Publications of the Department of Mathematics

Department of Mathematics

List of publications of the Department of Mathematics starting 2017

 

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

  1. 2024

    1. Alkämper, M., Magiera, J., & Rohde, C. (2024). An Interface-Preserving Moving Mesh in Multiple Space  Dimensions. ACM Trans. Math. Softw., 50(1), Article 1. https://doi.org/10.1145/3630000
    2. Braun, A., Kohler, M., Langer, S., & Walk, H. (2024). Convergence rates for shallow neural networks learned by gradient descent. Bernoulli, 30(1), Article 1. https://doi.org/10.3150/23-bej1605
    3. Corso, T. C., Hassan, M., Jha, A., & Stamm, B. (2024). An $L^2$-maximum principle for circular arcs on the disk.
    4. Kharitenko, A., & Scherer, C. W. (2024). On the exactness of a stability test for Lur’e systems with slope-restricted nonlinearities. IEEE Transactions on Automatic Control. https://doi.org/10.1109/TAC.2024.3362859
    5. Mel’nyk, T., & Rohde, C. (2024). Reduced-dimensional modelling for nonlinear convection-dominated flow in cylindric domains.
    6. Ruan, L., & Rybak, I. (2024). Stokes-Brinkman-Darcy models for coupled fluid-porous systems: derivation, analysis and validation. Appl. Math. Comp.  (Submitted).
    7. Strohbeck, P., & Rybak, I. (2024). Efficient preconditioners for coupled Stokes-Darcy problems. SIAM J. Sci. Comput. (Submitted).
  2. 2023

    1. Afşer, H., Györfi, L., & Walk, H. (2023). Classification With Repeated Observations. IEEE Signal Processing Letters, 30, 1522–1526. https://doi.org/10.1109/LSP.2023.3326057
    2. Bamer, F., Ebrahem, F., Markert, B., & Stamm, B. (2023). Molecular Mechanics of Disordered Solids. Archives of Computational Methods in Engineering, 30(3), Article 3. https://doi.org/10.1007/s11831-022-09861-1
    3. Berberich, J., Scherer, C. W., & Allgower, F. (2023). Combining Prior Knowledge and Data for Robust Controller Design. IEEE Transactions on Automatic Control, 68(8), Article 8. https://doi.org/10.1109/tac.2022.3209342
    4. Brehmer, P., Herbst, M. F., Wessel, S., Rizzi, M., & Stamm, B. (2023). Reduced basis surrogates for quantum spin systems based on tensor networks. Physical Review E. https://doi.org/10.1103/PhysRevE.108.025306
    5. 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
    6. Burbulla, S., Hörl, M., & Rohde, C. (2023). Flow in Porous Media with Fractures of Varying Aperture. Accepted by SIAM J. Sci. Comput. https://doi.org/10.48550/arXiv.2207.09301
    7. Cancès, E., Herbst, M. F., Kemlin, G., Levitt, A., & Stamm, B. (2023). Numerical stability and efficiency of response property calculations in density functional theory. Letters in Mathematical Physics, 113(1), Article 1. https://doi.org/10.1007/s11005-023-01645-3
    8. Dippon, J., Gwinner, J., Khan, A. A., & Sama, M. (2023). A new regularized stochastic approximation framework for stochastic inverse problems. Nonlinear Anal. Real World Appl., 73, Paper No. 103869, 29. https://doi.org/10.1016/j.nonrwa.2023.103869
    9. Dusson, G., Sigal, I. M., & Stamm, B. (2023). Analysis of the Feshbach-Schur method for the Fourier spectral discretizations of Schrödinger operators. Mathematics of Computation, 92(340), Article 340. https://doi.org/10.1090/mcom/3774
    10. Eggenweiler, E., Nickl, J., & Rybak, I. (2023). Justification of generalized interface conditions for Stokes-Darcy problems. In E. Franck, J. Fuhrmann, V. Michel-Dansac, & L. Navoret (Eds.), Finite Volumes for Complex Applications X - Volume 1, Elliptic and Parabolic Problems (pp. 275–283). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-40864-9_22
    11. Eggenweiler, E., & Rybak, I. (2023). Higher-order coupling conditions for arbitrary flows in Stokes-Darcy systems. J. Fluid Mech. (Submitted).
    12. Fukuizumi, R., Gao, Y., Schneider, G., & Takahashi, M. (2023). Pattern formation in 2D stochastic anisotropic Swift-Hohenberg equation. Interdiscip. Inform. Sci., 29(1), Article 1. https://doi.org/10.4036/iis.2023.a.03
    13. Gander, M. J., Lunowa, S. B., & Rohde, C. (2023). Consistent and Asymptotic-Preserving Finite-Volume Robin Transmission Conditions for Singularly Perturbed Elliptic Equations. In S. C. Brenner, E. Chung, A. Klawonn, F. Kwok, J. Xu, & J. Zou (Eds.), Domain Decomposition Methods in Science and Engineering XXVI (pp. 443--450). Springer International Publishing.
    14. Gander, M. J., Lunowa, S. B., & Rohde, C. (2023). Non-Overlapping Schwarz Waveform-Relaxation for Nonlinear Advection-Diffusion Equations. SIAM J. Sci. Comput., 45(1), Article 1. https://doi.org/10.1137/21M1415005
    15. Gladbach, P., Jansen, J., & Lienstromberg, C. (2023). Non-Newtonian thin-film equations: global existence of solutions, gradient-flow structure and guaranteed lift-off. https://doi.org/10.48550/ARXIV.2301.10300
    16. Gramlich, D., Holicki, T., Scherer, C. W., & Ebenbauer, C. (2023). A Structure Exploiting SDP Solver for Robust Controller Synthesis. IEEE Control Systems Letters, 7, 1831--1836. https://doi.org/10.1109/lcsys.2023.3277314
    17. Gramlich, D., Pauli, P., Scherer, C. W., Allgöwer, F., & Ebenbauer, C. (2023). Convolutional Neural Networks as 2-D systems. https://doi.org/10.48550/ARXIV.2303.03042
    18. Gramlich, D., Scherer, C. W., Häring, H., & Ebenbauer, C. (2023). Synthesis of constrained robust feedback policies and model predictive control. https://doi.org/10.48550/ARXIV.2310.11404
    19. Griesemer, M., & Hofacker, M. (2023). On the weakness of short-range interactions in Fermi gases. Lett. Math. Phys., 113(1), Article 1. https://doi.org/10.1007/s11005-022-01624-0
    20. Györfi, L., Linder, T., & Walk, H. (2023). Lossless Transformations and Excess Risk Bounds in Statistical Inference. Entropy, 25(10), Article 10. https://doi.org/10.3390/e25101394
    21. Haas, T., de Rijk, B., & Schneider, G. (2023). Validity of Whitham’s modulation equations for dissipative systems with a conservation law: phase dynamics in a generalized Ginzburg-Landau system. Indiana Univ. Math. J., 72(1), Article 1. https://doi.org/10.1512/iumj.2023.72.9297
    22. Hahn, B., & Wirth, B. (2023). Convex reconstruction of moving particles with inexact motion model. PAMM, 23(2), Article 2. https://doi.org/10.1002/pamm.202300054
    23. Hahn, B. N., Rigaud, G., & Schmähl, R. (2023). A class of regularizations based on nonlinear isotropic diffusion for inverse problems. IMA Journal of Numerical Analysis. https://doi.org/10.1093/imanum/drad002
    24. Heß, M., & Schneider, G. (2023). A robust way to justify the derivative NLS approximation. Z. Angew. Math. Phys., 74(6), Article 6. https://doi.org/10.1007/s00033-023-02121-7
    25. Hilder, B., de Rijk, B., & Schneider, G. (2023). Nonlinear stability of periodic roll solutions in the real Ginzburg-Landau equation against $C_ub^m$-perturbations. Comm. Math. Phys., 400(1), Article 1. https://doi.org/10.1007/s00220-022-04619-z
    26. Hilder, B., de Rijk, B., & Schneider, G. (2023). Moving modulating pulse and front solutions of permanent form in a FPU model with nearest and next-to-nearest neighbor interaction. SIAM J. Appl. Dyn. Syst., 22(2), Article 2. https://doi.org/10.1137/22M1502902
    27. Holicki, T., & Scherer, C. W. (2023). IQC based analysis and estimator design for discrete-time systems affected by impulsive uncertainties. Nonlinear Analysis: Hybrid Systems, 50, 101399. https://doi.org/10.1016/j.nahs.2023.101399
    28. Holzmüller, D., Zaverkin, V., Kästner, J., & Steinwart, I. (2023). A Framework and Benchmark for Deep Batch Active Learning for Regression. Journal of Machine Learning Research, 24(164), Article 164. http://jmlr.org/papers/v24/22-0937.html
    29. Hornischer, N. (2023). Model Order Reduction with Dynamically Transformed Modes for Electrophysiological Simulations. GAMM Archive for Students.
