Publikationen des Fachbereichs Mathematik

Fachbereich Mathematik

Publikationen der Mitglieder des Fachbereichs Mathematik ab 2017

 

Einen ersten Eindruck über die vielfältigen Publikationen der Forschenden des Fachbereichs, nicht nur in begutachteten Fachzeitschriften, gibt die folgende Übersicht exemplarisch für den Zeitraum ab 2017. Einen detaillerteren, evtl. vollständigeren und themenspezifischeren Eindruck vermitteln die Seiten der einzelnen Institute, Arbeitsgruppen und koordinierten Forschungsprogramme

  1. 2024

    1. "Knobloch, P., "Kuzmin, D., & "Jha, A. (2024). Well-balanced convex limiting for finite element discretizations of steady convection-diffusion-reaction equations (P. "Knobloch, D. "Kuzmin, & A. "Jha, Hrsg.).
    2. 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
    3. 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
    4. Claeys, X., Hassan, M., & Stamm, B. (2024). Continuity estimates for Riesz potentials on polygonal boundaries. Partial Differential Equations and Applications. https://doi.org/10.1007/s42985-024-00280-4
    5. Corso, T. C., Hassan, M., Jha, A., & Stamm, B. (2024). An $L^2$-maximum principle for circular arcs on the disk.
    6. Homs-Pons, C., Lautenschlager, R., Schmid, L., Ernst, J., Göddeke, D., Röhrle, O., & Schulte, M. (2024). Coupled Simulation and Parameter Inversion for Neural System  and Electrophysiological Muscle Models. GAMM-Mitteilungen. https://doi.org/10.1002/gamm.202370009
    7. Huber, F., Bürkner, P.-C., Göddeke, D., & Schulte, M. (2024). Knowledge-based modeling of simulation behavior for Bayesian  optimization. Computational Mechanics. https://doi.org/10.1007/s00466-023-02427-3
    8. Hörl, M., & Rohde, C. (2024). Rigorous Derivation of Discrete Fracture Models for Darcy Flow in the Limit of Vanishing Aperture. Netw. Heterog. Media, 19(1), Article 1. https://doi.org/10.3934/nhm.2024006
    9. Jha, A. (2024). Residual-Based a Posteriori Error Estimators for Algebraic Stabilizations. Applied Mathematics Letters, 157, 109192. https://doi.org/10.1016/j.aml.2024.109192
    10. 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
    11. Lindgren, E. B., Avis, H., Miller, A., Stamm, B., Besley, E., & Stace, A. J. (2024). The significance of multipole interactions for the stability of regular structures composed from charged particles. Journal of Colloid and Interface Science, 663, 458–466. https://doi.org/10.1016/j.jcis.2024.02.146
    12. Magiera, J., & Rohde, C. (2024). A Multiscale Method for Two-Component, Two-Phase Flow with a Neural Network Surrogate. Communications on Applied Mathematics and Computation. https://doi.org/10.1007/s42967-023-00349-8
    13. Maier, B., Göddeke, D., Huber, F., Klotz, T., Röhrle, O., & Schulte, M. (2024). OpenDiHu: An Efficient and Scalable Framework for Biophysical  Simulations of the Neuromuscular System. Journal of Computational Science, 79(102291), Article 102291. https://doi.org/10.1016/j.jocs.2024.102291
    14. Meijer, T. J., Holicki, T., Eijnden, S. J. A. M. van den, Scherer, C. W., & Heemels, W. P. M. H. (2024). The Non-Strict Projection Lemma. IEEE Transactions on Automatic Control, 1–8. https://doi.org/10.1109/TAC.2024.3371374
    15. Mel’nyk, T., & Rohde, C. (2024). Reduced-dimensional modelling for nonlinear convection-dominated flow in cylindric domains. https://arxiv.org/html/2404.07538v1
    16. Mel’nyk, T., & Rohde, C. (2024). Asymptotic approximations for semilinear parabolic convection-dominated transport problems in thin graph-like networks. J. Math. Anal. Appl., 529(1), Article 1. https://doi.org/10.1016/j.jmaa.2023.127587
    17. Mel’nyk, T., & Rohde, C. (2024). Asymptotic expansion for convection-dominated transport in a thin graph-like junction. Analysis and Applications. https://doi.org/10.1142/S0219530524500040
    18. Mel’nyk, T., & Rohde, C. (2024). Puiseux asymptotic expansions for convection-dominated transport problems in thin graph-like networks: strong boundary interactions. Asymptotic Analysis, 137, 27–52. https://doi.org/10.3233/ASY-231876
    19. Miao, Y., Rohde, C., & Tang, H. (2024). Well-posedness for a stochastic Camassa-Holm type equation with higher order nonlinearities. Stoch. Partial Differ. Equ. Anal. Comput., 12(1), Article 1. https://doi.org/10.1007/s40072-023-00291-z
    20. Ruan, L., & Rybak, I. (2024). Stokes-Brinkman-Darcy models for coupled fluid-porous systems: derivation, analysis and validation. Appl. Math. Comp.  (submitted).
