Department of Mathematics

Publications of the Department of Mathematics

List of publications of the Department of Mathematics starting 2017

 

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

  1. 2021

    1. 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, Article 31. https://doi.org/doi.org/10.1007/s00332-021-09755-9
    2. Altenbernd, M., Dreier, N.-A., Engwer, C., & Göddeke, D. (2021). Towards Local-Failure Local-Recovery in PDE Frameworks: The Case of Linear Solvers. In T. Kozubek, P. Arbenz, J. Jaros, L. Ríha, J. Sístek, & P. Tichý (Eds.), High Performance Computing in Science and Engineering -- HPCSE 2019 (Vol. 12456, pp. 17--38). Springer. https://doi.org/10.1007/978-3-030-67077-1_2
    3. Beck, A., Dürrwächter, J., Kuhn, T., Meyer, F., Munz, C.-D., & Rohde, C. (2021). Uncertainty Quantification in High Performance Computational Fluid Dynamics. In W. E. Nagel, D. H. Kröner, & M. M. Resch (Eds.), High Performance Computing in Science and Engineering ’19 (pp. 355--371). Springer International Publishing.
    4. Benacchio, T., Bonaventura, L., Altenbernd, M., Cantwell, C. D., Düben, P. D., Gillard, M., Giraud, L., Göddeke, D., Raffin, E., Teranishi, K., & Wedi, N. (2021). Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction. The International Journal of High Performance Computing Applications (Online First). https://doi.org/10.1177/1094342021990433
    5. 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
    6. Buchfink, P., & Haasdonk, B. (2021). Experimental Comparison of Symplectic and Non-symplectic Model Order Reduction an Uncertainty Quantification Problem. In F. J. Vermolen & C. Vuik (Eds.), Numerical Mathematics and Advanced Applications ENUMATH 2019 (Vol. 139). Springer International Publishing. https://doi.org/10.1007/978-3-030-55874-1
    7. 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
    8. de Rijk, B., & Schneider, G. (2021). Global existence and decay in multi-component reaction-diffusion-advection systems with different              velocities: oscillations in time and frequency. NoDEA Nonlinear Differential Equations Appl., 28(1), Paper No. 2, 38. https://doi.org/10.1007/s00030-020-00665-5
    9. 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
    10. 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
    11. 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, 105039. https://doi.org/10.1016/j.compfluid.2021.105039
    12. 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
    13. Eggenweiler, E., Discacciati, M., & Rybak, I. (2021). Analysis of the Stokes-Darcy problem with generalised interface conditions. ESAIM Math. Model. Numer. Anal. (Submitted). https://arxiv.org/abs/2104.02339
    14. Frank, R. L., Laptev, A., & Weidl, T. (2021). Lieb-Thirring Inequalities.
    15. 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
    16. Gander, M., Lunowa, S., & Rohde, C. (2021). Non-overlapping Schwarz Waveform-Relaxation for Nonlinear Advection-Diffusion Equations. http://www.uhasselt.be/Documents/CMAT/Preprints/2021/UP2103.pdf
    17. 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
    18. 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
    19. 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
    20. Girardi, G., & Wirth, J. (2021). Decay Estimates for a Klein-Gordon Model with Time-Periodic Coeffizients. In M. Cicognani, D. del Santo, A. Parmeggiani, & M. Reissig (Eds.), Anomalies in Partial Differential Equations (Vol. 43). Springer. https://doi.org/10.1007/978-3-030-61346-4_14
    21. 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
    22. Hahn, B. N. (2021). Motion compensation strategies in tomography. https://doi.org/10.1007/978-3-030-57784-1_3
    23. Hamm, T., & Steinwart, I. (2021). Adaptive Learning Rates for Support Vector Machines Working on Data with Low Intrinsic Dimension. Ann. Statist.
    24. Hamm, T., & Steinwart, I. (2021). Intrinsic Dimension Adaptive Partitioning for Kernel Methods. Fakultät für Mathematik und Physik, Universität Stuttgart.
    25. 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
    26. 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
    27. 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
    28. 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
    29. Aufgaben und Lösungen zur Höheren Mathematik 1. (2021). In K. V. Höllig & J. V. Hörner (Eds.), Springer eBook Collection (3rd ed. 2021.). https://doi.org/10.1007/978-3-662-63181-2
    30. 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
    31. Kollross, A. (2021). Polar actions on Damek-Ricci spaces. Differential Geometry and Its Applications, 76, 101753. https://doi.org/10.1016/j.difgeo.2021.101753
    32. Krämer, A., Maier, B., Rau, T., Huber, F., Klotz, T., Ertl, T., Göddeke, D., Mehl, M., Reina, G., & Röhrle, O. (2021). Multi-physics multi-scale HPC simulations of skeletal muscles. In W. E. Nagel, D. H. Kröner, & M. M. Resch (Eds.), High Performance Computing in Science and Engineering ’20: Transactions of the High Performance Computing Center, Stuttgart(HLRS) 2020. https://doi.org/10.1007/978-3-030-80602-6_13
    33. Kühnert, J., Göddeke, D., & Herschel, M. (2021, July). Provenance-integrated parameter selection and optimization in numerical simulations. 13th International Workshop on Theory and Practice OfProvenance (TaPP 2021). https://www.usenix.org/conference/tapp2021/presentation/kühnert
    34. Lang, R. (2021). On the eigenvalues of the non-self-adjoint Robin Laplacian on bounded domains and compact quantum graphs. [Dissertation, Universität Stuttgart]. https://doi.org/10.18419/opus-11428
    35. Magiera, J. (2021). A Molecular--Continuum Multiscale Solver for Liquid--Vapor Flow. Small Collaboration: Advanced Numerical Methods for Nonlinear Hyperbolic Balance Laws and Their Applications (Hybrid Meeting), 41. https://doi.org/10.14760/OWR-2021-41
    36. Magiera, J., & Rohde, C. (2021). Analysis and Numerics of Sharp and Diffuse Interface Models for Droplet Dynamics. In K. Schulte, C. Tropea, & B. Weigand (Eds.), Droplet Dynamics under Extreme Ambient Conditions. Springer.