    30. Jha, A., Nottoli, M., Mikhalev, A., Quan, C., & Stamm, B. (2023). Linear Scaling Computation of Forces for the Domain-Decomposition Linear Poisson--Boltzmann Method. The Journal of Chemical Physics, 158, 104105. https://doi.org/10.1063/5.0141025
    31. Keckstein, S., Dippon, J., Hudelist, G., Koninckx, P., Condous, G., Schroeder, L., & Keckstein, J. (2023). Sonomorphologic Changes in Colorectal Deep Endometriosis: The Long-Term Impact of Age and Hormonal Treatment. Ultraschall in Der Medizin - European Journal of Ultrasound, EFirst, Article EFirst. https://doi.org/10.1055/a-2209-5653
    32. 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
    33. Kharitenko, A., & Scherer, C. (2023). Time-varying Zames–Falb multipliers for LTI Systems are superfluous. Automatica, 147. https://doi.org/10.1016/j.automatica.2022.110577
    34. Lienstromberg, C., & Velázquez, J. J. L. (2023). Long-time asymptotics and regularity estimates for weak solutions to a doubly degenerate thin-film equation in the Taylor-Couette setting. arXiv, to appear in Pure and Applied Analysis. https://doi.org/10.48550/ARXIV.2203.00075
    35. Maier, D., Reichel, W., & Schneider, G. (2023). Breather solutions for a semilinear Klein-Gordon equation on a periodic metric graph. J. Math. Anal. Appl., 528(2), Article 2. https://doi.org/10.1016/j.jmaa.2023.127520
    36. Meijer, T. J., Holicki, T., Eijnden, S. J. A. M. van den, Scherer, C. W., & Heemels, W. P. M. H. (2023). The Non-Strict Projection Lemma. arXiv. https://doi.org/10.48550/ARXIV.2305.08735
    37. Mel’nyk, T. (2023). Complex Analysis (1; Issue 1). Springer Cham. https://doi.org/10.1007/978-3-031-39615-1
    38. Mel’nyk, T., & Rohde, C. (2023). Asymptotic approximations for semilinear parabolic convection-dominated transport problems in thin graph-like networks. In arXiv e-prints. https://doi.org/10.48550/arXiv.2302.10105
    39. Mel’nyk, T., & Rohde, C. (2023). Puiseux asymptotic expansions for convection-dominated transport problems in thin graph-like networks: strong boundary interactions. /brokenurl#  https://doi.org/10.48550/arXiv.2307.02387
    40. Mel’nyk, T. A. (2023). Asymptotic analysis of spectral problems in thick junctions with the branched fractal structure. Mathematical Methods in the Applied Sciences, 46(3), Article 3. https://doi.org/10.1002/mma.8692
    41. 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
    42. Morato, M. M., Holicki, T., & Scherer, C. W. (2023). Stabilizing Model Predictive Control Synthesis using Integral Quadratic Constraints and Full-Block Multipliers. International Journal of Robust and Nonlinear Control, 33(18), Article 18. https://doi.org/10.1002/rnc.6952
    43. Nagy, P.-A., & Semmelmann, U. (2023). Eigenvalue estimates for 3-Sasaki structures.
    44. Nottoli, M., Bondanza, M., Mazzeo, P., Cupellini, L., Curutchet, C., Loco, D., Lagardère, L., Piquemal, J., Mennucci, B., & Lipparini, F. (2023). QM/AMOEBA description of properties and dynamics of embedded molecules. WIREs Computational Molecular Science, 13(6), Article 6. https://doi.org/10.1002/wcms.1674
    45. Pelinovsky, D., & Schneider, G. (2023). KP-II approximation for a scalar Fermi-Pasta-Ul system on a 2D square lattice. SIAM J. Appl. Math., 83(1), Article 1. https://doi.org/10.1137/22M1509369
    46. Pes, F., Polack, É., Mazzeo, P., Dusson, G., Stamm, B., & Lipparini, F. (2023). A Quasi Time-Reversible Scheme Based on Density Matrix Extrapolation on the Grassmann Manifold for Born–Oppenheimer Molecular Dynamics. The Journal of Physical Chemistry Letters, 9720--9726. https://doi.org/10.1021/acs.jpclett.3c02098
    47. Scherer, C. W. (2023). Robust Exponential Stability and Invariance Guarantees with General Dynamic O’Shea-Zames-Falb Multipliers. https://doi.org/10.48550/ARXIV.2306.00571
    48. Schwahn, P., Semmelmann, U., & Weingart, G. (2023). Stability of the Non-Symmetric Space $E_7/PSO(8)$.
    49. Seus, D., Radu, F. A., & Rohde, C. (2023). Towards hybrid two-phase modelling using linear domain decomposition. Numer. Methods Partial Differential Equations, 39(1), Article 1. https://doi.org/10.1002/num.22906
    50. Theisen, L., & Stamm, B. (2023). A Scalable Two-Level Domain Decomposition Eigensolver for Periodic Schrödinger Eigenstates in Anisotropically Expanding Domains. https://doi.org/10.48550/arXiv.2311.08757
    51. Zaverkin, V., Holzmüller, D., Bonfirraro, L., & Kästner, J. (2023). Transfer learning for chemically accurate interatomic neural network potentials. Phys. Chem. Chem. Phys., 25(7), Article 7. https://doi.org/10.1039/D2CP05793J
  3. 2022

    1. Agullo, E., Altenbernd, M., Anzt, H., Bautista-Gomez, L., Benacchio, T., Bonaventura, L., Bungartz, H.-J., Chatterjee, S., Ciorba, F. M., DeBardeleben, N., Drzisga, D., Eibl, S., Engelmann, C., Gansterer, W. N., Giraud, L., Göddeke, D., Heisig, M., Jézéquel, F., Kohl, N., … Wohlmuth, B. (2022). Resiliency in numerical algorithm design for extreme scale simulations. The International Journal of High Performance ComputingApplications, 36(2), Article 2. https://doi.org/10.1177/10943420211055188
    2. Assenmacher, O., Bruell, G., & Lienstromberg, C. (2022). Non-Newtonian two-phase thin-film problem: local existence, uniqueness, and stability. Comm. Partial Differential Equations, 47(1), Article 1. https://doi.org/10.1080/03605302.2021.1957929
    3. Benner, P., Burger, M., Göddeke, D., Görgen, C., Himpe, C., Heiland, J., Koprucki, T., Ohlberger, M., Rave, S., Reiselbach, M., Saak, J., Schöbel, A., Tabelow, K., & Weber, M. (2022). Die mathematische Forschungsdateninitiative in der NFDI:  MaRDI (Mathematical Research Data Initiative). GAMM Rundbrief, 2022(1), Article 1.
    4. 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
    5. 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
    6. 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
    7. Buchfinck, P., Glas, S., & Haasdonk, B. (2022). Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems.
    8. Buchfink, P., Glas, S., & Haasdonk, B. (2022). Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems. IFAC-PapersOnLine, 55(20), Article 20. https://doi.org/10.1016/j.ifacol.2022.09.138
    9. Burbulla, S., Dedner, A., Hörl, M., & Rohde, C. (2022). Dune-MMesh: The Dune Grid Module for Moving Interfaces. J. Open Source Softw., 7(74), Article 74. https://doi.org/10.21105/joss.03959
    10. 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
    11. Cekić, M., Lefeuvre, T., Moroianu, A., & Semmelmann, U. (2022). Towards Brin’s conjecture on frame flow ergodicity: new progress and perspectives.
    12. Echterdiek, F., Kitterer, D., Dippon, J., Ott, M., Paul, G., Latus, J., & Schwenger, V. (2022). Outcome of kidney transplantations from ≥65‐year‐old deceased donors with acute kidney injury. Clinical Transplantation, 36(5), Article 5. https://doi.org/10.1111/ctr.14612
    13. Echterdiek, F., Tilgener, C., Dippon, J., Kitterer, D., Scheder-Bieschin, J., Paul, G., Ott, M., Humke, U., Schwenger, V., & Latus, J. (2022). Impact of the explanting surgeon’s impression of donor arteriosclerosis on outcome of kidney transplantations from donors aged ≥65 years. Langenbeck’s Archives of Surgery, 407(2), Article 2. https://doi.org/10.1007/s00423-021-02383-7
    14. 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
    15. 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
    16. Fiedler, C., Scherer, C. W., & Trimpe, S. (2022). Learning Functions and Uncertainty Sets Using Geometrically Constrained Kernel Regression. 61st IEEE Conf. Decision and Control, 2141–2146. https://doi.org/10.1109/cdc51059.2022.9993144
    17. Frank, R. L., Laptev, A., & Weidl, T. (2022). An improved one-dimensional Hardy inequality. J. Math. Sci. (N.Y.), 268(3, Problems in mathematical analysis. No. 118), Article 3, Problems in mathematical analysis. No. 118. https://doi.org/10.1007/s10958-022-06199-8
    18. Frank, R., Laptev, A., & Weidl, T. (2022). Schrödinger Operators: Eigenvalues and Lieb–Thirring Inequalities (p. 512). Cambridge University Press.
    19. Fukuizumi, R., & Schneider, G. (2022). Interchanging space and time in nonlinear optics modeling and dispersion management models. J. Nonlinear Sci., 32(3), Article 3. https://doi.org/10.1007/s00332-022-09788-8
    20. Gavrilenko, P., Haasdonk, B., Iliev, O., Ohlberger, M., Schindler, F., Toktaliev, P., Wenzel, T., & Youssef, M. (2022). A Full Order, Reduced Order and Machine Learning Model Pipeline for Efficient Prediction of Reactive Flows. In I. Lirkov & S. Margenov (Eds.), Large-Scale Scientific Computing (pp. 378--386). Springer International Publishing.
    21. Gilg, S., Schneider, G., & Uecker, H. (2022). Nonlinear dynamics of modulated waves on graphene like quantum graphs. Math. Nachr., 295(11), Article 11. https://doi.org/10.1002/mana.202100009
    22. Gramlich, D., Ebenbauer, C., & Scherer, C. W. (2022). Synthesis of Accelerated Gradient Algorithms for Optimization and Saddle Point Problems using Lyapunov functions. Syst. Control Lett., 165. https://doi.org/doi.org/10.1016/j.sysconle.2022.105271
    23. Gramlich, D., Scherer, C. W., & Ebenbauer, C. (2022). Robust Differential Dynamic Programming. 61st IEEE Conf. Decision and Control. https://doi.org/10.1109/cdc51059.2022.9992569
    24. Griesemer, M. (2022). Ground states of atoms and molecules in non-relativistic QED. In The Physics and Mathematics of Elliott Lieb (pp. 437--450). EMS Press. https://doi.org/10.4171/90-1/18
    25. Griesemer, M., & Hofacker, M. (2022). From Short-Range to Contact Interactions in Two-dimensional Many-Body Quantum Systems. Annales Henri Poincaré, 23(8), Article 8. https://doi.org/10.1007/s00023-021-01149-7
    26. 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
    27. Hahn, B. N., Garrido, M.-L. K., Klingenberg, C., & Warnecke, S. (2022). Using the Navier-Cauchy equation for motion estimation in dynamic imaging. Inverse Problems and Imaging, 0(0), Article 0. https://doi.org/10.3934/ipi.2022018
    28. Hassan, M., Williamson, C., Baptiste, J., Braun, S., Stace, A. J., Besley, E., & Stamm, B. (2022). Manipulating Interactions between Dielectric Particles with Electric Fields : A General Electrostatic Many-Body Framework. Journal of Chemical Theory and Computation, 18(10), Article 10. https://doi.org/10.1021/acs.jctc.2c00008
    29. Hilder, B. (2022). Modulating traveling fronts in a dispersive Swift-Hohenberg equation coupled to an additional conservation law. J. Math. Anal. Appl., 513(2), Article 2. https://doi.org/10.1016/j.jmaa.2022.126224
    30. Hilder, B., & Sharma, U. (2022). Quantitative coarse-graining of Markov chains.
    31. Holicki, T. (2022). A Complete Analysis and Design Framework for Linear Impulsive and Related Hybrid Systems [University of Stuttgart]. https://doi.org/10.18419/opus-12158
    32. Holicki, T., & Scherer, C. W. (2022). A Dynamic S-Procedure for Dynamic Uncertainties. IFAC-PapersOnline, 55(25), Article 25. https://doi.org/10.1016/j.ifacol.2022.09.331
    33. Holicki, T., & Scherer, C. W. (2022). Input-Output-Data-Enhanced Robust Analysis via Lifting.
    34. Holicki, T., & Scherer, C. W. (2022). IQC Based Analysis and Estimator Design for Discrete-Time Systems Affected by Impulsive Uncertainties.
