control and Automation, 638–643. https://doi.org/10.1109/MED.2015.7158818
    20. Gerth, D., Hahn, B. N., & Ramlau, R. (2015). The method of the approximate inverse for atmospheric tomography. Inverse Problems, 31(6), 065002. https://doi.org/10.1088/0266-5611/31/6/065002
    21. Giesselmann, J. (2015). Entropy as a fundamental principle in hyperbolic conservation laws and related models [Habilitationsschrift].
    22. Giesselmann, J., & Pryer, T. (2015). ENERGY CONSISTENT DISCONTINUOUS GALERKIN METHODS FOR A    QUASI-INCOMPRESSIBLE DIFFUSE TWO PHASE FLOW MODEL. ESAIM-MATHEMATICAL MODELLING AND NUMERICAL ANALYSIS-MODELISATION  MATHEMATIQUE ET ANALYSE NUMERIQUE, 49(1), 275–301. https://doi.org/10.1051/m2an/2014033
    23. Giesselmann, J. (2015). Low Mach asymptotic preserving scheme for the Euler-Korteweg model. IMA J. Numer. Anal., 35(2), 802--832. https://doi.org/10.1093/imanum/dru022
    24. Giesselmann, J. (2015). Relative entropy in multi-phase models of 1d elastodynamics: Convergence    of a non-local to a local model. JOURNAL OF DIFFERENTIAL EQUATIONS, 258(10), 3589–3606. https://doi.org/10.1016/j.jde.2015.01.047
    25. Giesselmann, J., Makridakis, C., & Pryer, T. (2015). A posteriori analysis of discontinuous Galerkin schemes for systems  of hyperbolic conservation laws. SIAM J. Numer. Anal., 53, 1280--1303. http://dx.doi.org/10.1137/140970999
    26. Grosan, T., Kohr, M., & Wendland, W. L. (2015). Dirichlet problem for a nonlinear generalized Darcy-Forchheimer-Brinkman  system in Lipschitz domains. Math. Meth. Appl. Sciences, 38, 3615–3628. https://doi.org/10.1002/mma3302
    27. Gugat, M., Herty, M., & Schleper, V. (2015). flow control in gas networks: exact controllability to a given demand    (vol 34, pg 745, 2011). MATHEMATICAL METHODS IN THE APPLIED SCIENCES, 38(5), 1001–1004. https://doi.org/10.1002/mma.3122
    28. Göddeke, D., Altenbernd, M., & Ribbrock, D. (2015). Fault-tolerant finite-element multigrid algorithms with hierarchically  compressed asynchronous checkpointing. Parallel Computing, 49, 117–135. https://doi.org/10.1016/j.parco.2015.07.003
    29. Hahn, B. N. (2015). Dynamic linear inverse problems with moderate movements of the object: Ill-posedness and regularization. Inverse Problems & Imaging, 9(2), 395–413. https://doi.org/10.3934/ipi.2015.9.395
    30. Hintermüller, M., & Langer, A. (2015). Non-overlapping domain decomposition methods for dual total variation  based image denoising. Journal of Scientific Computing, 62(2), 456--481. http://link.springer.com/article/10.1007/s10915-014-9863-8
    31. Hänel, A. (2015). Singular problems in quantum and elastic waveguides via Dirichlet-to-Neumann analysis. [Dissertation]. Universität Stuttgart.
    32. Höllig, K., & Hörner, J. (2015). Programming finite element methods with weighted B-splines. Computers & Mathematics with Applications, 70(7), 1441--1456. https://doi.org/10.1016/j.camwa.2015.02.019
    33. Kaulmann, S., Flemisch, B., Haasdonk, B., Lie, K. A., & Ohlberger, M. (2015). The localized reduced basis multiscale method for two-phase flows in    porous media. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 102(5, SI), 1018–1040. https://doi.org/10.1002/nme.4773
    34. Kissling, F., & Rohde, C. (2015). The Computation of Nonclassical Shock Waves in Porous Media with  a Heterogeneous Multiscale Method: The Multidimensional Case. Multiscale Model. Simul., 13 Nr. 4, 1507–1541. https://doi.org/10.1137/120899236
    35. Kohr, M., Lanza de Cristoforis, M., & Wendland, W. L. (2015). Poisson problems for semilinear Brinkman systems on Lipschitz domains  in R^3. ZAMP, 66, 833–846.