    37. Makogin, V., Oesting, M., Rapp, A., & Spodarev, E. (2021). Long range dependence for stable random processes. J. Time Series Anal., 42(2), 161--185. https://doi.org/10.1111/jtsa.12560
    38. Massa, F., Ostrowski, L., Bassi, F., & Rohde, C. (2021). An artificial Equation of State based Riemann solver for a discontinuous Galerkin discretization of the incompressible Navier–Stokes equations. J. Comput. Phys., 110705. https://doi.org/10.1016/j.jcp.2021.110705
    39. Nonnenmacher, M., Reeb, D., & Steinwart, I. (2021). Which Minimizer Does My Neural Network Converge To? In N. Oliver, F. Pérez-Cruz, S. Kramer, J. Read, & J. A. Lozano (Eds.), Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 87--102). Springer International Publishing. https://doi.org/10.1007/978-3-030-86523-8_6
    40. Osorno, M., Schirwon, M., Kijanski, N., Sivanesapillai, R., Steeb, H., & Göddeke, D. (2021). A cross-platform, high-performance SPH toolkit for image-based flow simulations on the pore scale of porous media. Computer Physics Communications, 267(108059), Article 108059. https://doi.org/10.1016/j.cpc.2021.108059
    41. Rohde, C., & von Wolff, L. (2021). A Ternary Cahn-Hilliard-Navier-Stokes model for two phase flow with precipitation and dissolution. Math. Models Methods Appl. Sci., 31(1), 1--35. https://doi.org/10.1142/S0218202521500019
    42. Rohde, C., & Tang, H. (2021). On the stochastic Dullin-Gottwald-Holm equation: global existence and wave-breaking phenomena. NoDEA Nonlinear Differential Equations Appl., 28(1), Paper No. 5, 34. https://doi.org/10.1007/s00030-020-00661-9
    43. Rörich, A., Werthmann, T. A., Göddeke, D., & Grasedyck, L. (2021). Bayesian inversion for electromyography using low-rank tensor formats. Inverse Problems, 37(5), 055003. https://doi.org/10.1088/1361-6420/abd85a
    44. Rörich, A., Werthmann, T. A., Göddeke, D., & Grasedyck, L. (2021). Bayesian inversion for electromyography using low-rank tensor formats. Inverse Problems, 37(5), 055003. https://doi.org/10.1088/1361-6420/abd85a
    45. Schricker, S., Monje, DC., Dippon, J., Kimmel, M., Alscher, MD., & Schanz, M. (2021). Physician-guided, hybrid genetic testing exerts promising effects on health-related behavior without compromising quality of life. Sci Rep., 2021 Apr 19;11(1), 8494. https://doi.org/10.1038/s41598-021-87821-8
    46. Stauch, G., Fritz, P., Rokai, R., Sediqi, A., Firooz, H., Voelker, HU., Weinhara, M., Mollin, J., Soudah, B., Dalquen, P., Brinckmann, F., & Dippon, J. (2021). The Importance of Clinical Data for the Diagnosis of Breast Tumours in North Afghanistan. Int. Jounal Breast Cancer, Jul 30;2021, 6625239. https://doi.org/10.1155/2021/6625239
    47. Steinwart, I., & Fischer, S. (2021). A Closer Look at Covering Number Bounds for Gaussian Kernels. J. Complexity, 62, 101513. https://doi.org/10.1016/j.jco.2020.101513
    48. Steinwart, I., & Ziegel, J. F. (2021). Strictly proper kernel scores and characteristic kernels on compact spaces. Appl. Comput. Harmon. Anal., 51, 510--542. https://doi.org/10.1016/j.acha.2019.11.005
    49. Strohbeck, P., Eggenweiler, E., & Rybak, I. (2021). Determination of the Beavers-Joseph slip coefficient for coupled Stokes/Darcy problems. Adv. Water Res. (Submitted). https://arxiv.org/abs/2106.15556
    50. von Wolff, L., Weinhardt, F., Class, H., Hommel, J., & Rohde, C. (2021). Investigation of Crystal Growth in Enzymatically Induced Calcite Precipitation by Micro-Fluidic Experimental Methods and Comparison with Mathematical Modeling. Transp. Porous Media, 137(2), 327--343. https://doi.org/10.1007/s11242-021-01560-y
  2. 2020

    1. Barberis, M. L., Moroianu, A., & Semmelmann, U. (2020). Generalized vector cross products and Killing forms on negatively curved manifolds. Geom. Dedicata, 205, 113--127. https://doi.org/10.1007/s10711-019-00467-9
    2. Bastian, P., Altenbernd, M., Dreier, N.-A., Engwer, C., Fahlke, J., Fritze, R., Geveler, M., Göddeke, D., Iliev, O., Ippisch, O., Mohring, J., Müthing, S., Ohlberger, M., Ribbrock, D., Shegunov, N., & Turek, S. (2020). Exa-Dune - Flexible PDE Solvers, Numerical Methods and Applications. In H.-J. Bungartz, S. Reiz, B. Uekermann, P. Neumann, & W. E. Nagel (Eds.), Software for Exascale Computing -- SPPEXA 2016--2019 (pp. 225--269). Springer. https://doi.org/10.1007/978-3-030-47956-5_9
    3. Baumstark, S., Schneider, G., Schratz, K., & Zimmermann, D. (2020). Effective Slow Dynamics Models for a Class of Dispersive Systems. Journal of Dynamics and Differential Equations, 32(4), 1867--1899. https://doi.org/10.1007/s10884-019-09791-w
    4. Baumstark, S., Schneider, G., & Schratz, K. (2020). Effective numerical simulation of the Klein-Gordon-Zakharov system in the Zakharov limit. In Mathematics of wave phenomena. Selected papers based on the presentations at the conference, Karlsruhe, Germany, July 23--27, 2018 (pp. 37--48). Cham: Birkhäuser.
    5. Beck, A., Dürrwächter, J., Kuhn, T., Meyer, F., Munz, C.-D., & Rohde, C. (2020). $hp$-Multilevel Monte Carlo methods for uncertainty quantification of compressible flows. SIAM J. Sci. Comput., 42(4), B1067–B1091. https://doi.org/10.1137/18M1210575
    6. Berre, I., Boon, W. M., Flemisch, B., Fumagalli, A., Gläser, D., Keilegavlen, E., Scotti, A., Stefansson, I., Tatomir, A., Brenner, K., Burbulla, S., Devloo, P., Duran, O., Favino, M., Hennicker, J., Lee, I.-H., Lipnikov, K., Masson, R., Mosthaf, K., … Zulian, P. (2020). Verification benchmarks for single-phase flow in three-dimensional fractured porous media.