    35. Holzmüller, D., & Steinwart, I. (2022). Training two-layer ReLU networks with gradient descent is inconsistent. Journal of Machine Learning Research, 23(181), Article 181. http://jmlr.org/papers/v23/20-830.html
    36. Hornischer, N. (2022). Model Order Reduction with Transformed Modes for Electrophysiological Simulations [Bathesis].
    37. Horsch, M. T., & Schembera, B. (2022). Documentation of epistemic metadata by a mid-level ontology of cognitive processes. Proc. JOWO 2022.
    38. 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
    39. Hägele, D., Schulz, C., Beschle, C., Booth, H., Butt, M., Barth, A., Deussen, O., & Weiskopf, D. (2022). Uncertainty Visualization: Fundamentals and Recent Developments. It - Information Technology, 64(4–5), Article 4–5. https://doi.org/10.1515/itit-2022-0033
    40. Jansen, J., Lienstromberg, C., & Nik, K. (2022). Long-time behaviour and stability for quasilinear doubly degenerate parabolic equations of higher order. arXiv. https://doi.org/10.48550/ARXIV.2204.08231
    41. 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
    42. Klink, M. (2022). Time Error Estimators and Adaptive Time-stepping Schemes [Bathesis].
    43. Klumpp, M., & Schneider, G. (2022). The Schrödinger approximation for the Helmholtz equation if the refractive index is a step function. Wave Motion, 110, Paper No. 102891, 6. https://doi.org/10.1016/j.wavemoti.2022.102891
    44. Klumpp, M., & Schneider, G. (2022). A note on the validity of the Schrödinger approximation for the Helmholtz equation. J. Appl. Anal., 28(1), Article 1. https://doi.org/10.1515/jaa-2021-2058
    45. Kohr, M., Mikhailov, S. E., & Wendland, W. L. (2022). On some mixed-transmission problems for the anisotropic Stokes and Navier-Stokes systems in Lipschitz domains with transversal interfaces. JMAA, 516(1, 126464), Article 1, 126464. https://doi.org/10.1016/j.jmaa.2022.126464
    46. 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, 47.
    47. 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).
    48. Leiteritz, R., Buchfink, P., Haasdonk, B., & Pflüger, D. (2022). Surrogate-data-enriched Physics-Aware Neural Networks. Proceedings of the Northern Lights Deep Learning Workshop 2022, 3. https://doi.org/10.7557/18.6268
    49. Lienstromberg, C., Pernas-Castano, T., & Velázquez, J. J. L. (2022). Analysis of a two-fluid Taylor-Couette flow with one non-Newtonian fluid. J. Nonlinear Sci., 32(2), Article 2. https://doi.org/10.1007/s00332-021-09750-0
    50. 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
    51. Magiera, J., & Rohde, C. (2022). Analysis and Numerics of Sharp and Diffuse Interface Models for Droplet Dynamics (K. Schulte, C. Tropea, & B. Weigand, Eds.; pp. 67–86). Springer International Publishing. https://doi.org/10.1007/978-3-031-09008-0_4
    52. Maier, B., Göddeke, D., Huber, F., Klotz, T., Röhrle, O., & Schulte, M. (2022). OpenDiHu: An Efficient and Scalable Framework for Biophysical Simulations of the Neuromuscular System.
    53. 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
    54. Melnyk, T., & Rohde, C. (2022). Asymptotic expansion for convection-dominated transport in a thin graph-like junction. In arXiv e-prints. https://doi.org/10.48550/ARXIV.2208.05812
    55. Mel’nyk, T., & Klevtsovskiy, A. V. (2022). Asymptotic expansion for the solution of a convection-diffusion problem in a thin graph-like junction. Asymptotic Analysis, 130(3–4), Article 3–4. https://doi.org/10.3233/ASY-221761
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    57. 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
    58. Merkle, R., & Barth, A. (2022). On some distributional properties of subordinated Gaussian random fields. Methodol Comput Appl Probab.
    59. Mikhalev, A., Nottoli, M., & Stamm, B. (2022). Linearly scaling computation of ddPCM solvation energy and forces using the fast multipole method. The Journal of Chemical Physics, 157(11), Article 11. https://doi.org/10.1063/5.0104536
    60. Nitzsche, M., Albers, H., Kluth, T., & Hahn, B. (2022). Compensating model imperfections during image reconstruction via Resesop. International Journal on Magnetic Particle Imaging, Vol 8 No 1 Suppl 1 (2022). https://doi.org/10.18416/IJMPI.2022.2203062
    61. Nottoli, M., Mikhalev, A., Stamm, B., & Lipparini, F. (2022). Coarse-Graining ddCOSMO through an Interface between Tinker and the ddX Library. The Journal of Physical Chemistry B, 126(43), Article 43. https://doi.org/10.1021/acs.jpcb.2c04579
    62. 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
    63. Santin, G., Karvonen, T., & Haasdonk, B. (2022). Sampling based approximation of linear functionals in reproducing kernel Hilbert spaces. BIT - Numerical Mathematics, 62(1), Article 1. https://doi.org/10.1007/s10543-021-00870-3
    64. Scherer, C. (2022). Dissipativity and Integral Quadratic Constraints, Tailored computational robustness tests for complex interconnections. IEEE Control Systems Magazine, 42(3), Article 3. https://doi.org/10.1109/MCS.2022.3157117
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    67. Shuva, S., Buchfink, P., Röhrle, O., & Haasdonk, B. (2022). Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue. In I. Lirkov & S. Margenov (Eds.), Large-Scale Scientific Computing (pp. 402--409). Springer International Publishing.
    68. Stamm, B., & Theisen, L. (2022). A Quasi-Optimal Factorization Preconditioner for Periodic Schrödinger Eigenstates in Anisotropically Expanding Domains. SIAM Journal on Numerical Analysis, 60(5), Article 5. https://doi.org/10.1137/21m1456005
    69. 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
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  4. 2021

    1. Alkämper, M., Magiera, J., & Rohde, C. (2021). An Interface Preserving Moving Mesh in Multiple SpaceDimensions. Computing Research Repository, abs/2112.11956. https://arxiv.org/abs/2112.11956
    2. Altenbernd, M., Dreier, N.-A., Engwer, C., & Göddeke, D. (2021). Towards Local-Failure Local-Recovery in PDE Frameworks: The Case of Linear Solvers. In T. Kozubek, P. Arbenz, J. Jaros, L. Ríha, J. Sístek, & P. Tichý (Eds.), High Performance Computing in Science and Engineering -- HPCSE 2019 (Vol. 12456, pp. 17--38). Springer. https://doi.org/10.1007/978-3-030-67077-1_2
    3. Altmann, K., & Witt, F. (2021). Toric co-Higgs sheaves. Journal of Pure and Applied Algebra, 225(8), Article 8. https://doi.org/10.1016/j.jpaa.2020.106634
    4. Barth, A., & Merkle, R. (2021). Multilevel Monte Carlo estimators for elliptic PDEs with Lévy-type diffusion coefficient. ArXiv E-Prints, ArXiv:2108.05604 Math.NA.
    5. Beck, A., Dürrwächter, J., Kuhn, T., Meyer, F., Munz, C.-D., & Rohde, C. (2021). Uncertainty Quantification in High Performance Computational Fluid Dynamics. In W. E. Nagel, D. H. Kröner, & M. M. Resch (Eds.), High Performance Computing in Science and Engineering ’19 (pp. 355--371). Springer International Publishing.
    6. Benacchio, T., Bonaventura, L., Altenbernd, M., Cantwell, C. D., Düben, P. D., Gillard, M., Giraud, L., Göddeke, D., Raffin, E., Teranishi, K., & Wedi, N. (2021). Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction. The International Journal of High Performance Computing Applications (Online First). https://doi.org/10.1177/1094342021990433
    7. 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 (Eds.), EMS Series of Congress Reports. EMS Press, Berlin. https://doi.org/10.4171/ECR/18
    8. Berrett, T. B., Gyorfi, L., & Walk, H. (2021). Strongly universally consistent nonparametric regression and    classification with privatised data. ELECTRONIC JOURNAL OF STATISTICS, 15(1), Article 1. https://doi.org/10.1214/21-EJS1845
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    15. de Rijk, B., & Schneider, G. (2021). Global existence and decay in multi-component reaction-diffusion-advection systems with different              velocities: oscillations in time and frequency. NoDEA Nonlinear Differential Equations Appl., 28(1), Article 1. https://doi.org/10.1007/s00030-020-00665-5
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    17. 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
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    19. 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
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    34. Hahn, B. N. (2021). Motion compensation strategies in tomography. https://doi.org/10.1007/978-3-030-57784-1_3
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    37. Hamm, T., & Steinwart, I. (2021). Adaptive Learning Rates for Support Vector Machines Working on Data with Low Intrinsic Dimension. Ann. Statist., 49, 3153--3180. https://doi.org/10.1214/21-AOS2078
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    76. Veenman, J., Scherer, C. W., Ardura, C., Bennani, S., Preda, V., & Girouart, B. (2021). IQClab: A new IQC based toolbox for robustness analysis and control design. IFAC-PapersOnline, 54(8), Article 8. https://doi.org/10.1016/j.ifacol.2021.08.583
    77. von Wolff, L., Weinhardt, F., Class, H., Hommel, J., & Rohde, C. (2021). Investigation of Crystal Growth in Enzymatically Induced Calcite Precipitation by Micro-Fluidic Experimental Methods and Comparison with Mathematical Modeling. Transport in Porous Media, 137(2), Article 2. https://doi.org/10.1007/s11242-021-01560-y
    78. Wenzel, T., Santin, G., & Haasdonk, B. (2021). A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability and uniform point distribution.