    36. Kohr, M., Pintea, C., & Wendland, W. L. (2015). Poisson-Transmission Problems for -Perturbations of the Stokes System on    Lipschitz Domains in Compact Riemannian Manifolds. JOURNAL OF DYNAMICS AND DIFFERENTIAL EQUATIONS, 27(3–4), 823–839. https://doi.org/10.1007/s10884-014-9359-0
    37. Kohr, M., de Cristoforis, M. L., & Wendland, W. L. (2015). Poisson problems for semilinear Brinkman systems on Lipschitz domains in    R-n. ZEITSCHRIFT FUR ANGEWANDTE MATHEMATIK UND PHYSIK, 66(3), 833–864. https://doi.org/10.1007/s00033-014-0439-0
    38. Kovarik, H., & Weidl, T. (2015). Improved Berezin-Li-Yau inequalities with magnetic field. In Proceedings of the Royal Society Of Edinburgh. Section A, Mathematics (Nr. 1; Bd. 145, Nummer 1, S. 145–160). Cambridge Univ. Press. https://doi.org/10.1017/S0308210513001595
    39. Kroeker, I., Nowak, W., & Rohde, C. (2015). A stochastically and spatially adaptive parallel scheme for uncertain    and nonlinear two-phase flow problems. COMPUTATIONAL GEOSCIENCES, 19(2), 269–284. https://doi.org/10.1007/s10596-014-9464-5
    40. Kröker, I., Nowak, W., & Rohde, C. (2015). A stochastically and spatially adaptive parallel scheme for uncertain  and nonlinear two-phase flow problems. Comput. Geosci., 19(2), 269--284. https://doi.org/10.1007/s10596-014-9464-5
    41. 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/
    42. Köroglu, H., Scherer, C. W., & Falcone, P. (2015). Robust Static Output Feedback Synthesis under an Integral Quadratic Constraint on the States. Eur. Control Conf., 3203–3208. https://doi.org/10.1109/ECC.2015.7331027
    43. List, F., & Radu, F. A. (2015). A study on iterative methods for solving Richards’ equation. http://www.nupus.uni-stuttgart.de/07_Preprints_Publications/Preprints/Preprints-PDFs/Preprint_201506.pdf
    44. Martini, I., & Haasdonk, B. (2015). Output Error Bounds for the Dirichlet-Neumann Reduced Basis Method. Numerical Mathematics and Advanced Applications - ENUMATH 2013, 103, 437--445. https://doi.org/10.1007/978-3-319-10705-9_43
    45. Martini, I., Rozza, G., & Haasdonk, B. (2015). Reduced basis approximation and a-posteriori error estimation for the coupled Stokes-Darcy system. Advances in Computational Mathematics, 41(5), 1131--1157. https://doi.org/10.1007/s10444-014-9396-6
    46. Micula, S., & Wendland, W. L. (2015). Trigonometric collocation for nonlinear Riemann-Hilbert problems  in doubly connected domains. IMA J. Num. Analysis, 35, 834–858.
    47. Micula, S., & Wendland, W. L. (2015). Trigonometric collocation for nonlinear Riemann-Hilbert problems on    doubly connected domains. IMA JOURNAL OF NUMERICAL ANALYSIS, 35(2), 834–858. https://doi.org/10.1093/imanum/dru009
    48. Missler, J., Schwarzmann, D., & Allerhand, L. I. (2015). On the Influence of Filter Choice in Output-Feedback MRAC during Adaptation Transients. IFAC-PapersOnLine, 48(11), 505–510. https://doi.org/10.1016/j.ifacol.2015.09.236
    49. Müthing, S., Ribbrock, D., & Göddeke, D. (2015). Integrating multi-threading and accelerators into DUNE-ISTL. In A. Abdulle, S. Deparis, D. Kressner, F. Nobile, & M. Picasso (Hrsg.), Numerical Mathematics and Advanced Applications -- ENUMATH 2013 (Bd. 103, S. 601--609). Springer. https://doi.org/10.1007/978-3-319-10705-9_59
    50. Neusser, J., Rohde, C., & Schleper, V. (2015). Relaxation of the Navier-Stokes-Korteweg Equations for Compressible  Two-Phase Flow with Phase Transition. J. Numer. Methods Fluids, 79, 615–639. https://doi.org/10.1002/fld.4065
    51. Neusser, J., Rohde, C., & Schleper, V. (2015). Relaxed Navier-Stokes-Korteweg Equations for compressible two-phase  flow with phase transition. J. Numer. Meth. Fluids, 79(12), 615–639. https://doi.org/10.1002/fld.4065
    52. Neusser, J., & Schleper, V. (2015). Numerical schemes for the coupling of compressible and incompressible  fluids in several space dimensions.