    7. Bitter, A. (2020). Virtual levels of multi-particle quantum systems and their implications for the Efimov effect [Dissertation, Universität Stuttgart]. https://doi.org/10.18419/opus-11315
    8. Blanke, S. E., Hahn, B. N., & Wald, A. (2020). Inverse problems with inexact forward operator: iterative regularization and application in dynamic imaging. Inverse Problems, 36(12), 124001. https://doi.org/10.1088/1361-6420/abb5e1
    9. Brinker, J., & Wirth, J. (2020). Gelfand Triples for the Kohn–Nirenberg Quantization on Homogeneous Lie Groups. In Advances in Harmonic Analysis and Partial Differential Equations. (pp. 51–97). Birkhäuser. https://doi.org/10.1007/978-3-030-58215-9_3
    10. Buchfink, P., Haasdonk, B., & Rave, S. (2020). PSD-Greedy Basis Generation for Structure-Preserving Model Order Reduction of Hamiltonian Systems. In P. Frolkovič, K. Mikula, & D. Ševčovič (Eds.), Proceedings of the Conference Algoritmy 2020 (pp. 151--160). Vydavateľstvo SPEKTRUM. http://www.iam.fmph.uniba.sk/amuc/ojs/index.php/algoritmy/article/view/1577/829
    11. de Rijk, B., & Schneider, G. (2020). Global Existence and Decay in Nonlinearly Coupled Reaction-Diffusion-Advection Equations with Different Velocities. J. Differential Equations, 268(7), 3392--3448. https://doi.org/10.1016/j.jde.2019.09.056
    12. Díaz-Ramos, J. C., Domínguez-Vázquez, M., & Kollross, A. (2020). On homogeneous manifolds whose isotropy actions are polar. Manuscripta Mathematica, 161(1), 15--34. https://doi.org/10.1007/s00229-018-1077-1
    13. Eggenweiler, E., & Rybak, I. (2020). Interface conditions for arbitrary flows in coupled porous-medium and free-flow systems. In R. Klöfkorn, E. Keilegavlen, F. Radu, & J. Fuhrmann (Eds.), Finite Volumes for Complex Applications IX - Methods, Theoretical Aspects, Examples (Vol. 323, pp. 345–353). Springer International Publishing. https://doi.org/10.1007/978-3-030-43651-3_31
    14. Fischer, S., & Steinwart, I. (2020). Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm. J. Mach. Learn. Res., 205, 1--38.
    15. Fischer, S. (2020). Some new bounds on the entropy numbers of diagonal operators. J. Approx. Theory, 251, 105343. https://doi.org/10.1016/j.jat.2019.105343
    16. Geck, M. (2020). Green functions and Glauberman degree-divisibility. Annals of Mathematics, 192(1), 229–249. https://doi.org/10.4007/annals.2020.192.1.4
    17. Geck, M. (2020). On Jacob’s construction of the rational canonical form of a matrix. The Electronic Journal of Linear Algebra, 36(36), 177--182. https://doi.org/10.13001/ela.2020.5055
    18. Geck, M., & Malle, G. (2020). The character theory of finite groups of Lie type. A guided tour. In Cambridge Studies in Advanced Mathematics (Vol. 187, p. ix+394). Cambridge University Press. https://doi.org/10.1017/9781108779081
    19. Geck, M. (2020). ChevLie: Constructing Lie algebras and Chevalley groups. Journal of Software for Algebra and Geometry, 10(1), 41--49. https://doi.org/10.2140/jsag.2020.10.41
    20. Geck, M. (2020). Computing Green functions in small characteristic. Journal of Algebra, 561, 163--199. https://doi.org/10.1016/j.jalgebra.2019.12.016
    21. Advances in Harmonic Analysis and Partial Differential Equations. (2020). In V. Georgiev, T. Ozawa, M. Ruzhansky, & J. Wirth (Eds.), Trends in Mathematics. Birkhäuser. https://doi.org/10.1007/978-3-030-58215-9
    22. Giesselmann, J., Meyer, F., & Rohde, C. (2020). A posteriori error analysis and adaptive non-intrusive numerical schemes for systems of random conservation laws. BIT Numerical Mathematics, 60(3), 619–649. https://doi.org/10.1007/s10543-019-00794-z
    23. Ginoux, N., Habib, G., Pilca, M., & Semmelmann, U. (2020). An Obata-type characterisation of Calabi metrics on line bundles. North-West. Eur. J. Math., 6, 119--136, i.
    24. Giraud, L., Rüde, U., & Stals, L. (2020). Resiliency in Numerical Algorithm Design for Extreme Scale Simulations (Dagstuhl Seminar 20101). Dagstuhl Reports, 10(3), 1--57. https://doi.org/10.4230/DagRep.10.3.1
    25. Griesemer, M., Hofacker, M., & Linden, U. (2020). From short-range to contact interactions in the 1d Bose gas. Math. Phys. Anal. Geom., 23(2), Paper No. 19, 28. https://doi.org/10.1007/s11040-020-09344-4
    26. Haas, T., de Rijk, B., & Schneider, G. (2020). MODULATION EQUATIONS NEAR THE ECKHAUS BOUNDARY: THE KdV EQUATION. SIAM JOURNAL ON MATHEMATICAL ANALYSIS, 52(6), 5389–5421. https://doi.org/10.1137/19M1266873
    27. Haas, T., & Schneider, G. (2020). Failure of the N-wave interaction approximation without imposing    periodic boundary conditions. ZAMM-ZEITSCHRIFT FUR ANGEWANDTE MATHEMATIK UND MECHANIK, 100(6), Article 6. https://doi.org/10.1002/zamm.201900230
    28. Hilder, B. (2020). Modulating traveling fronts for the Swift-Hohenberg equation in the case of an additional conservation law. Journal of Differential Equations, 269(5), 4353--4380. https://doi.org/10.1016/j.jde.2020.03.033
    29. Hilder, B., Peletier, M. A., Sharma, U., & Tse, O. (2020). An inequality connecting entropy distance, Fisher Information and large deviations. Stochastic Processes and Their Applications, 130(5), 2596--2638. https://doi.org/10.1016/j.spa.2019.07.012
    30. Holicki, T., & Scherer, C. W. (2020). Output-Feedback Synthesis for a Class of Aperiodic Impulsive Systems. IFAC-PapersOnline, 53(2), 7299–7304. https://doi.org/10.1016/j.ifacol.2020.12.981
    31. Holzmüller, D., & Steinwart, I. (2020). Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent. Fakultät für Mathematik und Physik, Universität Stuttgart.
    32. Jentsch, T., & Weingart, G. (2020). RIEMANNIAN AND KAHLERIAN NORMAL COORDINATES. ASIAN JOURNAL OF MATHEMATICS, 24(3), 369–416.
    33. Kennedy, J. B., & Lang, R. (2020). On the eigenvalues of quantum graph Laplacians with large complex δ couplings. Portugaliae Mathematica. A Journal of the Portuguese Mathematical Society, 77(2), 133–161.