    79. Wenzel, T., Santin, G., & Haasdonk, B. (2021). Analysis of target data-dependent greedy kernel algorithms: Convergence rates for f-, f P- and f/P-greedy. arXiv. https://doi.org/10.48550/ARXIV.2105.07411
    80. Wenzel, T., Santin, G., & Haasdonk, B. (2021). Universality and Optimality of Structured Deep Kernel Networks. arXiv. https://doi.org/10.48550/ARXIV.2105.07228
    81. Wittwar, D., & Haasdonk, B. (n.d.). Convergence rates for matrix P-greedy variants. In Numerical mathematics and advanced applications---ENUMATH              2019 (Vol. 139, pp. 1195--1203). Springer, Cham. https://doi.org/10.1007/978-3-030-55874-1\_119
    82. Zaverkin, V., Kästner, J., Holzmüller, D., & Steinwart, I. (2021). Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments. J. Chem. Theory Comput. https://doi.org/10.1021/acs.jctc.1c00527
  5. 2020

    1. Alla, A., Haasdonk, B., & Schmidt, A. (2020). Feedback control of parametrized PDEs via model order              reduction and dynamic programming principle. Adv. Comput. Math., 46(1), Article 1. https://doi.org/10.1007/s10444-020-09744-8
    2. Barberis, M. L., Moroianu, A., & Semmelmann, U. (2020). Generalized vector cross products and Killing forms on negatively curved manifolds. Geom. Dedicata, 205, 113--127. https://doi.org/10.1007/s10711-019-00467-9
    3. Barreau, M., Scherer, C. W., Gouaisbaut, F., & Seuret, A. (2020). Integral Quadratic Constraints on Linear Infinite-dimensional Systems for Robust Stability Analysis. IFAC-PapersOnline, 53(2), Article 2. https://www.sciencedirect.com/science/article/pii/S2405896320321297
    4. Barth, A., & Merkle, R. (2020). Subordinated Gaussian Random Fields in Elliptic Partial Differential Equations. ArXiv E-Prints, ArXiv:2011.09311 Math.NA.
    5. Barth, A., & Merkle, R. (2020). Subordinated Gaussian Random Fields. ArXiv E-Prints, ArXiv:2012.06353 Math.PR.
    6. Bastian, P., Altenbernd, M., Dreier, N.-A., Engwer, C., Fahlke, J., Fritze, R., Geveler, M., Göddeke, D., Iliev, O., Ippisch, O., Mohring, J., Müthing, S., Ohlberger, M., Ribbrock, D., Shegunov, N., & Turek, S. (2020). Exa-Dune - Flexible PDE Solvers, Numerical Methods and Applications. In H.-J. Bungartz, S. Reiz, B. Uekermann, P. Neumann, & W. E. Nagel (Eds.), Software for Exascale Computing -- SPPEXA 2016--2019 (pp. 225--269). Springer. https://doi.org/10.1007/978-3-030-47956-5_9
    7. Baumstark, S., Schneider, G., & Schratz, K. (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.
    8. 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), Article 4. https://doi.org/10.1007/s10884-019-09791-w
    9. Beck, A., Dürrwächter, J., Kuhn, T., Meyer, F., Munz, C.-D., & Rohde, C. (2020). $hp$-Multilevel Monte Carlo methods for uncertainty quantification of compressible flows. SIAM J. Sci. Comput., 42(4), Article 4. https://doi.org/10.1137/18M1210575
    10. Berberich, J., Koch, A., Scherer, C. W., & Allgöwer, F. (2020). Robust data-driven state-feedback design. 2020 American Control Conference (ACC), 1532–1538. https://doi.org/10.23919/acc45564.2020.9147320
    11. Berre, I., Boon, W. M., Flemisch, B., Fumagalli, A., Gläser, D., Keilegavlen, E., Scotti, A., Stefansson, I., Tatomir, A., Brenner, K., Burbulla, S., Devloo, P., Duran, O., Favino, M., Hennicker, J., Lee, I.-H., Lipnikov, K., Masson, R., Mosthaf, K., … Zulian, P. (2020). Verification benchmarks for single-phase flow in three-dimensional fractured porous media.
    12. Bitter, A. (2020). Virtual levels of multi-particle quantum systems and their implications for the Efimov effect [Dissertation, Universität Stuttgart]. https://doi.org/10.18419/opus-11315
    13. Blanke, S. E., Hahn, B. N., & Wald, A. (2020). Inverse problems with inexact forward operator: iterative regularization and application in dynamic imaging. Inverse Problems, 36(12), Article 12. https://doi.org/10.1088/1361-6420/abb5e1
    14. Brencher, L., & Barth, A. (2020). Hyperbolic Conservation Laws with Stochastic Discontinuous Flux Functions. International Conference on Finite Volumes for Complex Applications, 265--273.
    15. Bringedal, C., von Wolff, L., & Pop, I. S. (2020). Phase Field Modeling of Precipitation and Dissolution Processes in Porous Media: Upscaling and Numerical Experiments. Multiscale Modeling &amp$\mathsemicolon$ Simulation, 18(2), Article 2. https://doi.org/10.1137/19m1239003
    16. 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
    17. 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
    18. de Rijk, B., & Schneider, G. (2020). Global Existence and Decay in Nonlinearly Coupled Reaction-Diffusion-Advection Equations with Different Velocities. J. Differential Equations, 268(7), Article 7. https://doi.org/10.1016/j.jde.2019.09.056
    19. Díaz-Ramos, J. C., Domínguez-Vázquez, M., & Kollross, A. (2020). On homogeneous manifolds whose isotropy actions are polar. Manuscripta Mathematica, 161(1), Article 1. https://doi.org/10.1007/s00229-018-1077-1
    20. 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
    21. Escher, J., Knopf, P., Lienstromberg, C., & Matioc, B.-V. (2020). Stratified periodic water waves with singular density gradients. Ann. Mat. Pura Appl. (4), 199(5), Article 5. https://doi.org/10.1007/s10231-020-00950-1
    22. IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart,  Germany, May 22-25, 2018: MORCOS 2018. (2020). In J. Fehr & B. Haasdonk (Eds.), IUTAM Bookseries. Springer.
    23. Fischer, S., & Steinwart, I. (2020). Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm. J. Mach. Learn. Res., 205, Article 205.
    24. 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
    25. Geck, M. (2020). Green functions and Glauberman degree-divisibility. Annals of Mathematics, 192(1), Article 1. https://doi.org/10.4007/annals.2020.192.1.4
    26. Geck, M. (2020). On Jacob’s construction of the rational canonical form of a matrix. The Electronic Journal of Linear Algebra, 36(36), Article 36. https://doi.org/10.13001/ela.2020.5055
    27. Geck, M. (2020). Computing Green functions in small characteristic. Journal of Algebra, 561, 163--199. https://doi.org/10.1016/j.jalgebra.2019.12.016
    28. Geck, M. (2020). ChevLie: Constructing Lie algebras and Chevalley groups. Journal of Software for Algebra and Geometry, 10(1), Article 1. https://doi.org/10.2140/jsag.2020.10.41
    29. 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
    30. 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
    31. 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), Article 3. https://doi.org/10.1007/s10543-019-00794-z
    32. 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.
    33. Giraud, L., Rüde, U., & Stals, L. (2020). Resiliency in Numerical Algorithm Design for Extreme Scale Simulations (Dagstuhl Seminar 20101). Dagstuhl Reports, 10(3), Article 3. https://doi.org/10.4230/DagRep.10.3.1
    34. Griesemer, M., Hofacker, M., & Linden, U. (2020). From short-range to contact interactions in the 1d Bose gas. Math. Phys. Anal. Geom., 23(2), Article 2. https://doi.org/10.1007/s11040-020-09344-4
    35. Grunert, D., Fehr, J., & Haasdonk, B. (2020). Well-scaled, a-posteriori error estimation for model order reduction of large second-order mechanical systems. ZAMM, 100(8), Article 8. https://doi.org/10.1002/zamm.201900186
    36. Göddeke, D., Schirwon, M., & Borg, N. (2020). Smartphone-Apps im Mathematikstudium. https://doi.org/10.18419/darus-1147
    37. Haas, T., de Rijk, B., & Schneider, G. (2020). MODULATION EQUATIONS NEAR THE ECKHAUS BOUNDARY: THE KdV EQUATION. SIAM JOURNAL ON MATHEMATICAL ANALYSIS, 52(6), Article 6. https://doi.org/10.1137/19M1266873
    38. 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
    39. Haasdonk, B., Hamzi, B., Santin, G., & Wittwar, D. (2020). Greedy kernel methods for center manifold approximation. In Spectral and high order methods for partial differential              equations---ICOSAHOM 2018 (Vol. 134, pp. 95--106). Springer, Cham. https://doi.org/10.1007/978-3-030-39647-3\_6
    40. Hilder, B. (2020). Modulating traveling fronts for the Swift-Hohenberg equation in the case of an additional conservation law. Journal of Differential Equations, 269(5), Article 5. https://doi.org/10.1016/j.jde.2020.03.033
    41. 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), Article 5. https://doi.org/10.1016/j.spa.2019.07.012
    42. Holicki, T., & Scherer, C. W. (2020). Output-Feedback Synthesis for a Class of Aperiodic Impulsive Systems. IFAC-PapersOnline, 53(2), Article 2. https://doi.org/10.1016/j.ifacol.2020.12.981
    43. Holzmüller, D., & Steinwart, I. (2020). Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent. Fakultät für Mathematik und Physik, Universität Stuttgart.