    53. Oztepe, G. S., Choudhury, S. R., & Bhatt, A. (2015). Multiple Scales and Energy Analysis of Coupled Rayleigh-Van der Pol  Oscillators with Time-Delayed Displacement and Velocity Feedback:  Hopf Bifurcations and Amplitude Death. Far East Journal of Dynamical Systems. https://doi.org/10.17654/FJDSMar2015_031_059
    54. Redeker, M., & Haasdonk, B. (2015). A POD-EIM reduced two-scale model for precipitation in porous media [SimTech Preprint]. University of Stuttgart. http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=964
    55. Redeker, M., & Haasdonk, B. (2015). A POD-EIM reduced two-scale model for crystal growth. Advances in Computational Mathematics, 41(5), 987--1013. https://doi.org/10.1007/s10444-014-9367-y
    56. Rohde, C., & Zeiler, C. (2015). A relaxation Riemann solver for compressible two-phase flow with phase    transition and surface tension. APPLIED NUMERICAL MATHEMATICS, 95(SI), 267–279. https://doi.org/10.1016/j.apnum.2014.05.001
    57. Ruzhansky, M., & Wirth, J. (2015). L-p Fourier multipliers on compact Lie groups. Math. Z., 280(3–4), 621--642. https://doi.org/10.1007/s00209-015-1440-9
    58. Rybak, I. V., Gray, W. G., & Miller, C. T. (2015). Modeling two-fluid-phase flow and species transport in porous media. J. Hydrology, 521, 565--581. https://doi.org/10.1016/j.jhydrol.2014.11.051
    59. Rybak, I., Magiera, J., Helmig, R., & Rohde, C. (2015). Multirate time integration for coupled saturated/unsaturated porous medium and free flow systems. Comput. Geosci., 19, 299–309. https://doi.org/10.1007/s10596-015-9469-8
    60. Scherer, C. W. (2015). Gain-scheduling control with dynamic multipliers by convex optimization. SIAM J. Contr. Optim., 53(3), 1224–1249. https://doi.org/10.1137/140985871
    61. Schleper, V. (2015). A hybrid model for traffic flow and crowd dynamics with random individual  properties. Math. Biosci. Eng., 12(2), 393–413. https://doi.org/10.3934/mbe.2015.12.393
    62. Schleper, V. (2015). Nonlinear Transport and Coupling of Conservation Laws.
    63. Schmidt, A., Dihlmann, M., & Haasdonk, B. (2015). Basis generation approaches for a reduced basis linear quadratic regulator. Proc. MATHMOD 2015 - 8th Vienna International Conference on Mathematical Modelling, 713--718. https://doi.org/10.1016/j.ifacol.2015.05.016
    64. Schmidt, A., & Haasdonk, B. (2015). Reduced basis method for $H_2$ optimal feedback control problems. University of Stuttgart. http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1442
    65. Schmidt, A., & Haasdonk, B. (2015). Reduced Basis Approximation of Large Scale Algebraic Riccati Equations. University of Stuttgart.