    34. Koch, T., Gläser, D., Weishaupt, K., Ackermann, S., Beck, M., Becker, B., Burbulla, S., Class, H., Coltman, E., Emmert, S., Fetzer, T., Grüninger, C., Heck, K., Hommel, J., Kurz, T., Lipp, M., Mohammadi, F., Scherrer, S., Schneider, M., … Flemisch, B. (2020). DuMux 3 – an open-source simulator for solving flow and transport problems in porous media with a focus on model coupling. Computers & Mathematics with Applications. https://doi.org/10.1016/j.camwa.2020.02.012
    35. Kohr, M., Mikhailov, S. E., & Wendland, W. L. (2020). Potentials and transmission problems in weighted Sobolev spaces for anisotropic Stokes and Navier–Stokes systems with L∞ strongly elliptic coefficient tensor. Complex Variables and Elliptic Equations, 65(1), 109–140. https://doi.org/10.1080/17476933.2019.1631293
    36. Kollross, A. (2020). Octonions, triality, the exceptional Lie algebra F4 and polar actions on the Cayley hyperbolic plane. International Journal of Mathematics, 31(07), 2050051. https://doi.org/10.1142/s0129167x20500512
    37. Maier, D. (2020). BREATHER SOLUTIONS ON DISCRETE NECKLACE GRAPHS. OPERATORS AND MATRICES, 14(3), 767–776. https://doi.org/10.7153/oam-2020-14-48
    38. Maier, D. (2020). Construction of breather solutions for nonlinear Klein-Gordon equations    on periodic metric graphs. JOURNAL OF DIFFERENTIAL EQUATIONS, 268(6), 2491–2509. https://doi.org/10.1016/j.jde.2019.09.035
    39. Minorics, L. A. (2020). Spectral asymptotics for Krein-Feller operators with respect to V-variable Cantor measures. Forum Mathematicum, 32(1), 121–138. https://doi.org/10.1515/forum-2018-0188
    40. 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
    41. 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
    42. 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
    43. Pelinovsky, D. E., & Schneider, G. (2020). The monoatomic FPU system as a limit of a diatomic FPU system. Appl. Math. Lett., 107, 7.
    44. Polyakova, A. P., Svetov, I. E., & Hahn, B. N. (2020). The Singular Value Decomposition of the Operators of the Dynamic Ray Transforms Acting on 2D Vector Fields. In Y. D. Sergeyev & D. E. Kvasov (Eds.), Numerical Computations: Theory and Algorithms (pp. 446--453). Springer International Publishing. https://doi.org/10.1007/978-3-030-40616-5_42
    45. 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
    46. 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
    47. Schneider, G. (2020). The KdV approximation for a system with unstable resonances. Math. Methods Appl. Sci., 43(6), 3185--3199.
    48. 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
    49. Steinwart, I. (2020). Reproducing Kernel Hilbert Spaces Cannot Contain all Continuous Functions on a Compact Metric Space. Fakultät für Mathematik und Physik, Universität Stuttgart.
    50. 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
    51. 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
  3. 2019

    1. Bastian, P., Altenbernd, M., Dreier, N.-A., Engwer, C., Fahlke, J., Fritze, R., Geveler, M., Göddeke, D., Iliev, O., Ippisch, O., Mohring, J., Müthing, S., Ohlberger, M., Ribbrock, D., Shegunov, N., & Turek, S. (2019). Exa-Dune -- Flexible PDE Solvers, Numerical Methods and Applications.
    2. Bauer, R., Cummings, P., & Schneider, G. (2019). A model for the periodic water wave problem and its long wave amplitude equations. In Nonlinear water waves. An interdisciplinary interface. Based on the workshop held at the Erwin Schrödinger International Institute for Mathematics and Physics, Vienna, Austria, November 27 -- December 7, 2017 (pp. 123--138). Cham: Birkhäuser.
    3. Bauer, R., Düll, W.-P., & Schneider, G. (2019). The Korteweg-de Vries, Burgers and Whitham limits for a spatially periodic Boussinesq model. Proc. R. Soc. Edinb., Sect. A, Math., 149(1), 191--217.
    4. Bianchi, L. A., Blömker, D., & Schneider, G. (2019). Modulation equation and SPDEs on unbounded domains. Commun. Math. Phys., 371(1), 19--54.
    5. Brehler, M., Schirwon, M., Krummrich, P. M., & Göddeke, D. (2019). Simulation of Nonlinear Signal Propagation in Multimode Fibers on Multi-GPU Systems. Communications in Nonlinear Science and Numerical Simulation. https://doi.org/10.1016/j.cnsns.2019.105150
    6. Brünnette, T., Santin, G., & Haasdonk, B. (2019). Greedy Kernel Methods for Accelerating Implicit Integrators for Parametric ODEs. In F. A. Radu, K. Kumar, I. Berre, J. M. Nordbotten, & I. S. Pop (Eds.), Numerical Mathematics and Advanced Applications - ENUMATH 2017 (pp. 889--896). Springer International Publishing.
    7. 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
    8. Carlberg, K., Brencher, L., Haasdonk, B., & Barth, A. (2019). Data-Driven Time Parallelism via Forecasting. SIAM Journal on Scientific Computing, 41(3), B466–B496. https://doi.org/10.1137/18M1174362
    9. Chirilus-Bruckner, M., Maier, D., & Schneider, G. (2019). Diffusive stability for periodic metric graphs. Math. Nachr., 292(6), 1246--1259.
    10. 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
    11. 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
    12. 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
    13. 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
    14. 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
    15. 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
    16. Geck, M. (2019). Eigenvalues and Polynomial Equations. The American Mathematical Monthly, 126(10), 933--935. https://doi.org/10.1080/00029890.2019.1651168
    17. 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
    18. 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
    19. 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
    20. 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
    21. 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
    22. 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
    23. 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
    24. Aufgaben und Lösungen zur Höheren Mathematik 1. (2019). In K. V. Höllig & J. V. Hörner (Eds.), SpringerLink. Bücher (2. Auflage, Vol. 1). https://doi.org/10.1007/978-3-662-58445-3
    25. 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.