    44. Häufle, D. F. B., Wochner, I., Holzmüller, D., Driess, D., Günther, M., & Schmitt, S. (2020). Muscles Reduce Neuronal Information Load : Quantification of Control Effort in Biological vs. Robotic Pointing and Walking. Frontiers In Robotics and AI, 7, 77. https://doi.org/10.3389/frobt.2020.00077
    45. Jentsch, T., & Weingart, G. (2020). RIEMANNIAN AND KAHLERIAN NORMAL COORDINATES. ASIAN JOURNAL OF MATHEMATICS, 24(3), Article 3.
    46. Kennedy, J. B., & Lang, R. (2020). On the eigenvalues of quantum graph Laplacians with large complex δ couplings. Portugaliae Mathematica. A Journal of the Portuguese Mathematical Society, 77(2), Article 2.
    47. 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
    48. Kohr, M., Mikhailov, S. E., & Wendland, W. L. (2020). Potentials and transmission problems in weighted Sobolev spaces for anisotropic Stokes and Navier–Stokes systems with L∞ strongly elliptic coefficient tensor. Complex Variables and Elliptic Equations, 65(1), Article 1. https://doi.org/10.1080/17476933.2019.1631293
    49. Kollross, A. (2020). Octonions, triality, the exceptional Lie algebra F4 and polar actions on the Cayley hyperbolic plane. International Journal of Mathematics, 31(07), Article 07. https://doi.org/10.1142/s0129167x20500512
    50. Lienstromberg, C., & Müller, S. (2020). Local strong solutions to a quasilinear degenerate fourth-order thin-film equation. NoDEA Nonlinear Differential Equations Appl., 27(2), Article 2. https://doi.org/10.1007/s00030-020-0619-x
    51. Maier, D. (2020). BREATHER SOLUTIONS ON DISCRETE NECKLACE GRAPHS. OPERATORS AND MATRICES, 14(3), Article 3. https://doi.org/10.7153/oam-2020-14-48
    52. Maier, D. (2020). Construction of breather solutions for nonlinear Klein-Gordon equations    on periodic metric graphs. JOURNAL OF DIFFERENTIAL EQUATIONS, 268(6), Article 6. https://doi.org/10.1016/j.jde.2019.09.035
    53. Michalowsky, S., Scherer, C., & Ebenbauer, C. (2020). Robust and structure exploiting optimisation algorithms: An integral quadratic constraint approach. International Journal of Control, 2020, 1–24. https://doi.org/10.1080/00207179.2020.1745286
    54. Minorics, L. A. (2020). Spectral asymptotics for Krein-Feller operators with respect to V-variable Cantor measures. Forum Mathematicum, 32(1), Article 1. https://doi.org/10.1515/forum-2018-0188
    55. Nagy, P.-A., & Semmelmann, U. (2020). Conformal Killing forms in Kaehler geometry.
    56. 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
    57. Oesting, M., & Schnurr, A. (2020). Ordinal patterns in clusters of subsequent extremes of regularly varying time series. Extremes, 23(4), Article 4. https://doi.org/10.1007/s10687-020-00391-2
    58. 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
    59. Pelinovsky, D. E., & Schneider, G. (2020). The monoatomic FPU system as a limit of a diatomic FPU system. Appl. Math. Lett., 107, 7.
    60. 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
    61. Rigaud, G., & Hahn, B. N. (2020). Reconstruction algorithm for 3D Compton scattering imaging with incomplete data. Inverse Problems in Science and Engineering, 29(7), Article 7. https://doi.org/10.1080/17415977.2020.1815723
    62. 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
    63. Rösinger, C. A., & Scherer, C. W. (2020). Lifting to Passivity for $H_2$-Gain-Scheduling Synthesis with Full Block Scalings. IFAC-PapersOnline, 53(2), Article 2. https://doi.org/10.1016/j.ifacol.2020.12.570
    64. Rösinger, C. A., & Scherer, C. W. (2020). A Flexible Synthesis Framework of Structured Controllers for Networked Systems. IEEE Trans. Control Netw. Syst., 7(1), Article 1. https://doi.org/10.1109/TCNS.2019.2914411
    65. Schneider, G. (2020). The KdV approximation for a system with unstable resonances. Math. Methods Appl. Sci., 43(6), Article 6.
    66. Semmelmann, U., Wang, C., & Wang, M. Y.-K. (2020). On the linear stability of nearly Kähler 6-manifolds. Ann. Global Anal. Geom., 57(1), Article 1. https://doi.org/10.1007/s10455-019-09686-5
    67. Steinwart, I. (2020). Reproducing Kernel Hilbert Spaces Cannot Contain all Continuous Functions on a Compact Metric Space. Fakultät für Mathematik und Physik, Universität Stuttgart.
    68. 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
    69. Vonica, A., Bhat, N., Phan, K., Guo, J., Iancu, L., Weber, J. A., Karger, A., Cain, J. W., Wang, E. C. E., DeStefano, G. M., O’Donnell-Luria, A. H., Christiano, A. M., Riley, B., Butler, S. J., & Luria, V. (2020). Apcdd1 is a dual BMP/Wnt inhibitor in the developing nervous system and skin. Developmental Biology, 464(1), Article 1. https://doi.org/10.1016/j.ydbio.2020.03.015
  6. 2019

    1. Ammann, B., Kröncke, K., Weiss, H., & Witt, F. (2019). Holonomy rigidity for Ricci-flat metrics. Math. Z., 291(1–2), Article 1–2. https://doi.org/10.1007/s00209-018-2084-3
    2. Baggio, G., Zampieri, S., & Scherer, C. W. (2019). Gramian Optimization with Input-Power Constraints. 58th IEEE Conf. Decision and Control, 5686–5691. https://doi.org/10.1109/CDC40024.2019.9029169
    3. Bastian, P., Altenbernd, M., Dreier, N.-A., Engwer, C., Fahlke, J., Fritze, R., Geveler, M., Göddeke, D., Iliev, O., Ippisch, O., Mohring, J., Müthing, S., Ohlberger, M., Ribbrock, D., Shegunov, N., & Turek, S. (2019). Exa-Dune -- Flexible PDE Solvers, Numerical Methods and Applications.
    4. Bauer, R., Cummings, P., & Schneider, G. (2019). A model for the periodic water wave problem and its long wave amplitude equations. In Nonlinear water waves. An interdisciplinary interface. Based on the workshop held at the Erwin Schrödinger International Institute for Mathematics and Physics, Vienna, Austria, November 27 -- December 7, 2017 (pp. 123--138). Cham: Birkhäuser.
    5. Bauer, R., Düll, W.-P., & Schneider, G. (2019). The Korteweg-de Vries, Burgers and Whitham limits for a spatially periodic Boussinesq model. Proc. R. Soc. Edinb., Sect. A, Math., 149(1), Article 1.
    6. Bhatt, A., Fehr, J., Grunert, D., & Haasdonk, B. (2019). A Posteriori Error Estimation in Model Order Reduction of Elastic Multibody Systems with Large Rigid Motion. In J. Fehr & B. Haasdonk (Eds.), IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22–25, 2018. Springer. https://doi.org/DOI:10.1007/978-3-030-21013-7_7
    7. Bhatt, A., Fehr, J., & Haasdonk, B. (2019). Model order reduction of an elastic body under large rigid motion. Proceedings of ENUMATH 2017, Lect. Notes Comput. Sci. Eng.,(126), Article 126. https://doi.org/10.1007/978-3-319-96415-7\_23
    8. Bianchi, L. A., Blömker, D., & Schneider, G. (2019). Modulation equation and SPDEs on unbounded domains. Commun. Math. Phys., 371(1), Article 1.
    9. Brehler, M., Schirwon, M., Krummrich, P. M., & Göddeke, D. (2019). Simulation of Nonlinear Signal Propagation in Multimode Fibers on Multi-GPU Systems. Communications in Nonlinear Science and Numerical Simulation. https://doi.org/10.1016/j.cnsns.2019.105150
    10. Brünnette, T., Santin, G., & Haasdonk, B. (2019). Greedy Kernel Methods for Accelerating Implicit Integrators for Parametric ODEs. In F. A. Radu, K. Kumar, I. Berre, J. M. Nordbotten, & I. S. Pop (Eds.), Numerical Mathematics and Advanced Applications - ENUMATH 2017 (pp. 889--896). Springer International Publishing.
    11. Buchfink, P., Bhatt, A., & Haasdonk, B. (2019). Symplectic Model Order Reduction with Non-Orthonormal Bases. Mathematical and Computational Applications, 24(2), Article 2. https://doi.org/10.3390/mca24020043
    12. Carlberg, K., Brencher, L., Haasdonk, B., & Barth, A. (2019). Data-Driven Time Parallelism via Forecasting. SIAM Journal on Scientific Computing, 41(3), Article 3. https://doi.org/10.1137/18M1174362
    13. Chirilus-Bruckner, M., Maier, D., & Schneider, G. (2019). Diffusive stability for periodic metric graphs. Math. Nachr., 292(6), Article 6.