    66. Steinwart, I. (2015). Supplement B to ``Fully Adaptive Density-Based Clustering’’. Fakultät für Mathematik und Physik, Universität Stuttgart. https://doi.org/10.1214/15-AOS1331SUPP
    67. Steinwart, I. (2015). Fully Adaptive Density-Based Clustering. Ann. Statist., 43, 2132--2167. https://doi.org/10.1214/15-AOS1331
    68. Steinwart, I. (2015). Supplement A to ``Fully Adaptive Density-Based Clustering’’ (Nr. 2013–016; Nummern 2013–016). Fakultät für Mathematik und Physik, Universität Stuttgart. https://doi.org/10.1214/15-AOS1331SUPP
    69. Steinwart, I. (2015). Measuring the capacity of sets of functions in the analysis of ERM. In A. Gammerman & V. Vovk (Hrsg.), Festschrift in Honor of Alexey Chervonenkis (S. 223--239). Springer. https://doi.org/10.1007/978-3-642-41136-6
    70. Thomann, P., Steinwart, I., & Schmid, N. (2015). Towards an Axiomatic Approach to Hierarchical Clustering of Measures. J. Mach. Learn. Res., 16, 1949--2002.
    71. Veenman, J. (2015). A general framework for robust analysis and control: an integral quadratic constraint based approach [Dissertation, Logos Verlag, Berlin]. http://www.logos-verlag.de/cgi-bin/engbuchmid?isbn=3963&lng=eng&id=
    72. Wirth, J. (2015). Diffusion phenomena for partially dissipative hyperbolic              systems. In Nonlinear dynamics in partial differential equations (Bd. 64, S. 303--310). Math. Soc. Japan, Tokyo.
    73. Wirtz, D., Karajan, N., & Haasdonk, B. (2015). Surrogate modeling of multiscale models using kernel methods. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 101(1), 1–28. https://doi.org/10.1002/nme.4767
    74. Wirtz, D., Karajan, N., & Haasdonk, B. (2015). Surrogate Modelling of multiscale models using kernel methods. International Journal of Numerical Methods in Engineering, 101(1), 1–28. https://doi.org/10.1002/nme.4767
    75. Zeiler, C. (2015). Liquid Vapor Phase Transitions: Modeling, Riemann Solvers and Computation [Verlag Dr. Hut]. http://elib.uni-stuttgart.de/handle/11682/8919%7D
  10. 2014

    1. Adibi, H., & Minbashian, H. (2014). Integral Equations (in Persian). Amirkabir University of Technology Press.
    2. Aki, G. L., Dreyer, W., Giesselmann, J., & Kraus, C. (2014). A quasi-incompressible diffuse interface model with phase transition. Math. Models Methods Appl. Sci., 24(5), 827–861. https://doi.org/10.1142/S0218202513500693
    3. Apprich, C., Höllig, K., Hörner, J., Keller, A., & Yazdani, E. N. (2014). Finite Element Approximation with Hierarchical B-Splines. In J.-D. Boissonnat, A. Cohen, O. Gibaru, C. Gout, T. Lyche, M.-L. Mazure, & L. L. Schumaker (Hrsg.), Curves and Surfaces (Bd. 9213, S. 1–15). Springer. http://dblp.uni-trier.de/db/conf/cas/cas2014.html#ApprichHHKY14
    4. Armiti-Juber, A., & Rohde, C. (2014). Almost Parallel Flows in Porous Media. In J. Fuhrmann, M. Ohlberger, & C. Rohde (Hrsg.), Finite Volumes for Complex Applications VII-Elliptic, Parabolic and Hyperbolic Problems (Bd. 78, S. 873–881). Springer International Publishing. https://doi.org/10.1007/978-3-319-05591-6_88
    5. Barth, A., & Moreno-Bromberg, S. (2014). Optimal risk and liquidity management with costly refinancing opportunities. Insurance Math. Econom., 57, 31--45. https://doi.org/10.1016/j.insmatheco.2014.05.001
    6. Barth, A., & Benth, F. E. (2014). The forward dynamics in energy markets -- infinite-dimensional modelling  and simulation. Stochastics, 86(6), 932--966. https://doi.org/10.1080/17442508.2014.895359