    21. 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. Beschle, C. A., & Barth, A. (2023). Quasi continuous level Monte Carlo for random elliptic PDEs.
    5. 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
    6. Brennenstuhl, M., Otto, R., Schembera, B., & Eicker, U. (2023). Optimized Dimensioning and Economic Assessment of Decentralized Hybrid Small Wind and PV Power Systems for Residential Buildings. https://www.researchsquare.com/article/rs-3677621/latest.pdf
    7. 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
    8. Burbulla, S., Hörl, M., & Rohde, C. (2023). Flow in Porous Media with Fractures of Varying Aperture. SIAM J. Sci. Comput, 45(4), Article 4. https://doi.org/10.1137/22M1510406
    9. 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. https://doi.org/10.1007/s11005-023-01645-3
    10. 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
    11. Cerejeiras, P., Ferreira, M., Kähler, U., & Wirth, J. (2023). Global Operator Calculus on Spin Groups. Journal of Fourier Analysis and Applications, 29(3), Article 3. https://doi.org/10.1007/s00041-023-10015-5
    12. 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
    13. 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
    14. 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 (Hrsg.), Finite Volumes for Complex Applications X - Volume 1, Elliptic and Parabolic Problems (S. 275–283). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-40864-9_22
    15. Eggenweiler, E., & Rybak, I. (2023). Higher-order coupling conditions for arbitrary flows in Stokes-Darcy systems. J. Fluid Mech. (submitted).
    16. 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
    17. 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 (Hrsg.), Domain Decomposition Methods in Science and Engineering XXVI (S. 443--450). Springer International Publishing.
    18. 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
    19. 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
    20. Gramlich, D., Holicki, T., Scherer, C. W., & Ebenbauer, C. (2023). A Structure Exploiting SDP Solver for Robust Controller Synthesis. IEEE Control Syst. Lett., 7, 1831–1836. https://doi.org/10.1109/LCSYS.2023.3277314
    21. 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
    22. 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
    23. 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
    24. 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
    25. 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
    26. 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
    27. 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
    28. Hewing, L., Gramlich, D., Verhoek, C., Polonio, R., Veenman, J., Ardura, C., Tóth, R., Ebenbauer, C., Scherer, C., & Preda, V. (2023, Juli). Enhancing the Guidance, Navigation and Control of Autonomous Parafoils using Machine Learning Methods. Papers of ESA GNC-ICATT 2023. https://doi.org/10.5270/esa-gnc-icatt-2023-135
    29. 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
    30. 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
    31. 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
    32. Holicki, T., & Scherer, C. W. (2023). IQC based analysis and estimator design for discrete-time systems affected by impulsive uncertainties. Nonlinear Anal. Hybri., 50, 101399. https://doi.org/10.1016/j.nahs.2023.101399
    33. Holicki, T., & Scherer, C. W. (2023). Input-Output-Data-Enhanced Robust Analysis via Lifting. IFAC-PapersOnLine, 56(2), Article 2. https://doi.org/10.1016/j.ifacol.2023.10.047
    34. 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
    35. Hornischer, N. (2023). Model Order Reduction with Dynamically Transformed Modes for Electrophysiological Simulations. GAMM Archive for Students.