    14. Colombo, R. M., LeFloch, P. G., Rohde, C., & Trivisa, K. (2019). Nonlinear Hyperbolic Problems: Modeling, Analysis, and Numerics. Oberwohlfach Rep., 16, Article 16. https://www.ems-ph.org/journals/show_issue.php?issn=1660-8933&vol=16&iss=2
    15. Conlon, R., Degeratu, A., & Rochon, F. (2019). Quasi-asymptotically conical Calabi-Yau manifolds. Geom. Topol., 23(1), Article 1. https://doi.org/10.2140/gt.2019.23.29
    16. Defant, A., Mastyo, M., Sánchez-Pérez, E. A., & Steinwart, I. (2019). Translation invariant maps on function spaces over locally compact groups. J. Math. Anal. Appl., 470, 795--820. https://doi.org/10.1016/j.jmaa.2018.10.033
    17. Denzel, A., Haasdonk, B., & Kästner, J. (2019). Gaussian Process Regression for Minimum Energy Path Optimization and Transition State Search. J. Phys. Chem. A, 123(44), Article 44. https://doi.org/10.1021/acs.jpca.9b08239
    18. Engelke, S., de Fondeville, R., & Oesting, M. (2019). Extremal behaviour of aggregated data with an application to downscaling. Biometrika, 106(1), Article 1. https://doi.org/10.1093/biomet/asy052
    19. Farooq, M., & Steinwart, I. (2019). Learning Rates for Kernel-Based Expectile Regression. Mach. Learn., 108, 203--227. https://doi.org/10.1007/s10994-018-5762-9
    20. Föll, R., Haasdonk, B., Hanselmann, M., & Ulmer, H. (2019). Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation. https://openreview.net/forum?id=BkgosiRcKm
    21. Geck, M. (2019). Eigenvalues and Polynomial Equations. The American Mathematical Monthly, 126(10), Article 10. https://doi.org/10.1080/00029890.2019.1651168
    22. Griesemer, M., & Linden, U. (2019). Spectral theory of the Fermi polaron. Ann. Henri Poincaré, 20(6), Article 6. https://doi.org/10.1007/s00023-019-00796-1
    23. Gyorfi, L., Henze, N., & Walk, H. (2019). The Limit Distribution Of The Maximum Probability Nearest-Neighbour Ball. Journal of Applied Probability, 56(2), Article 2. https://doi.org/10.1017/jpr.2019.37
    24. Györfi, L., & Walk, H. (2019). Nearest neighbor based conformal prediction. Annales de l’ISUP, 63(2–3), Article 2–3. https://hal.science/hal-03603867
    25. Hahn, B. N., & Kienle Garrido, M.-L. (2019). An efficient reconstruction approach for a class of dynamic imaging operators. Inverse Problems, 35(9), Article 9. https://doi.org/10.1088/1361-6420/ab178b
    26. Hansmann, M., Kohler, M., & Walk, H. (2019). On the strong universal consistency of local averaging regression    estimates (vol 71, pg 1233, 2019). ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, 71(5), Article 5. https://doi.org/10.1007/s10463-018-0687-4
    27. Heil, K., & Jentsch, T. (2019). A special class of symmetric Killing 2-tensors. JOURNAL OF GEOMETRY AND PHYSICS, 138, 103–123. https://doi.org/10.1016/j.geomphys.2018.12.009
    28. Holicki, T., & Scherer, C. W. (2019). A Homotopy Approach for Robust Output-Feedback Synthesis. Proc. 27th. Med. Conf. Control Autom., 87–93. https://doi.org/10.1109/MED.2019.8798536
    29. Holicki, T., & Scherer, C. W. (2019). Stability analysis and output-feedback synthesis of hybrid systems affected by piecewise constant parameters via dynamic resetting scalings. Nonlinear Analysis: Hybrid Systems, 34, 179--208. https://doi.org/10.1016/j.nahs.2019.06.003
    30. Homma, Y., & Semmelmann, U. (2019). The Kernel of the Rarita-Schwinger Operator on Riemannian Spin Manifolds. Comm. Math. Phys., 370(3), Article 3. https://doi.org/10.1007/s00220-019-03324-8
    31. 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
    32. Kluth, T., Hahn, B. N., & Brandt, C. (2019). Spatio-temporal concentration reconstruction using motion priors in magnetic particle imaging. Proc. Int. Workshop Magnetic Particle Imaging.
    33. Kohr, M., Mikhailov, S. E., & Wendland, W. L. (2019). Newtonian and single layer potentials for the Stokes system with L∞ coefficients and the exterior Dirichlet problem. In Analysis as a life (pp. 237--260). Birkhäuser/Springer, Cham. https://doi.org/10.1007/978-3-030-02650-9\_12
    34. 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), Article 1. https://doi.org/10.1080/17476933.2019.1631293
    35. Kohr, M., & Wendland, W. L. (2019). Boundary value problems for the Brinkman system with L∞ coefficients in Lipschitz domains on compact Riemannian manifolds. A variational approach. Journal de Mathématiques Pures et Appliquées, 131, Article 131. https://doi.org/10.1016/j.matpur.2019.04.002
    36. Köppel, M., Franzelin, F., Kröker, I., Oladyshkin, S., Santin, G., Wittwar, D., Barth, A., Haasdonk, B., Nowak, W., Pflüger, D., & Rohde, C. (2019). Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario. Computational Geosciences, 23(2), Article 2. https://doi.org/10.1007/s10596-018-9785-x
    37. Mazzeo, R., Swoboda, J., Weiss, H., & Witt, F. (2019). Asymptotic geometry of the Hitchin metric. Commun. Math. Phys., 367(1), Article 1. https://doi.org/10.1007/s00220-019-03358-y
    38. 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.
    39. 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
    40. 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
    41. Rösinger, C. A., & Scherer, C. W. (2019). A Scalings Approach to $H_2$-Gain-Scheduling Synthesis without Elimination. IFAC-PapersOnLine, 52(28), Article 28. https://doi.org/10.1016/j.ifacol.2019.12.347
    42. Santin, G., & Haasdonk, B. (2019). Kernel Methods for Surrogate Modelling. University of Stuttgart.
    43. Santin, G., & Haasdonk, B. (2019). Kernel Methods for Surrogate Modeling (ArXiv 1907.10556; Issue 1907.10556). https://arxiv.org/abs/1907.10556
    44. Santin, G., Wittwar, D., & Haasdonk, B. (2019). Sparse approximation of regularized kernel interpolation by greedy algorithms.
    45. Schanz, M., Wasser, C., Allgaeuer, S., Schricker, S., Dippon, J., Alscher, MD., & Kimmel, M. (2019). Urinary TIMP-2·IGFBP7-guided randomized controlled intervention trial to prevent acute kidney injury in the emergency department. Transplant., 2019 Nov 1;34(11), 1902–1909. https://doi.org/10.1093/ndt/gfy186
    46. Schmidt, A., Wittwar, D., & Haasdonk, B. (2019). Rigorous and effective a-posteriori error bounds for nonlinear problems -- Application to RB methods. Advances in Computational Mathematics. https://doi.org/10.1007/s10444-019-09730-9
    47. 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
    48. 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
    49. Semmelmann, U., & Weingart, G. (2019). The standard Laplace operator. Manuscripta Math., 158(1–2), Article 1–2. https://doi.org/10.1007/s00229-018-1023-2
    50. 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
    51. Steinwart, I. (2019). Convergence Types and Rates  in Generic Karhunen-Loève Expansions with Applications to Sample Path Properties. Potential Anal., 51, 361--395. https://doi.org/10.1007/s11118-018-9715-5
    52. Steinwart, I. (2019). A Sober Look at Neural Network Initializations. Fakultät für Mathematik und Physik, Universität Stuttgart.
    53. Wenzel, T., Santin, G., & Haasdonk, B. (2019). A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution.
    54. Wittwar, D., & Haasdonk, B. (2019). Greedy Algorithms for Matrix-Valued Kernels. In F. A. Radu, K. Kumar, I. Berre, J. M. Nordbotten, & I. S. Pop (Eds.), Numerical Mathematics and Advanced Applications ENUMATH 2017 (pp. 113--121). Springer International Publishing.
    55. Wittwar, D., Santin, G., & Haasdonk, B. (2019). Part II on matrix valued kernels including analysis.
    56. Zhang, R., Kyriss, T., Dippon, J., Boedeker, E., & Friedel, G. (2019). Preoperative serum lactate dehydrogenase level as a predictor of major omplications following thoracoscopic lobectomy: a propensity-adjusted analysis. European Journal of Cardio-Thoracic Surgery, 56(2), Article 2. https://doi.org/10.1093/ejcts/ezz027
    57. Zhang, R., Dippon, J., & Friedel, G. (2019). Refined risk stratification for thoracoscopic lobectomy or segmentectomy. Journal of Thoracic Disease, 11(1), Article 1. https://doi.org/10.21037/jtd.2018.12.44
    58. 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
  7. 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). Approximation and simulation of infinite-dimensional Levy processes. STOCHASTICS AND PARTIAL DIFFERENTIAL EQUATIONS-ANALYSIS AND COMPUTATIONS, 6(2), Article 2. https://doi.org/10.1007/s40072-017-0109-2
    4. Barth, A., & Stein, A. (2018). A Study of Elliptic Partial Differential Equations with Jump Diffusion    Coefficients. SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 6(4), Article 4. https://doi.org/10.1137/17M1148888
    5. Barth, A., & 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
    6. Bhatt, A., Fehr, J., & Hassdonk, B. (2018). Model Order Reduction of an Elastic Body under Large Rigid Motion. Proceedings of ENUMATH 2017, Voss, Norway.
    7. Bhatt, A., & Haasdonk, B. (2018). Certified and structure-preserving model order reduction of EMBS. In RAMSA 2017, New Delhi.
    8. Bhatt, A., Haasdonk, B., & Moore, B. E. (2018). Structure-preserving Integration and Model Order Reduction.
    9. Blaschzyk, I., & Steinwart, I. (2018). Improved Classification Rates under Refined Margin Conditions. Electron. J. Stat., 12, 793--823. https://doi.org/10.1214/18-EJS1406
    10. Brehler, M., Schirwon, M., Göddeke, D., & Krummrich, P. (2018, July). Modeling the Kerr-Nonlinearity in Mode-Division Multiplexing Fiber  Transmission Systems on GPUs. Proceedings of Advanced Photonics 2018.
    11. Brünnette, T., Santin, G., & Haasdonk, B. (2018). Greedy kernel methods for accelerating implicit integrators for parametric ODEs. Proc. ENUMATH 2017.
    12. Buchfink, P. (2018). Structure-preserving Model Reduction for Elasticity [Diploma thesis].
    13. De Marchi, S., Iske, A., & Santin, G. (2018). Image reconstruction from scattered Radon data by weighted positive  definite kernel functions. Calcolo, 55(1), Article 1. https://doi.org/10.1007/s10092-018-0247-6
    14. de Rijk, B. (2018). Spectra and stability of spatially periodic pulse patterns II: the critical spectral curve. SIAM J. Math. Anal., 50(2), Article 2. https://doi.org/10.1137/17M1127594
    15. de Rijk, B., & Sandstede, B. (2018). Diffusive stability against nonlocalized perturbations of planar wave trains in reaction-diffusion systems. J. Differential Equations, 265(10), Article 10. https://doi.org/10.1016/j.jde.2018.07.011
    16. Degeratu, A., & Mazzeo, R. (2018). Fredholm theory for elliptic operators on quasi-asymptotically conical spaces. Proc. Lond. Math. Soc. (3), 116(5), Article 5. https://doi.org/10.1112/plms.12105
    17. Devroye, L., Gyorfi, L., Lugosi, G., & Walk, H. (2018). A nearest neighbor estimate of the residual variance. ELECTRONIC JOURNAL OF STATISTICS, 12(1), Article 1. https://doi.org/10.1214/18-EJS1438
    18. Dibak, C., Haasdonk, B., Schmidt, A., Dürr, F., & Rothermel, K. (2018). Enabling interactive mobile simulations through distributed reduced models. Pervasive and Mobile Computing, Elsevier BV, 45, 19--34. https://doi.org/10.1016/j.pmcj.2018.02.002
    19. Doelman, A., Rademacher, J., de Rijk, B., & Veerman, F. (2018). Destabilization Mechanisms of Periodic Pulse Patterns Near a Homoclinic Limit. SIAM J. Appl. Dyn. Syst., 17(2), Article 2. https://doi.org/10.1137/17M1122840
    20. Doering, M., Gyorfi, L., & Walk, H. (2018). Rate of Convergence of k-Nearest-Neighbor Classification Rule. JOURNAL OF MACHINE LEARNING RESEARCH, 18.