    26. Kohr, M., Mikhailov, S. E., & Wendland, W. L. (2019). Potentials and transmission problems in weighted Sobolev spaces for anisotropic Stokes and Navier–Stokes systems with L∞ strongly elliptic coefficient tensor. Complex Variables and Elliptic Equations, 65(1), 109–140. https://doi.org/10.1080/17476933.2019.1631293
    27. 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
    28. 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 (Eds.), Analysis as a Life: Dedicated to Heinrich Begehr on the Occasion of his 80th Birthday (pp. 237--260). Springer International Publishing. https://doi.org/10.1007/978-3-030-02650-9_12
    29. Köppel, M., Franzelin, F., Kröker, I., Oladyshkin, S., Santin, G., Wittwar, D., Barth, A., Haasdonk, B., Nowak, W., Pflüger, D., & Rohde, C. (2019). Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario. Computational Geosciences, 23(2), 339--354. https://doi.org/10.1007/s10596-018-9785-x
    30. 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.
    31. 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
    32. Ostrowski, L., & Massa, F. (2019). An incompressible-compressible approach for droplet impact. In G. Cossali & S. Tonini (Eds.), Proceedings of the DIPSI Workshop 2019: Droplet ImpactPhenomena & Spray Investigations, Bergamo, Italy, 17th May 2019 (pp. 18–21). Università degli studi di Bergamo. https://doi.org/10.6092/DIPSI2019_pp18-21
    33. Santin, G., & Haasdonk, B. (2019). Kernel Methods for Surrogate Modelling. University of Stuttgart.
    34. Santin, G., Wittwar, D., & Haasdonk, B. (2019). Sparse approximation of regularized kernel interpolation by greedy algorithms.
    35. Santin, G., & Haasdonk, B. (2019). Kernel Methods for Surrogate Modeling (ArXiv 1907.10556; Issue 1907.10556). https://arxiv.org/abs/1907.10556
    36. 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
    37. 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
    38. Schneider, G. (2019). The Zakharov limit of Klein-Gordon-Zakharov like systems in case of analytic solutions. Applicable Analysis. https://doi.org/10.1080/00036811.2019.1695785
    39. 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
    40. 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
    41. 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
    42. Steinwart, I. (2019). A Sober Look at Neural Network Initializations. Fakultät für Mathematik und Physik, Universität Stuttgart.
    43. 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
    44. Wenzel, T., Santin, G., & Haasdonk, B. (2019). A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution.
    45. Wittwar, D., Santin, G., & Haasdonk, B. (2019). Part II on matrix valued kernels including analysis.
    46. Wittwar, D., & Haasdonk, B. (2019). Greedy Algorithms for Matrix-Valued Kernels. In F. A. Radu, K. Kumar, I. Berre, J. M. Nordbotten, & I. S. Pop (Eds.), Numerical Mathematics and Advanced Applications ENUMATH 2017 (pp. 113--121). Springer International Publishing.
    47. 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
    48. 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
  4. 2018

    1. Afkham, B. M., Bhatt, A., Haasdonk, B., & Hesthaven, J. S. (2018). Symplectic Model-Reduction with a Weighted Inner Product.
    2. Babak, M. Afkham., Bhatt, A., Haasdonk, B., & Hesthaven, J. S. (2018). Symplectic Model-Reduction with a Weighted Inner Product.
    3. Barth, A., & Stein, A. (2018). A Study of Elliptic Partial Differential Equations with Jump Diffusion    Coefficients. SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 6(4), 1707–1743. https://doi.org/10.1137/17M1148888
    4. Barth, A., & Stüwe, T. (2018). Weak convergence of Galerkin approximations of stochastic partial  differential equations driven by additive Lévy noise. Math. Comput. Simulation, 143, 215--225. https://doi.org/10.1016/j.matcom.2017.03.007
    5. Barth, A., & Stein, A. (2018). Approximation and simulation of infinite-dimensional Levy processes. STOCHASTICS AND PARTIAL DIFFERENTIAL EQUATIONS-ANALYSIS AND COMPUTATIONS, 6(2), 286–334. https://doi.org/10.1007/s40072-017-0109-2
    6. Bhatt, A., Fehr, J., & Hassdonk, B. (2018). Model Order Reduction of an Elastic Body under Large Rigid Motion. Proceedings of ENUMATH 2017, Voss, Norway.
    7. Bhatt, A., Fehr, J., Grunert, D., & Haasdonk, B. (2018). A Posteriori Error Estimation in Model Order Reduction of Elastic Multibody Systems with Large Rigid Motion. Proceedings of MORCOS 2018, Stuttgart, Germany.
    8. Bhatt, A., & Haasdonk, B. (2018). Certified and structure-preserving model order reduction of EMBS. In RAMSA 2017, New Delhi.
    9. Bhatt, A., Haasdonk, B., & Moore, B. E. (2018). Structure-preserving Integration and Model Order Reduction.
    10. Blaschzyk, I., & Steinwart, I. (2018). Improved classification rates under refined margin conditions. ELECTRONIC JOURNAL OF STATISTICS, 12(1), 793–823. https://doi.org/10.1214/18-EJS1406
    11. Brehler, M., Schirwon, M., Göddeke, D., & Krummrich, P. (2018, July). Modeling the Kerr-Nonlinearity in Mode-Division Multiplexing Fiber  Transmission Systems on GPUs. Proceedings of Advanced Photonics 2018.
    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. 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
    15. 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
    16. 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
    17. 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
    18. 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
    19. 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
    20. 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
    21. Doering, M., Gyorfi, L., & Walk, H. (2018). Rate of Convergence of k-Nearest-Neighbor Classification Rule. JOURNAL OF MACHINE LEARNING RESEARCH, 18.
    22. Dreier, N.-A., Altenbernd, M., Engwer, C., & Göddeke, D. (2018, March). A high-level C++ approach to manage local errors, asynchrony and  faults in an MPI application. Proceedings of 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2018).
    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., Hilder, B., & Schneider, G. (2018). Analysis of the embedded cell method in 1D for the numerical homogenization of metal-ceramic composite materials. J. Appl. Anal., 24(1), 71--80. https://doi.org/10.1515/jaa-2018-0007
    26. 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
    27. Engwer, C., Altenbernd, M., Dreier, N.-A., & G�ddeke, D. (2018, March). A high-level C++ approach to manage local errors, asynchrony and  faults in an MPI application. Proceedings of the 26th Euromicro International Conference on Parallel,  Distributed and Network-Based Processing (PDP 2018).
    28. Engwer, C., Altenbernd, M., Dreier, N.-A., & Göddeke, D. (2018, March). A high-level C++ approach to manage local errors, asynchrony and  faults in an MPI application. Proceedings of the 26th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2018).
    29. 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
    30. 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.