    7. Bastian, P., Engwer, C., Göddeke, D., Iliev, O., Ippisch, O., Ohlberger, M., Turek, S., Fahlke, J., Kaulmann, S., Müthing, S., & Ribbrock, D. (2014). EXA-DUNE: Flexible PDE Solvers, Numerical Methods and Applications. In L. Lopes, J. Zilinskas, A. Costan, RobertoG. Cascella, G. Kecskemeti, E. Jeannot, M. Cannataro, L. Ricci, S. Benkner, S. Petit, V. Scarano, J. Gracia, S. Hunold, StephenL. Scott, S. Lankes, C. Lengauer, J. Carretero, J. Breitbart, & M. Alexander (Hrsg.), Euro-Par 2014: Parallel Processing Workshops (Bd. 8806, S. 530--541). Springer. https://doi.org/10.1007/978-3-319-14313-2_45
    8. Bonnafé, C., & Geck, M. (2014). Conjugacy classes of involutions and Kazhdan–Lusztig cells. Representation Theory of the American Mathematical Society, 18(6), 155--182. https://doi.org/10.1090/s1088-4165-2014-00456-4
    9. Burkovska, O., Haasdonk, B., Salomon, J., & Wohlmuth, B. (2014). Reduced basis methods for pricing options with the Black-Scholes and Heston model. SIAM Journal on Financial Mathematics, 6, 685--712. https://doi.org/10.1137/140981216
    10. Bürger, R., Kröker, I., & Rohde, C. (2014). A hybrid stochastic Galerkin method for uncertainty quantification applied to a conservation law modelling a clarifier-thickener unit. ZAMM Z. Angew. Math. Mech., 94(10), 793–817. https://doi.org/10.1002/zamm.201200174
    11. Chalons, C., Engel, P., & Rohde, C. (2014). A Conservative and Convergent Scheme for Undercompressive Shock Waves. SIAM J. Numer. Anal., 52(1), 554–579. http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=732
    12. Corli, A., Rohde, C., & Schleper, V. (2014). Parabolic approximations of diffusive-dispersive equations. J. Math. Anal. Appl., 414, 773–798. http://dx.doi.org/10.1016/j.jmaa.2014.01.049
    13. Cruz-Uribe, D., Fiorenza, A., Ruzhansky, M., & Wirth, J. (2014). Variable Lebesgue spaces and hyperbolic systems. In Advanced Courses in Mathematics. CRM Barcelona (S. x+169). Birkhäuser/Springer, Basel.
    14. Dihlmann, M., & Haasdonk, B. (2014). A reduced basis Kalman filter for parametrized partial differential equations. University of Stuttgart.
    15. Dreyer, W., Giesselmann, J., & Kraus, C. (2014). A compressible mixture model with phase transition. Physica D, 273–274, 1–13. http://dx.doi.org/10.1016/j.physd.2014.01.006
    16. Dreyer, W., Giesselmann, J., & Kraus, C. (2014). Modeling of compressible electrolytes with phase transition. http://arxiv.org/abs/1405.6625
    17. Ehlers, W., Helmig, R., & Rohde, C. (2014). Editorial: Deformation and transport phenomena in porous media. ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, 94(7–8), 559--559. https://doi.org/10.1002/zamm.201400559
    18. Engel, P., Viorel, A., & Rohde, C. (2014). A Low-Order Approximation for Viscous-Capillary Phase Transition  Dynamics. Port. Math., 70(4), 319–344. http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=723
    19. Eymard, R., & Schleper, V. (2014). Study of a numerical scheme for miscible two-phase flow in porous  media. Numer. Meth. Part. D. E., 30, 723–748. https://doi.org/10.1002/num.21823
    20. Fechter, S., Zeiler, C., Munz, C.-D., & Rohde, C. (2014). Simulation of compressible multi-phase flows at extreme ambient conditions using a Discontinuous-Galerkin method. ILASS Europe, 26th European Conference on Liquid Atomization and Spray Systems.
    21. Finite Volumes for Complex Applications VII Elliptic, Parabolic and  Hyperbolic Problems, FVCA 7, Berlin, June 2014. (2014). In J. Fuhrmann, M. Ohlberger, & C. Rohde (Hrsg.), Springer Proceedings in Mathematics & Statistics: Bd. Vol. 77/78.