    21. Dreier, N.-A., Altenbernd, M., Engwer, C., & Göddeke, D. (2018, March). A high-level C++ approach to manage local errors, asynchrony and  faults in an MPI application. Proceedings of 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2018).
    22. Düll, W.-P. (2018). On the mathematical description of time-dependent surface water waves. Jahresber. Dtsch. Math.-Ver., 120(2), Article 2. https://doi.org/10.1365/s13291-017-0173-6
    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), Article 4. 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), Article 1.
    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), Article 1. https://doi.org/10.1515/jaa-2018-0007
    26. 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).
    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. Escher, J., & Lienstromberg, C. (2018). Travelling waves in dilatant non-Newtonian thin films. J. Differential Equations, 264(3), Article 3. https://doi.org/10.1016/j.jde.2017.10.015
    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), Article 3. 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). A first guide to the character theory of finite groups of Lie type. Local Representation Theory and Simple Groups (Eds. R. Kessar, G. Malle, D. Testerman), 63--106. https://doi.org/10.4171/185-1/3
    34. 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
    35. Georgiev, V., & Wirth, J. (2018). Zero resonances for localised potentials. Journal of Mathematical Physics, 59(7), Article 7. https://doi.org/10.1063/1.5027717
    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., Stark, D., & Stephan, E. P. (2018). Boundary elements with mesh refinements for the wave equation. Numer. Math., (accepted). https://arxiv.org/abs/1801.09736
    38. 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
    39. Griesemer, M., & Wünsch, A. (2018). On the domain of the Nelson Hamiltonian. J. Math. Phys., 59(4), Article 4. https://doi.org/10.1063/1.5018579
    40. Griesemer, M., & Linden, U. (2018). Stability of the two-dimensional Fermi polaron. Lett. Math. Phys., 108(8), Article 8. https://doi.org/10.1007/s11005-018-1055-2
    41. Guo, Y., & Scherer, C. W. (2018). Robust Gain-Scheduled Controller Design with a Hierarchical Structure. IFAC-PapersOnline, 51(25), Article 25. https://doi.org/10.1016/j.ifacol.2018.11.110
    42. Haasdonk, B., Hamzi, B., Santin, G., & Wittwar, D. (2018). Greedy Kernel Methods for Center Manifold Approximation (ArXiv 1810.11329; Issue 1810.11329).
    43. 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
    44. Haesaert, S., Weiland, S., & Scherer, C. W. (2018). A separation theorem for guaranteed $H_2$ performance through matrix inequalities. Automatica, 96, 306–313. https://doi.org/10.1016/j.automatica.2018.07.002
    45. Hang, H., Steinwart, I., Feng, Y., & Suykens, J. A. K. (2018). Kernel Density Estimation for Dynamical Systems. J. Mach. Learn. Res., 19, 1--49.
    46. Harbrecht, H., Wendland, W. L., & Zorii, N. (2018). Minimal energy problems for strongly singular Riesz kernels. Mathematische Nachrichten, 291, Article 291. https://doi.org/10.1002/mana.201600024
    47. Holicki, T., & Scherer, C. W. (2018). Output-Feedback Gain-Scheduling Synthesis for a Class of Switched Systems via Dynamic Resetting $D$-Scalings. 57th IEEE Conf. Decision and Control, 6440–6445. https://doi.org/10.1109/CDC.2018.8619128
    48. 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
    49. Kohler, M., Krzyzak, A., Tent, R., & Walk, H. (2018). Nonparametric quantile estimation using importance sampling. ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, 70(2), Article 2. https://doi.org/10.1007/s10463-016-0595-4
    50. Kohr, M., & Wendland, W. L. (2018). Layer Potentials and Poisson Problems for the Nonsmooth Coefficient Brinkman System in Sobolev and Besov Spaces. Journal of Mathematical Fluid Mechanics, 4(20), Article 20. https://doi.org/10.1007/s00021-018-0394-1
    51. Kohr, M., & Wendland, W. L. (2018). Variational approach for the Stokes and Navier–Stokes systems with nonsmooth coefficients in Lipschitz domains on compact Riemannian manifolds. Calculus of Variations and Partial Differential Equations, 57:165. https://doi.org/10.1007/s00526-018-1426-7
    52. Kovar\’ık, H., Ruszkowski, B., & Weidl, T. (2018). Melas-type bounds for the Heisenberg Laplacian on bounded domains. Journal of Spectral Theory, 8(2), Article 2. https://doi.org/10.4171/jst/200
    53. Kraemer, B., Scharpf, M., Keckstein, S., Dippon, J., Tsaousidis, C., Brunecker, K., Enderle, MD., Neugebauer, A., Nuessle, D., Fend, F., Brucker, S., Taran, FA., Kommoss, S., & Rothmund, R. (2018). A prospective randomized experimental study to investigate the peritoneal adhesion formation after waterjet injection and argon plasma coagulation (HybridAPC) in a rat model. Arch Gynecol Obstet., 2018, Apr;297(4), 961–967. https://doi.org/10.1007/s00404-018-4661-4
    54. 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
    55. 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), Article 8. https://doi.org/10.1002/cnm.3095
    56. 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
    57. 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
    58. Langer, A. (2018). Locally adaptive total variation for removing mixed Gaussian-impulse  noise. International Journal of Computer Mathematics, 19. https://www.tandfonline.com/doi/abs/10.1080/00207160.2018.1438603
    59. Langer, A. (2018). Overlapping domain decomposition methods for total variation denoising. http://people.ricam.oeaw.ac.at/a.langer/publications/DDfTV.pdf
    60. 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
    61. 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
    62. 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
    63. 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).
    64. Oesting, M. (2018). Equivalent representations of max-stable processes via $\ell^p$-norms. J. Appl. Probab., 55(1), Article 1. https://doi.org/10.1017/jpr.2018.5
    65. Oesting, M., Bel, L., & Lantuéjoul, C. (2018). Sampling from a max-stable process conditional on a homogeneous functional with an application for downscaling climate data. Scand. J. Stat., 45(2), Article 2. https://doi.org/10.1111/sjos.12299
    66. Oesting, M., Schlather, M., & Zhou, C. (2018). Exact and fast simulation of max-stable processes on a compact set using the normalized spectral representation. Bernoulli, 24(2), Article 2. https://doi.org/10.3150/16-BEJ905
    67. Oesting, M., & Stein, A. (2018). Spatial modeling of drought events using max-stable processes. Stoch. Env. Res. Risk A., 32(1), Article 1. https://doi.org/10.1007/s00477-017-1406-z
    68. Oesting, M., & Strokorb, K. (2018). Efficient simulation of Brown-Resnick processes based on variance reduction of Gaussian processes. Adv. in Appl. Probab., 50(4), Article 4. https://doi.org/10.1017/apr.2018.54
    69. 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
    70. Rigaud, G., & Hahn, B. N. (2018). 3D Compton scattering imaging and contour reconstruction for a class of Radon transforms. Inverse Problems, 34(7), Article 7. https://doi.org/10.1088/1361-6420/aabf0b
    71. 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
    72. 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
    73. Ruiz, P. A., Freiberg, U. R., & Kigami, J. (2018). Completely symmetric resistance forms on the stretched Sierpinski gasket. JOURNAL OF FRACTAL GEOMETRY, 5(3), Article 3. https://doi.org/10.4171/JFG/61
    74. Santin, G., Wittwar, D., & Haasdonk, B. (2018). Greedy regularized kernel interpolation (ArXiv Preprint 1807.09575; Issue 1807.09575). University of Stuttgart.
    75. Scherer, C. W., & Holicki, T. (2018). An IQC theorem for relations: Towards stability analysis of data-integrated systems. IFAC-PapersOnline, 51(25), Article 25. https://doi.org/10.1016/j.ifacol.2018.11.138
    76. Scherer, C. W., & Veenman, J. (2018). Stability analysis by dynamic dissipation inequalities: On merging frequency-domain techniques with time-domain conditions. Syst. Control Lett., 121, 7–15. https://doi.org/10.1016/j.sysconle.2018.08.005
    77. 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
    78. 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.
    79. Schmidt, A., & Haasdonk, B. (2018). Reduced basis approximation of large scale parametric algebraic Riccati equations. ESAIM: Control, Optimisation and Calculus of Variations, 24(1), Article 1. https://doi.org/10.1051/cocv/2017011
    80. 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
    81. 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
    82. Wittwar, D., Santin, G., & Haasdonk, B. (2018). Interpolation with uncoupled separable matrix-valued kernels. ArXiv E-Prints.
    83. 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
    84. Zhang, R., Kyriss, T., Dippon, J., Ciupa, S., Boedeker, E., & Friedel, G. (2018). Impact of comorbidity burden on morbidity following horacoscopic lobectomy: a propensity-matched analysis. J Thorac Dis., 2018 Mar;10(3), 1806–1814. https://doi.org/10.21037/jtd.2018.02.62
    85. 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), Article 5. https://doi.org/10.1093/ejcts/ezx436
  8. 2017

    1. Afkham, B., & Hesthaven, J. (2017). Structure Preserving Model Reduction of Parametric Hamiltonian Systems. SIAM Journal on Scientific Computing, 39(6), Article 6. 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), Article 1. 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., 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.
