Publikationen des Fachbereichs Mathematik

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

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

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

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

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

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

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

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

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

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

Buchfinck, P., Glas, S., & Haasdonk, B. (2022). Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems.

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

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

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

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

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

Frank, R., Laptev, A., & Weidl, T. (2022). Schrödinger Operators: Eigenvalues and Lieb–Thirring Inequalities. In Cambridge Studies in Advanced Mathematics (S. 512).

Frank, R. L., Laptev, A., & Weidl, T. (2022). An improved one-dimensional Hardy inequality. https://arxiv.org/abs/2204.00877

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.

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

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

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

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

Horsch, M. T., & Schembera, B. (2022). Documentation of epistemic metadata by a mid-level ontology of cognitive processes. Proc. JOWO 2022.

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

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

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

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.

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

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

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

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

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

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

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

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

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

Merkle, R., & Barth, A. (2022). On some distributional properties of subordinated Gaussian random fields. Methodol Comput Appl Probab.

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

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

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

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

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.

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

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

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

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.

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

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

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

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

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

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

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

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

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.

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.

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

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

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

Brencher, L., & Barth, A. (2021). Scalar conservation laws with stochastic discontinuous flux function. ArXiv e-prints, arXiv:2107.00549 math.NA.

Brencher, L., & Barth, A. (2021). Stochastic conservation laws with discontinuous flux functions: The multidimensional case.

Buchfink, P., Glas, S., & Haasdonk, B. (2021). Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds. https://doi.org/10.48550/arXiv.2112.10815

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

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

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.

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

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

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

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

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

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

Ehring, T., & Haasdonk, B. (2021). Greedy sampling and approximation for realizing feedback control for high dimensional nonlinear systems.

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

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

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

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

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

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

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

Haasdonk, B. (2021). Model Order Reduction, Applications, MOR Software (D. Gruyter, Hrsg.; Bd. 3). De Gruyter. https://doi.org/10.1515/9783110499001

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

Haasdonk, B., Wenzel, T., Santin, G., & Schmitt, S. (2021). Biomechanical Surrogate Modelling Using Stabilized Vectorial Greedy Kernel Methods.

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

Hahn, B. N. (2021). Motion compensation strategies in tomography. https://doi.org/10.1007/978-3-030-57784-1_3

Hamm, T., & Steinwart, I. (2021). Adaptive Learning Rates for Support Vector Machines Working on Data with Low Intrinsic Dimension. Ann. Statist.

Hamm, T., & Steinwart, I. (2021). Intrinsic Dimension Adaptive Partitioning for Kernel Methods. Fakultät für Mathematik und Physik, Universität Stuttgart.

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

Krämer, A., Maier, B., Rau, T., Huber, F., Klotz, T., Ertl, T., Göddeke, D., Mehl, M., Reina, G., & Röhrle, O. (2021). Multi-physics multi-scale HPC simulations of skeletal muscles. In W. E. Nagel, D. H. Kröner, & M. M. Resch (Hrsg.), High Performance Computing in Science and Engineering ’20: Transactions of the High Performance Computing Center, Stuttgart(HLRS) 2020. https://doi.org/10.1007/978-3-030-80602-6_13

Krämer, A., Maier, B., Rau, T., Huber, F., Klotz, T., Ertl, T., Göddeke, D., Mehl, M., Reina, G., & Röhrle, O. (2021). High Performance Computing in Science and Engineering 20 (W. E. Nagel, D. H. Kröner, & M. M. Resch, Hrsg.). Springer. https://doi.org/10.1007/978-3-030-80602-6_13

Kühnert, J., Göddeke, D., & Herschel, M. (2021, Juli). Provenance-integrated parameter selection and optimization in numerical simulations. 13th International Workshop on Theory and Practice ofProvenance (TaPP 2021). https://www.usenix.org/conference/tapp2021/presentation/kühnert

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

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Schneider, G. (2020). The KdV approximation for a system with unstable resonances. Math. Methods Appl. Sci., 43(6), 3185--3199.

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Carlberg, K., Brencher, L., Haasdonk, B., & Barth, A. (2019). Data-driven time parallelism via forecasting. SIAM Journal on Scientific Computing, 41(3), B466--B496.

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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

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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

Föll, R., Haasdonk, B., Hanselmann, M., & Ulmer, H. (2019). Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation. https://openreview.net/forum?id=BkgosiRcKm

Geck, M. (2019). Eigenvalues and Polynomial Equations. The American Mathematical Monthly, 126(10), 933--935. https://doi.org/10.1080/00029890.2019.1651168

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

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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

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

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

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

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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.

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

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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

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

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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.

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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

Santin, G., & Haasdonk, B. (2019). Kernel Methods for Surrogate Modelling. University of Stuttgart.

Santin, G., Wittwar, D., & Haasdonk, B. (2019). Sparse approximation of regularized kernel interpolation by greedy algorithms.

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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

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

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

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

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

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

Steinwart, I. (2019). A Sober Look at Neural Network Initializations. Fakultät für Mathematik und Physik, Universität Stuttgart.

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

Wenzel, T., Santin, G., & Haasdonk, B. (2019). A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution.

Wittwar, D., Santin, G., & Haasdonk, B. (2019). Part II on matrix valued kernels including analysis.

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.

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

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

Afkham, B. M., Bhatt, A., Haasdonk, B., & Hesthaven, J. S. (2018). Symplectic Model-Reduction with a Weighted Inner Product.

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

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

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

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

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

Bhatt, A., & Haasdonk, B. (2018). Certified and structure-preserving model order reduction of EMBS. In RAMSA 2017, New Delhi.

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.

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

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

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.

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

Buchfink, P. (2018). Structure-preserving Model Reduction for Elasticity [Diploma thesis].

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

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

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

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

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

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

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

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

Doering, M., Gyorfi, L., & Walk, H. (2018). Rate of Convergence of k-Nearest-Neighbor Classification Rule. JOURNAL OF MACHINE LEARNING RESEARCH, 18.

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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.

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

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

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).

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

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.

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

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

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

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Georgiev, V., & Wirth, J. (2018). Zero resonances for localised potentials. Journal of Mathematical Physics, 59(7), 071502. https://doi.org/10.1063/1.5027717

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

Gimperlein, H., Meyer, F., Özdemir, C., & Stephan, E. P. (2018). Time domain boundary elements for dynamic contact problems. Computer Methods in Applied Mechanics and Engineering, 333, 147–175. https://doi.org/10.1016/j.cma.2018.01.025

Gimperlein, H., Meyer, F., Özdemir, C., Stark, D., & Stephan, E. P. (2018). Boundary elements with mesh refinements for the wave equation. Numer. Math., 139(4), 867--912. https://doi.org/10.1007/s00211-018-0954-6

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

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

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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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

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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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.

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

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Wittwar, D., Schmidt, A., & Haasdonk, B. (2017). Reduced Basis Approximation for the Discrete-time Parametric Algebraic  Riccati Equation. University of Stuttgart.

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Antoulas, A. C., Haasdonk, B., & Peherstorfer, B. (2016). MORML 2016 Book of Abstracts. University of Stuttgart.

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Bhatt, A. (2016). Structure-preserving Finite Difference Methods for Linearly Damped  Differential Equations. University of Central Florida.

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

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

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

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

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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

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

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

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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

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

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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

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.

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.

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

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

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

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

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

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

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

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

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

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

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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

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

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

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

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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

Harbrecht, H., Wendland, W. L., & Zorii, N. (2016). Rapid solution of minimal Riesz energy problems. Numer. Methods Partial Diff. Equ., 32, 1535–1552.

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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

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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

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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

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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

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