    31. 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
    32. Fritzen, F., Haasdonk, B., Ryckelynck, D., & Schöps, S. (2018). An algorithmic comparison of the Hyper-Reduction and the Discrete  Empirical Interpolation Method for a nonlinear thermal problem. Math. Comput. Appl. 2018, 23(1), Article 1. https://doi.org/doi:10.3390/mca23010008
    33. Geck, M. (2018). On the values of unipotent characters in bad characteristic. Rendiconti Del Seminario Matematico Della Università Di Padova, 141, 37--63. https://doi.org/10.4171/rsmup/14
    34. Geck, M. (2018). A first guide to the character theory of finite groups of Lie type. Local Representation Theory and Simple Groups (Eds. R. Kessar, G. Malle, D. Testerman), 63--106. https://doi.org/10.4171/185-1/3
    35. Georgiev, V., & Wirth, J. (2018). Zero resonances for localised potentials. Journal of Mathematical Physics, 59(7), 071502. https://doi.org/10.1063/1.5027717
    36. 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
    37. Gimperlein, H., Meyer, F., �zdemir, C., & Stephan, E. P. (2018). Time domain boundary elements for dynamic contact problems. Computer Methods in Applied Mechanics and Engineering, 333, 147–175. https://doi.org/10.1016/j.cma.2018.01.025
    38. Gimperlein, H., Meyer, F., �zdemir, C., Stark, D., & Stephan, E. P. (2018). Boundary elements with mesh refinements for the wave equation. Numer. Math., (accepted). https://arxiv.org/abs/1801.09736
    39. Griesemer, M., & Wünsch, A. (2018). On the domain of the Nelson Hamiltonian. J. Math. Phys., 59(4), 042111, 21. https://doi.org/10.1063/1.5018579
    40. Griesemer, M., & Linden, U. (2018). Stability of the two-dimensional Fermi polaron. Lett. Math. Phys., 108(8), 1837--1849. https://doi.org/10.1007/s11005-018-1055-2
    41. Haasdonk, B., Hamzi, B., Santin, G., & Wittwar, D. (2018). Greedy Kernel Methods for Center Manifold Approximation (ArXiv 1810.11329; Issue 1810.11329).
    42. Haasdonk, B., & Santin, G. (2018). Greedy Kernel Approximation for Sparse Surrogate Modeling. In W. Keiper, A. Milde, & S. Volkwein (Eds.), Reduced-Order Modeling (ROM) for Simulation and Optimization: Powerful Algorithms as Key Enablers for Scientific Computing (pp. 21--45). Springer International Publishing. https://doi.org/10.1007/978-3-319-75319-5_2
    43. Hang, H., Steinwart, I., Feng, Y., & Suykens, J. A. K. (2018). Kernel Density Estimation for Dynamical Systems. J. Mach. Learn. Res., 19, 1--49.
    44. Harbrecht, H., Wendland, W. L., & Zorii, N. (2018). Minimal energy problems for strongly singular Riesz kernels. Mathematische Nachrichten, 291, 55–85. https://doi.org/10.1002/mana.201600024
    45. 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
    46. Hsiao, G. C., Steinbach, O., & Wendland, W. L. (2018). Boundary Element Methods: Foundation and Error Analysis. Encyclopedia of Computational Mechanics Second Edition, 62. https://doi.org/10.1002/9781119176817.ecm2007
    47. Kohler, M., Krzyzak, A., Tent, R., & Walk, H. (2018). Nonparametric quantile estimation using importance sampling. ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, 70(2), 439–465. https://doi.org/10.1007/s10463-016-0595-4
    48. 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
    49. Kohr, M., & Wendland, W. L. (2018). Variational approach for the Stokes and Navier–Stokes systems with nonsmooth coefficients in Lipschitz domains on compact Riemannian manifolds. Calculus of Variations and Partial Differential Equations, 57:165. https://doi.org/10.1007/s00526-018-1426-7
    50. 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
    51. 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
    52. Kuhn, T., Dürrwächter, J., Beck, A., Munz, C.-D., Meyer, F., & Rohde, C. (2018). Uncertainty Quantification for Direct Aeroacoustic Simulations of  Cavity Flows: Vol. (submitted). http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1891
    53. Köppl, T., Santin, G., Haasdonk, B., & Helmig, R. (2018). Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods. International Journal for Numerical Methods in Biomedical Engineering, 34(8), e3095. https://doi.org/10.1002/cnm.3095
    54. K�ppel, M., Martin, V., Jaffré, J., & Roberts, J. E. (2018). A Lagrange multiplier method for a discrete fracture model for flow  in porous media. (Submitted). https://hal.archives-ouvertes.fr/hal-01700663v2
    55. K�ppel, M., Martin, V., & Roberts, J. E. (2018). A stabilized Lagrange multiplier finite-element method for flow in  porous media with fractures. (Submitted). https://hal.archives-ouvertes.fr/hal-01761591
    56. 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
    57. 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
    58. Langer, A. (2018). Overlapping domain decomposition methods for total variation denoising. http://people.ricam.oeaw.ac.at/a.langer/publications/DDfTV.pdf
    59. Maboudi Afkham, B., & Hesthaven, J. S. (2018). Structure-Preserving Model-Reduction of Dissipative Hamiltonian Systems. Journal of Scientific Computing, 1–19. https://doi.org/10.1007/s10915-018-0653-6
    60. Meyer, F., Schlachter, L., & Schneider, F. (2018). A hyperbolicity-preserving discontinuous stochastic Galerkin scheme  for uncertain hyperbolic systems of equations. https://arxiv.org/abs/1805.10177
    61. Miller, C. T., Gray, W. G., Kees, C. E., Rybak, I. V., & Shepherd, B. J. (2018). Modeling sediment transport in three-phase surface water systems. J. Hydraul. Res. (Accepted).
    62. Oesting, M., Bel, L., & Lantuéjoul, C. (2018). Sampling from a max-stable process conditional on a homogeneous functional with an application for downscaling climate data. Scand. J. Stat., 45(2), 382--404. https://doi.org/10.1111/sjos.12299
    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
    64. Oesting, M., & Strokorb, K. (2018). Efficient simulation of Brown-Resnick processes based on variance reduction of Gaussian processes. Adv. in Appl. Probab., 50(4), 1155--1175. https://doi.org/10.1017/apr.2018.54
    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 P�clet number. Erscheint Bei Int. J. Multiph. Flow. 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., 69:76. https://doi.org/10.1007/s00033-018-0958-1
    70. Rohde, C. (2018). Fully resolved compressible two-phase flow : modelling, analytical and numerical issues. In M. Bulicek, E. Feireisl, & M. Pokorný (Eds.), New trends and results in mathematical description of fluid flows (pp. 115–181). Birkhäuser. https://doi.org/10.1007/978-3-319-94343-5
    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; Issue 1807.09575). University of Stuttgart.