    36. Horsch, M., Schembera, B., & DFG, M. (2023). Epistemic metadata in molecular modelling: First-stage case-study report (10 cases). In Inprodat eV, Kaiserslautern, Tech. Rep (Inprodat eV, Kaiserslautern, Tech. Rep). https://www.researchgate.net/profile/Martin-Horsch/publication/366974408_Epistemic_metadata_in_molecular_modelling_First-stage_case-study_report_10_cases/links/63bc41e4a03100368a6645a6/Epistemic-metadata-in-molecular-modelling-First-stage-case-study-report-10-cases.pdf
    37. Jha, A., John, V., & Knobloch, P. (2023). Adaptive Grids in the Context of Algebraic Stabilizations for Convection-Diffusion-Reaction Equations. SIAM Journal on Scientific Computing, 45(4), Article 4. https://doi.org/10.1137/21m1466360
    38. 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
    39. 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
    40. Keim, J., Schwarz, A., Chiocchetti, S., Rohde, C., & Beck, A. (2023). A Reinforcement Learning Based Slope Limiter for Two-Dimensional Finite Volume Schemes. https://doi.org/10.13140/RG.2.2.18046.87363
    41. 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
    42. Kharitenko, A., & Scherer, C. (2023). Time-varying Zames–Falb multipliers for LTI Systems are superfluous. Automatica, 147, 110577. https://doi.org/10.1016/j.automatica.2022.110577
    43. Kröker, I., Oladyshkin, S., & Rybak, I. (2023). Global sensitivity analysis using multi-resolution polynomial chaos expansion for coupled Stokes-Darcy flow problems. Comput. Geosci. https://doi.org/10.1007/s10596-023-10236-z
    44. Lienstromberg, C., Schiffer, S., & Schubert, R. (2023). A data-driven approach to viscous fluid mechanics: the              stationary case. Arch. Ration. Mech. Anal., 247(2), Article 2. https://doi.org/10.1007/s00205-023-01849-w
    45. Lienstromberg, C., Schiffer, S., & Schubert, R. (2023). A variational approach to the non-newtonian Navier-Stokes equations. https://doi.org/doi:10.48550/ARXIV.2312.03546
    46. 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. https://doi.org/10.48550/ARXIV.2203.00075
    47. 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
    48. 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
    49. Mel’nyk, T. (2023). Complex Analysis (No. 1; Nummer 1). Springer Cham. https://doi.org/10.1007/978-3-031-39615-1
    50. 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
    51. Merkle, R., & Barth, A. (2023). On Properties and Applications of Gaussian Subordinated Lévy Fields. Methodology and Computing in Applied Probability, 25, 62. https://doi.org/10.1007/s11009-023-10033-2
    52. Miller, C. T., Gray, W. G., Kees, C. E., Rybak, I., & Shepherd, B. J. (2023). Correction to: Modelling Sediment Transport in Three-Phase Surface Water Systems. J. Hydraul. Res., 61, 168–171. https://doi.org/10.1080/00221686.2022.2107580
    53. Mohammadi, F., Eggenweiler, E., Flemisch, B., Oladyshkin, S., Rybak, I., Schneider, M., & Weishaupt, K. (2023). A Surrogate-Assisted Uncertainty-Aware Bayesian Validation Framework and its Application to Coupling Free Flow and Porous-Medium Flow. Comput. Geosci. https://doi.org/10.1007/s10596-023-10228-z
    54. Morato, M. M., Holicki, T., & Scherer, C. W. (2023). Stabilizing Model Predictive Control Synthesis using Integral Quadratic Constraints and Full-Block Multipliers. Int. J. Robust Nonlin. https://doi.org/10.1002/rnc.6952
    55. Nagy, P.-A., & Semmelmann, U. (2023). Eigenvalue estimates for 3-Sasaki structures.
    56. 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
    57. 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
    58. 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
    59. Ruan, L., & Rybak, I. (2023). Stokes-Brinkman-Darcy models for coupled free-flow and porous-medium systems. In E. Franck, J. Fuhrmann, V. Michel-Dansac, & L. Navoret (Hrsg.), Finite Volumes for Complex Applications X - Volume 1, Elliptic and Parabolic Problems (S. 365–373). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-40864-9_31
    60. Rösinger, C. A., & Scherer, C. W. (2023). Gain-Scheduling Controller Synthesis for Networked Systems with Full Block Scalings. https://doi.org/10.1109/TAC.2023.3329851
    61. Rösinger, C. A., & Scherer, C. W. (2023). Gain-Scheduling Controller Synthesis for Nested Systems With Full Block Scalings. IEEE Transactions on Automatic Control, 1–16. https://doi.org/10.1109/TAC.2023.3329851
    62. Schembera, B., Wübbeling, F., Koprucki, T., Biedinger, C., Reidelbach, M., Schmidt, B., Göddeke, D., & Fiedler, J. (2023). Building Ontologies and Knowledge Graphs for Mathematics and its Applications. Proceedings of the Conference on Research Data Infrastructure, 1. https://doi.org/10.52825/cordi.v1i.255
    63. 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
    64. Scherer, C. W., Ebenbauer, C., & Holicki, T. (2023). Optimization Algorithm Synthesis based on Integral Quadratic Constraints: A Tutorial. https://doi.org/10.48550/ARXIV.2306.00565