    22. Garikapati, H. (2014). A PGD Based Preconditioner for Scalar Elliptic Problems.
    23. Gaspoz, F. D., & Morin, P. (2014). Approximation classes for adaptive higher order finite element approximation. Math. Comp., 83(289), 2127--2160. https://doi.org/10.1090/S0025-5718-2013-02777-9
    24. Geck, M. (2014). On the Characterization of Galois Extensions. The American Mathematical Monthly, 121(7), 637. https://doi.org/10.4169/amer.math.monthly.121.07.637
    25. Geck, M. (2014). Algebra: Gruppen, Ringe, Körper. Mit einer Einführung in die Darstellungstheorie endlicher Gruppen. edition delkhofen.
    26. Geck, M. (2014). Kazhdan-Lusztig cells and the Frobenius-Schur indicator. Journal of Algebra, 398, 329--342. https://doi.org/10.1016/j.jalgebra.2013.01.019
    27. Giesselmann, J., & Tzavaras, A. E. (2014). Singular Limiting Induced from Continuum Solutions and the Problem  of Dynamic Cavitation. Arch. Ration. Mech. Anal., 212(1), 241–281. https://doi.org/10.1007/s00205-013-0677-x
    28. Giesselmann, J., & M�ller, T. (2014). Estimating the Geometric Error of Finite Volume Schemes for Conservation  Laws on Surfaces for generic numerical flux functions. In M. O. J. Fuhrmann & C. Rohde (Hrsg.), Finite Volumes for Complex Applications VII-Methods and Theoretical  Aspects (Bd. 77).
    29. Giesselmann, J., & Tzavaras, A. E. (2014). On cavitation in elastodynamics. In F. Ancona, A. Bressan, P. Marcati, & A. Marson (Hrsg.), Hyperbolic Problems: Theory, Numerics, Applications (S. 599–606). AIMS. https://aimsciences.org/books/am/AMVol8.html
    30. Giesselmann, J., Makridakis, C., & Pryer, T. (2014). Energy consistent DG methods for the Navier-Stokes-Korteweg system. Math. Comp., 83, 2071-- 2099. http://dx.doi.org/10.1090/S0025-5718-2014-02792-0
    31. Giesselmann, J., & M�ller, T. (2014). Geometric error of finite volume schemes for conservation laws on  evolving surfaces. Numer. Math., 128(3), 489�516. https://doi.org/10.1007/s00211-014-0621-5
    32. Giesselmann, J., & Pryer, T. (2014). On aposteriori error analysis of DG schemes approximating hyperbolic  conservation laws. In M. O. J. Fuhrmann & C. Rohde (Hrsg.), Finite Volumes for Complex Applications VII-Methods and Theoretical  Aspects (Bd. 77).
    33. Giesselmann, J. (2014). A Relative Entropy Approach to Convergence of a Low Order Approximation  to a Nonlinear Elasticity Model with Viscosity and Capillarity. SIAM J. Math. Anal., 46(5), 3518--3539. https://doi.org/10.1137/140951710
    34. Göddeke, D., Komatitsch, D., & Möller, M. (2014). Finite and Spectral Element Methods on Unstructured Grids for Flow  and Wave Propagation Methods. In V. Kindratenko (Hrsg.), Numerical Computations with GPUs (S. 183--206). Springer. https://doi.org/10.1007/978-3-319-06548-9_9
    35. Haasdonk, B. (2014). Reduced Basis Methods for Parametrized PDEs -- A Tutorial Introduction  for Stationary and Instationary Problems [SimTech Preprint]. IANS, University of Stuttgart, Germany. http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=938
    36. Haasdonk, B., & Ohlberger, M. (2014). Wenn die Probleme zahlreicher werden: Reduzierte Basis Methoden  f�r effiziente und gesicherte numerische Simulation. GAMM Rundbrief, 2014(1), 6–13.
    37. Haasdonk, B., & Ohlberger, M. (2014). Wenn die Probleme zahlreicher werden: Reduzierte Basis Methoden für effiziente und gesicherte numerische Simulation. GAMM Rundbrief, 2014(1), 6–13.