    73. 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
    74. 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
    75. 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.
    76. 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
    77. Seus, D., Mitra, K., Pop, I. S., Radu, F. A., & Rohde, C. (2018). A linear domain decomposition method for partially saturated flow  in porous media. Comp. Methods in Appl. Mech. Eng, 333, 331--355. https://doi.org/10.1016/j.cma.2018.01.029
    78. Sharanya, V., Sekhar, G. P. R., & Rohde, C. (2018). The low surface Péclet number regime for surfactant-laden viscous droplets: Influence of surfactant concentration, interfacial slip effects and cross migration. Int. J. of Multiph. Flow, 107, 82–103. https://doi.org/10.1016/j.ijmultiphaseflow.2018.05.008
    79. Wittwar, D., Santin, G., & Haasdonk, B. (2018). Interpolation with uncoupled separable matrix-valued kernels. Dolomites Res. Notes Approx., 11, 23--29. https://doi.org/10.14658/pupj-drna-2018-3-4
    80. 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
    81. 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
    82. Zhang, R., Kyriss, T., Dippon, J., Ciupa, S., Boedeker, E., & Friedel, G. (2018). Impact of comorbidity burden on morbidity following horacoscopic lobectomy: a propensity-matched analysis. J Thorac Dis., 2018 Mar;10(3), 1806–1814. https://doi.org/10.21037/jtd.2018.02.62
  5. 2017

    1. Afkham, B., & Hesthaven, J. (2017). Structure Preserving Model Reduction of Parametric Hamiltonian Systems. SIAM Journal on Scientific Computing, 39(6), A2616–A2644. https://doi.org/10.1137/17M1111991
    2. Alkämper, M., & Klöfkorn, R. (2017). Distributed Newest Vertex Bisection. Journal of Parallel and Distributed Computing, 104, 1–11. http://dx.doi.org/10.1016/j.jpdc.2016.12.003
    3. Alkämper, M., Klöfkorn, R., & Gaspoz, F. (2017). A Weak Compatibility Condition for Newest Vertex Bisection in any  Dimension. http://arxiv.org/abs/1711.03141
    4. 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
    5. Alk�mper, M., & Klofkorn, R. (2017). Distributed Newest Vertex Bisection. JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 104, 1–11. https://doi.org/10.1016/j.jpdc.2016.12.003
    6. 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
    7. 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.
    8. 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 (Eds.), Model Reduction of Parametrized Systems (pp. 333--347). Springer International Publishing. https://doi.org/10.1007/978-3-319-58786-8_21
    9. Armiti-Juber, A., & Rohde, C. (2017). On Darcy-and Brinkman-Type Models for Two-Phase Flow in Asymptotically  Flat Domains. https://arxiv.org/abs/1712.07470
    10. Barth, A., & Fuchs, F. G. (2017). Uncertainty quantification for linear hyperbolic equations with stochastic  process or random field coefficients. Appl. Numer. Math., 121, 38--51. https://doi.org/10.1016/j.apnum.2017.06.009
    11. Barth, A., Harrach, B., Hyvoenen, N., & Mustonen, L. (2017). Detecting stochastic inclusions in electrical impedance tomography. INVERSE PROBLEMS, 33(11), Article 11. https://doi.org/10.1088/1361-6420/aa8f5c
    12. Barth, A., & Stein, A. (2017). A study of elliptic partial differential equations with jump diffusion  coefficients.
    13. Barth, A., Harrach, B., Hyvönen, N., & Mustonen, L. (2017). Detecting stochastic inclusions in electrical impedance tomography. Inv. Prob., 33(11), 115012. http://arxiv.org/abs/1706.03962
    14. Baur, U., Benner, P., Haasdonk, B., Himpe, C., Maier, I., & Ohlberger, M. (2017). Comparison of methods for parametric model order reduction of instationary problems. In P. Benner, A. Cohen, M. Ohlberger, & K. Willcox (Eds.), Model Reduction and Approximation: Theory and Algorithms. SIAM Philadelphia. https://www2.mpi-magdeburg.mpg.de/preprints/2015/MPIMD15-01.pdf
    15. Bhatt, A., & VanGorder, R. (2017). Chaos in a non-autonomous nonlinear system describing asymmetric  water wheels.
    16. Bhatt, A., & Moore, B. E. (2017). Structure-preserving ERK methods for non-autonomous DEs.
    17. Bhatt, A., & Moore, B. E. (2017). Structure-preserving numerical integration of DEs with conformal  invariants.
    18. Brehler, M., Schirwon, M., Göddeke, D., & Krummrich, P. M. (2017). A GPU-accelerated Fourth-Order Runge-Kutta in the Interaction  Picture Method for the Simulation of Nonlinear Signal Propagation  in Multimode Fibers. Journal of Lightwave Technology, 35(17), 3622--3628. https://doi.org/10.1109/JLT.2017.2715358
    19. Brünnette, T., Santin, G., & Haasdonk, B. (2017). Greedy kernel methods for accelerating implicit integrators for parametric ODEs. University of Stuttgart. http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1767
    20. Bürger, R., & Kröker, I. (2017). Hybrid Stochastic Galerkin Finite Volumes for the Diffusively Corrected  Lighthill-Whitham-Richards Traffic Model. In C. Cancès & P. Omnes (Eds.), Finite Volumes for Complex Applications VIII - Hyperbolic, Elliptic  and Parabolic Problems: FVCA 8, Lille, France, June 2017 (pp. 189--197). Springer International Publishing. https://doi.org/10.1007/978-3-319-57394-6_21
    21. Cavoretto, R., De Marchi, S., De Rossi, A., Perracchione, E., & Santin, G. (2017). Partition of unity interpolation using stable kernel-based techniques. APPLIED NUMERICAL MATHEMATICS, 116(SI), 95–107. https://doi.org/10.1016/j.apnum.2016.07.005
    22. Chalons, C., Magiera, J., Rohde, C., & Wiebe, M. (2017). A Finite-Volume Tracking Scheme for Two-Phase Compressible Flow. Erscheint Bei Springer Proc. Math. Stat.
    23. Chalons, Christophe., Rohde, C., & Wiebe, M. (2017). A Finite Volume Method for Undercompressive Shock Waves in Two Space  Dimensions. ESAIM Math. Model. Numer. Anal., 51(5), 1987–2015. https://doi.org/10.1051/m2an/2017027
    24. Chertock, A., Degond, P., & Neusser, J. (2017). An asymptotic-preserving method for a relaxation of the    Navier-Stokes-Korteweg equations. JOURNAL OF COMPUTATIONAL PHYSICS, 335, 387–403. https://doi.org/10.1016/j.jcp.2017.01.030
    25. De Marchi, S., Iske, A., & Santin, G. (2017). Image Reconstruction from Scattered Radon Data by Weighted Positive  Definite Kernel Functions.