    65. Schwahn, P., Semmelmann, U., & Weingart, G. (2023). Stability of the Non-Symmetric Space $E_7/PSO(8)$.
    66. 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
    67. Strohbeck, P., Eggenweiler, E., & Rybak, I. (2023). A modification of the Beavers-Joseph condition for arbitrary flows to the fluid-porous interface. Transp. Porous Med., 147(3), Article 3. https://doi.org/10.1007/s11242-023-01919-3
    68. Strohbeck, P., Riethmüller, C., Göddeke, D., & Rybak, I. (2023). Robust and efficient preconditioners for Stokes-Darcy problems. In E. Franck, J. Fuhrmann, V. Michel-Dansac, & L. Navoret (Hrsg.), Finite Volumes for Complex Applications X - Volume 1, Elliptic and Parabolic Problems (S. 375–383). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-40864-9_32
    69. Taha, F. A., Yan, S., & Bitar, E. (2023). A Distributionally Robust Approach to Regret Optimal Control using the Wasserstein Distance. 2023 62nd IEEE Conference on Decision and Control (CDC), 2768–2775. https://doi.org/10.1109/CDC49753.2023.10384311
    70. 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
    71. 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. Barth, A., & Stein, A. (2022). Numerical analysis for time-dependent advection-diffusion problems with random discontinuous coefficients. ESAIM: M2AN, 56(5), Article 5. https://doi.org/10.1051/m2an/2022054
    4. 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.
    5. Berberich, J., Scherer, C. W., & Allgower, F. (2022). Combining Prior Knowledge and Data for Robust Controller Design. IEEE Transactions on Automatic Control, 1--16. https://doi.org/10.1109/tac.2022.3209342
    6. 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
    7. Beschle, C., & Barth, A. (2022). Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5. https://doi.org/10.18419/darus-3154
    8. 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
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    44. 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
    45. 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
    46. 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 (Bd. 134, S. 95--106). Springer, Cham. https://doi.org/10.1007/978-3-030-39647-3\_6
    47. 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
    48. Hitz, T., Keim, J., Munz, C.-D., & Rohde, C. (2020). A parabolic relaxation model for the Navier-Stokes-Korteweg equations. J. Comput. Phys., 421, 109714. https://doi.org/10.1016/j.jcp.2020.109714
    49. 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
    50. Holzmüller, D., & Steinwart, I. (2020). Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent. Fakultät für Mathematik und Physik, Universität Stuttgart.
    51. Holzmüller, D., & Steinwart, I. (2020). Training two-layer ReLU networks with gradient descent is inconsistent. arXiv:2002.04861. https://arxiv.org/abs/2002.04861
    52. 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
    53. Jentsch, T., & Weingart, G. (2020). RIEMANNIAN AND KAHLERIAN NORMAL COORDINATES. ASIAN JOURNAL OF MATHEMATICS, 24(3), Article 3.
    54. 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.
    55. 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
    56. 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
    57. 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
    58. 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
    59. Magiera, J., Ray, D., Hesthaven, J. S., & Rohde, C. (2020). Constraint-aware neural networks for Riemann problems. J. Comput. Phys., 409(109345), Article 109345. https://doi.org/10.1016/j.jcp.2020.109345