    38. Hahn, B. N. (2014). Reconstruction of dynamic objects with affine deformations in computerized tomography. Journal of Inverse and Ill-posed Problems, 22(3), 323–339. https://doi.org/10.1515/jip-2012-0094
    39. Hahn, B. N. (2014). Efficient algorithms for linear dynamic inverse problems with known motion. Inverse Problems, 30(3), 035008. https://doi.org/10.1088/0266-5611/30/3/035008
    40. Hang, H., & Steinwart, I. (2014). Fast Learning from $\alpha$-mixing Observations. J. Multivariate Anal., 127, 184--199. https://doi.org/10.1016/j.jmva.2014.02.012
    41. Harbrecht, H., Wendland, W. L., & Zorii, N. (2014). Riesz minimal energy problems on C^k-1,1 manifolds. Math. Nachr., 287, 48–69.
    42. Hintermüller, M., & Langer, A. (2014). Adaptive Regularization for Parseval Frames in Image Processing. SFB-Report No. 2014-014. http://people.ricam.oeaw.ac.at/a.langer/publications/SFB-Report-2014-014.pdf
    43. Hintermüller, M., & Langer, A. (2014). Surrogate Functional Based Subspace Correction Methods for Image  Processing. In Domain Decomposition Methods in Science and Engineering XXI (S. 829--837). Springer. http://link.springer.com/chapter/10.1007/978-3-319-05789-7_80
    44. Kabil, B., & Rohde, C. (2014). The influence of surface tension and configurational forces on the  stability of liquid-vapor interfaces. Nonlinear Analysis: Theory, Methods & Applications, 107(0), 63–75. http://dx.doi.org/10.1016/j.na.2014.04.003
    45. Kaulmann, S., Flemisch, B., Haasdonk, B., Lie, K. A., & Ohlberger, M. (2014). The Localized Reduced Basis Multiscale method for two-phase flow in porous media. arXiv preprint arXiv:1405.2810.
    46. Kaulmann, S., Flemisch, B., Haasdonk, B., Lie, K.-A., & Ohlberger, M. (2014). The Localized Reduced Basis Multiscale method for two-phase flows  in porous media. arXiv.org. http://arxiv.org/abs/1405.2810v1
    47. Kazaz, L. (2014). Black Box Model Order Reduction of Nonlinear Systems with Kernel  and Discrete Empirical Interpolation.
    48. Kohls, K., Rösch, A., & Siebert, K. G. (2014). A Posteriori Error Analysis of Optimal Control Problems with Control  Constraints. SIAM J. Control Optim., 52(3), 1832�1861. (30 pages). http://dx.doi.org/10.1137/130909251
    49. Kohr, M., Pintea, C., & Wendland, W. L. (2014). Neumann-transmission problems for pseudodifferential Brinkman operators  on Lipschitz domains in compact Riemannian manifolds. Communications in Pure and Applied Analysis, 13, 1–28. https://doi.org/03934/cpaa.2013.13.
    50. Kohr, M., Lanza de Cristoforis, M., & Wendland, W. L. (2014). Boundary value problems of Robin type for the Brinkman and Darcy-Forchheimer-Brinkman  systems in Lipschitz domains. J. Math. Fluid Mechanics, 16, 595–830.
    51. Kohr, M., Lanza de Cristoforis, M., & Wendland, W. L. (2014). Nonlinear Darcy-Forchheimer-Brinkman system with linear boundary  conditions in Lipschitz domains. In A. G. T. Aliev Azerogly & S. V. Rogosin (Hrsg.), Complex Analysis and Potential Theory with Applications (S. 111–124). Cambridge Sci. Publ.
    52. Köppel, M., Kröker, I., & Rohde, C. (2014). Stochastic Modeling for Heterogeneous Two-Phase Flow. In J. Fuhrmann, M. Ohlberger, & C. Rohde (Hrsg.), Finite Volumes for Complex Applications VII-Methods and Theoretical  Aspects (Bd. 77, S. 353–361). Springer International Publishing. https://doi.org/10.1007/978-3-319-05684-5_34
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    54. Müthing, S., Bastian, P., Göddeke, D., & Ribbrock, D. (2014). Node-level performance engineering for an advanced density driven  porous media flow solver. 3rd Workshop on Computational Engineering 2014, Stuttgart, Germany, 109--113.
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