    26. De Marchi, S., Idda, A., & Santin, G. (2017). A Rescaled Method for RBF Approximation. In G. E. Fasshauer & L. L. Schumaker (Eds.), Approximation Theory XV: San Antonio 2016 (pp. 39--59). Springer International Publishing. https://doi.org/10.1007/978-3-319-59912-0_3
    27. Diaz Ramos, J. C., Dominguez Vazquez, M., & Kollross, A. (2017). Polar actions on complex hyperbolic spaces. Mathematische Zeitschrift, 287(3), 1183--1213. https://doi.org/10.1007/s00209-017-1864-5
    28. Dibak, C., Schmidt, A., Dürr, F., Haasdonk, B., & Rothermel, K. (2017). Server-Assisted Interactive Mobile Simulations for Pervasive Applications. Proceedings of the 15th IEEE International Conference on Pervasive Computing and Communications (PerCom), 1--10. http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2017-02&engl=1
    29. Dombry, C., Engelke, S., & Oesting, M. (2017). Bayesian inference for multivariate extreme value distributions. Electron. J. Stat., 11(2), 4813--4844. https://doi.org/10.1214/17-EJS1367
    30. Düll, W.-P. (2017). Justification of the nonlinear Schrödinger approximation for a quasilinear Klein-Gordon equation. Comm. Math. Phys., 355(3), 1189--1207. https://doi.org/10.1007/s00220-017-2966-y
    31. Farooq, M., & Steinwart, I. (2017). An SVM-like Approach for Expectile Regression. Comput. Statist. Data Anal., 109, 159--181. https://doi.org/10.1016/j.csda.2016.11.010
    32. Fechter, S., Munz, C.-D., Rohde, C., & Zeiler, C. (2017). A sharp interface method for compressible liquid-vapor flow with phase transition and surface tension. J. Comput. Phys., 336, 347–374. https://doi.org/10.1016/j.jcp.2017.02.001
    33. Fehr, J., Grunert, D., Bhatt, A., & Hassdonk, B. (2017). A Sensitivity Study of Error Estimation in Reduced Elastic Multibody  Systems. Proceedings of MATHMOD 2018, Vienna, Austria.
    34. Feistauer, M., Bartos, O., Roskovec, F., & S�ndig, 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/
    35. Feistauer, M., Roskovec, F., & S�ndig, 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
    36. Fukuizumi, R., Marzuola, J. L., Pelinovsky, D., & Schneider, G. (Eds.). (2017). Nonlinear partial differential equations on graphs. Abstracts from the workshop held June 18--24, 2017. Oberwolfach Rep., 14(2), 1805--1868.
    37. 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
    38. Gaspoz, F. D., Kreuzer, C., Siebert, K., & Ziegler, D. (2017). A convergent time-space adaptive $dG(s)$ finite element method for  parabolic problems motivated by equal error distribution. In Submitted. https://arxiv.org/abs/1610.06814
    39. Gaspoz, F. D., & Morin, P. (2017). APPROXIMATION CLASSES FOR ADAPTIVE HIGHER ORDER FINITE ELEMENT    APPROXIMATION (vol 83, pg 2127, 2014). MATHEMATICS OF COMPUTATION, 86(305), 1525–1526. https://doi.org/10.1090/mcom/3243
    40. Gaspoz, F. D., Morin, P., & Veeser, A. (2017). A posteriori error estimates with point sources in fractional sobolev spaces. NUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS, 33(4), 1018–1042. https://doi.org/10.1002/num.22065
    41. Geck, M. (2017). On the construction of semisimple Lie algebras and Chevalley groups. Proceedings of the American Mathematical Society, 145(8), 3233--3247. https://doi.org/10.1090/proc/13600
    42. Geck, M., & Müller, J. (2017). Invariant bilinear forms on W-graph representations and linear algebra over integral domains. Algorithmic and Experimental Methods in Algebra, Geometry and Number Theory (Eds. G. Böckle, W. Decker, G. Malle), 311–360. https://doi.org/10.1007/978-3-319-70566-8_13
    43. Geck, M. (2017). Minuscule weights and Chevalley                      groups. Finite Simple Groups: Thirty Years of the Atlas and Beyond (Celebrating the Atlases and Honoring John Conway, November 2-5, 2015 at Princeton University), 694, 159--176. https://doi.org/10.1090/conm/694/13955
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    45. Geck, M. (2017). On the modular composition factors of the Steinberg representation. Journal of Algebra, 475, 370--391. https://doi.org/10.1016/j.jalgebra.2015.11.005
    46. Giesselmann, J., Meyer, F., & Rohde, C. (2017). A posteriori error analysis for random scalar conservation laws using  the Stochastic Galerkin method.: Vol. (submitted). https://arxiv.org/abs/1709.04351
    47. Giesselmann, J., & Pryer, T. (2017). Goal-oriented error analysis of a DG scheme for a second gradient  elastodynamics model. In C. Cances & P. Omnes (Eds.), Finite Volumes for Complex Applications VIII-Methods and Theoretical  Aspects (Vol. 199). http://www.springer.com/de/book/9783319573960
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    49. Giesselmann, J., & Pryer, T. (2017). A posteriori analysis for dynamic model adaptation in convection  dominated problems. Math. Models Methods Appl. Sci. (M3AS), 27(13), 2381-- 2423. https://doi.org/10.1142/S0218202517500476
    50. Giesselmann, J., Lattanzio, C., & Tzavaras, A. E. (2017). Relative Energy for the Korteweg Theory and Related Hamiltonian Flows in Gas Dynamics. ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS, 223(3), 1427–1484. https://doi.org/10.1007/s00205-016-1063-2
    51. Griesemer, M., Schmid, J., & Schneider, G. (2017). On the dynamics of the mean-field polaron in the              high-frequency limit. Lett. Math. Phys., 107(10), 1809--1821. https://doi.org/10.1007/s11005-017-0969-4
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    53. 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
    54. 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
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    57. 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
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    67. 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.
    68. Kohr, M., Medkova, D., & Wendland, W. L. (2017). On the Oseen-Brinkman flow around an (m-1)-dimensional obstacle. Monatshefte F�r Mathematik, 483, 269–302. https://doi.org/MOFM-D16-00078
    69. 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.
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    72. 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
    73. 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. http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1759
    74. 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
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