    7. 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
    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. APPLIED NUMERICAL MATHEMATICS, 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., Harrach, B., Hyvönen, N., & Mustonen, L. (2017). Detecting stochastic inclusions in electrical impedance tomography. Inv. Prob., 33(11), Article 11. http://arxiv.org/abs/1706.03962
    13. Barth, A., & Stein, A. (2017). A study of elliptic partial differential equations with jump diffusion  coefficients.
    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), Article 17. 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), Article SI. https://doi.org/10.1016/j.apnum.2016.07.005
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    23. Chalons, C., Rohde, C., & Wiebe, M. (2017). A FINITE VOLUME METHOD FOR UNDERCOMPRESSIVE SHOCK WAVES IN TWO SPACE    DIMENSIONS. ESAIM-MATHEMATICAL MODELLING AND NUMERICAL ANALYSIS-MODELISATION  MATHEMATIQUE ET ANALYSE NUMERIQUE, 51(5), Article 5. 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
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    26. De Marchi, S., Iske, A., & Santin, G. (2017). Image Reconstruction from Scattered Radon Data by Weighted Positive  Definite Kernel Functions.
    27. Diaz Ramos, J. C., Dominguez Vazquez, M., & Kollross, A. (2017). Polar actions on complex hyperbolic spaces. Mathematische Zeitschrift, 287(3), Article 3. 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. 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom), 111--120. https://doi.org/10.1109/PERCOM.2017.7917857
    29. Dombry, C., Engelke, S., & Oesting, M. (2017). Bayesian inference for multivariate extreme value distributions. Electron. J. Stat., 11(2), Article 2. 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), Article 3. https://doi.org/10.1007/s00220-017-2966-y
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    32. Escher, J., & Lienstromberg, C. (2017). A survey on second-order free boundary value problems modelling MEMS with general permittivity profile. Discrete Contin. Dyn. Syst. Ser. S, 10(4), Article 4. https://doi.org/10.3934/dcdss.2017038
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    34. 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
    35. 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.
    36. 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/
    37. 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
    38. Fetzer, M., & Scherer, C. W. (2017). Full-block multipliers for repeated, slope restricted scalar nonlinearities. Int. J. Robust Nonlin., 27(17), Article 17. https://doi.org/10.1002/rnc.3751
    39. Fetzer, M., & Scherer, C. W. (2017). Absolute stability analysis of discrete time feedback interconnections. IFAC-PapersOnline, 50(1), Article 1. https://doi.org/10.1016/j.ifacol.2017.08.757
    40. Fetzer, M., & Scherer, C. W. (2017). Zames-Falb Multipliers for Invariance. IEEE Control Syst. Lett., 1(2), Article 2. https://doi.org/10.1109/LCSYS.2017.2718556
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    48. Geck, M. (2017). On the construction of semisimple Lie algebras and Chevalley groups. Proceedings of the American Mathematical Society, 145(8), Article 8. https://doi.org/10.1090/proc/13600
    49. 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
    50. Geck, M. (2017). James’ Submodule Theorem and the Steinberg Module. Symmetry, Integrability and Geometry: Methods and Applications, 13. https://doi.org/10.3842/sigma.2017.091
    51. 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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    59. 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), Article 10. https://doi.org/10.1007/s11005-017-0969-4
    60. 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
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    65. Hang, H., & Steinwart, I. (2017). A Bernstein-type Inequality for Some Mixing Processes and Dynamical Systems with an Application to Learning. Ann. Statist., 45, 708--743. https://doi.org/10.1214/16-AOS1465
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    83. K�ppel, M., Kr�ker, I., & Rohde, C. (2017). Intrusive Uncertainty Quantification for Hyperbolic-Elliptic Systems  Governing Two-Phase Flow in Heterogeneous Porous Media. Comput. Geosci., 21, 807–832. https://doi.org/10.1007/s10596-017-9662-z
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    99. Santin, G., & Haasdonk, B. (2017). Non-symmetric kernel greedy interpolation.
    100. Santin, G., & Haasdonk, B. (2017). Greedy Kernel Approximation for Sparse Surrogate Modelling. University of Stuttgart.
    101. Schanz, M., Ketteler, M., Heck, M., Dippon, J., Alscher, MD., & Kimmel, M. (2017). Impact of an in-Hospital Patient Education Program on Choice of Renal Replacement Modality in Unplanned Dialysis Initiation. Kidney & Blood Pressure Research, 42(5), Article 5. https://doi.org/10.1159/000484531
    102. Schanz, M., Schaaf, L., Dippon, J., Biegger, D., Fritz, P., Alscher, MD., & Kimmel, M. (2017). Renal effects of metallothionein induction by zinc in vitro and in vivo. BMC Nephrol, 2017 Mar 16;18(1), 91. https://doi.org/10.1186/s12882-017-0503-z
    103. Schmid, J., & Griesemer, M. (2017). Well-posedness of non-autonomous linear evolution equations in              uniformly convex spaces. Math. Nachr., 290(2–3), Article 2–3. https://doi.org/10.1002/mana.201500052
    104. Schmidt, A., & Haasdonk, B. (2017). Data-driven surrogates of value functions and applications to feedback  control for dynamical systems. University of Stuttgart. http://www.simtech.uni-stuttgart.de/publikationen/prints.php?ID=1742
    105. Schmidt, A., & Haasdonk, B. (2017). Reduced basis approximation of large scale parametric algebraic Riccati  equations. ESAIM: Control, Optimisation and Calculus of Variations. https://doi.org/10.1051/cocv/2017011
    106. Schneider, G., & Uecker, H. (2017). Nonlinear PDEs. A dynamical systems approach. In Grad. Stud. Math. (Vol. 182, p. xiii + 575). Providence, RI: American Mathematical Society (AMS).
    107. Seus, D., Radu, F. A., & Rohde, C. (2017). A linear domain decomposition method for two-phase flow in porous  media. https://doi.org/arXiv:1712.04869
    108. Steinwart, I. (2017). Representation of Quasi-Monotone Functionals by Families of Separating Hyperplanes. Math. Nachr., 290, 1859--1883. https://doi.org/10.1002/mana.201500350
    109. Steinwart, I. (2017). A Short Note on the Comparison of Interpolation Widths, Entropy Numbers, and Kolmogorov Widths. J. Approx. Theory, 215, 13--27. https://doi.org/10.1016/j.jat.2016.11.006
    110. Steinwart, I., Sriperumbudur, B. K., & Thomann, P. (2017). Adaptive Clustering Using Kernel Density Estimators. Fakultät für Mathematik und Physik, Universität Stuttgart.
    111. Steinwart, I., & Thomann, P. (2017). liquidSVM: A Fast and Versatile SVM Package. Fakultät für Mathematik und Physik, Universität Stuttgart.
    112. Tempel, P., Schmidt, A., Haasdonk, B., & Pott, A. (2017). Application of the Rigid Finite Element Method to the Simulation of Cable-Driven Parallel Robots. University of Stuttgart.
    113. Thomann, P., Steinwart, I., Blaschzyk, I., & Meister, M. (2017). Spatial Decompositions for Large Scale SVMs. In A. Singh & J. Zhu (Eds.), Proceedings of Machine Learning Research Volume 54: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics 2017 (pp. 1329--1337).
    114. Wirth, J. (2017). On t-dependent hyperbolic systems. Part 2. J. Math. Anal. Appl., 448(1), Article 1. https://doi.org/10.1016/j.jmaa.2016.11.026
    115. Wirth, J. (2017). Regular singular problems for hyperbolic systems and their              asymptotic integration. In New trends in analysis and interdisciplinary applications (pp. 553--561). Birkhäuser/Springer, Cham. https://doi.org/10.1007/978-3-319-48812-7_70
    116. Wittwar, D., & Haasdonk, B. (2017). On uncoupled separable matrix-valued kernels. University of Stuttgart.
    117. Wittwar, D., Santin, G., & Haasdonk, B. (2017). Interpolation with uncoupled separable matrix-valued kernels. [ArXiv preprint 1807.09111, Accepted for publications in Dolomites Res. Notes Approx.].
    118. Wittwar, D., Schmidt, A., & Haasdonk, B. (2017). Reduced Basis Approximation for the Discrete-time Parametric Algebraic  Riccati Equation. University of Stuttgart.
  9. 2016

    1. Alk�mper, M., Dedner, A., Kl�fkorn, R., & Nolte, M. (2016). The DUNE-ALUGrid Module. Archive of Numerical Software, 4(1), Article 1. https://doi.org/10.11588/ans.2016.1.23252
    2. Alla, A., Schmidt, A., & Haasdonk, B. (2016). Model order reduction approaches for infinite horizon optimal control problems via the HJB equation. University of Stuttgart. https://arxiv.org/abs/1607.02337
    3. Allerhand, L., Gershon., E., & Shaked, U. (2016). Robust state-feedback control of stochastic state-multiplicative discrete-time linear switched systems with dwell time. Int. J. Robust Nonlin., 26(2), Article 2. https://doi.org/10.1002/rnc.3301
    4. Altenbernd, M., & Göddeke, D. (2016). Soft fault detection and correction for multigrid. The International Journal of High Performance Computing Applications. https://doi.org/10.1177/1094342016684006
    5. Ammann, B., Weiss, H., & Witt, F. (2016). A spinorial energy functional: critical points and gradient flow. Math. Ann., 365(3–4), Article 3–4. https://doi.org/10.1007/s00208-015-1315-8
    6. Ammann, B., Weiss, H., & Witt, F. (2016). The spinorial energy functional on surfaces. Math. Z., 282(1–2), Article 1–2. https://doi.org/10.1007/s00209-015-1537-1
    7. Amsallem, D., & Haasdonk, B. (2016). PEBL-ROM: Projection-Error Based Local Reduced-Order Models. AMSES, Advanced Modeling and Simulation in Engineering Sciences, 3(6), Article 6. https://doi.org/10.1186/s40323-016-0059-7
    8. Antoulas, A. C., Haasdonk, B., & Peherstorfer, B. (2016). MORML 2016 Book of Abstracts. University of Stuttgart.
    9. Apprich, C., Höllig, K., Hörner, J., & Reif, U. (2016). Collocation with WEB--Splines. Advances in Computational Mathematics, 42(4), Article 4. https://doi.org/10.1007/s10444-015-9444-x