    60. 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
    61. 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
    62. 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
    63. 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
    64. Nagy, P.-A., & Semmelmann, U. (2020). Conformal Killing forms in Kaehler geometry.
    65. 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
    66. 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
    67. 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
    68. Ostrowski, L., Massa, F. C., & Rohde, C. (2020). A phase field approach to compressible droplet impingement. In G. Lamanna, S. Tonini, G. E. Cossali, & B. Weigand (Hrsg.), Droplet Interactions and Spray Processes (S. 113–126). Springer International Publishing. https://doi.org/10.1007/978-3-030-33338-6_9
    69. Ostrowski, L., & Rohde, C. (2020). Compressible multicomponent flow in porous media with Maxwell-Stefan diffusion. Math. Meth. Appl. Sci., 43(7), Article 7. https://doi.org/10.1002/mma.6185
    70. Ostrowski, L., & Rohde, C. (2020). Phase field modelling for compressible droplet impingement. In A. Bressan, M. Lewicka, D. Wang, & Y. Zheng (Hrsg.), Hyperbolic Problems: Theory, Numerics, Applications. Proceedings of the Seventeenth International Conference on Hyperbolic Problems 2018 (Bd. 10, S. 586–593). AIMS Series on Applied Mathematics. https://www.aimsciences.org/fileAIMS/cms/news/info/upload//c0904f1f-97d5-451f-b068-25f1612b6852.pdf
    71. Pelinovsky, D. E., & Schneider, G. (2020). The monoatomic FPU system as a limit of a diatomic FPU system. Appl. Math. Lett., 107, 7.
    72. Polyakova, A. P., Svetov, I. E., & Hahn, B. N. (2020). The Singular Value Decomposition of the Operators of the Dynamic Ray Transforms Acting on 2D Vector Fields. In Y. D. Sergeyev & D. E. Kvasov (Hrsg.), Numerical Computations: Theory and Algorithms (S. 446--453). Springer International Publishing. https://doi.org/10.1007/978-3-030-40616-5_42
    73. 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
    74. Rohde, C., & von Wolff, L. (2020). Homogenization of non-local Navier-Stokes-Korteweg equations for compressible liquid-vapour flow in porous media. SIAM J. Math. Anal., 52(6), Article 6. https://doi.org/10.1137/19M1242434
    75. 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
    76. 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
    77. 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
    78. Schneider, G. (2020). The KdV approximation for a system with unstable resonances. Math. Methods Appl. Sci., 43(6), Article 6.
    79. 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
    80. Stein, A., & Barth, A. (2020). A Multilevel Monte Carlo Algorithm for Parabolic Advection-Diffusion Problems with Discontinuous Coefficients. In B. Tuffin & P. L’Ecuyer (Hrsg.), Monte Carlo and Quasi-Monte Carlo Methods (Bd. 324, S. 445--466). Springer International Publishing. https://doi.org/10.1007/978-3-030-43465-6_22
    81. Steinwart, I. (2020). Reproducing Kernel Hilbert Spaces Cannot Contain all Continuous Functions on a Compact Metric Space. Fakultät für Mathematik und Physik, Universität Stuttgart.
    82. 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
    83. 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. Armiti-Juber, A., & Rohde, C. (2019). On Darcy-and Brinkman-type models for two-phase flow in asymptotically flat domains. Comput. Geosci., 23(2), Article 2. https://doi.org/10.1007/s10596-018-9756-2
    3. Armiti-Juber, A., & Rohde, C. (2019). Existence of weak solutions for a nonlocal pseudo-parabolic model for Brinkman two-phase flow in asymptotically flat porous media. J. Math. Anal. Appl., 477(1), Article 1. https://doi.org/10.1016/j.jmaa.2019.04.049
    4. Baggio, G., Zampieri, S., & Scherer, C. W. (2019). Gramian Optimization with Input-Power Constraints. 58th IEEE Conf. Decision and Control, 5686–5691. https://doi.org/10.1109/CDC40024.2019.9029169
    5. Bauer, R., Cummings, P., & Schneider, G. (2019). A model for the periodic water wave problem and its long wave amplitude equations. In Nonlinear water waves. An interdisciplinary interface. Based on the workshop held at the Erwin Schrödinger International Institute for Mathematics and Physics, Vienna, Austria, November 27 -- December 7, 2017 (S. 123--138). Cham: Birkhäuser.
    6. Bauer, R., Düll, W.-P., & Schneider, G. (2019). The Korteweg--de Vries, Burgers and Whitham limits for a spatially periodic Boussinesq model. Proc. Roy. Soc. Edinburgh Sect. A, 149(1), Article 1. https://doi.org/10.1017/S0308210518000227
    7. Bhatt, A., Fehr, J., Grunert, D., & Haasdonk, B. (2019). A Posteriori Error Estimation in Model Order Reduction of Elastic Multibody Systems with Large Rigid Motion. In J. Fehr & B. Haasdonk (Hrsg.), IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22–25, 2018. Springer. https://doi.org/DOI:10.1007/978-3-030-21013-7_7
    8. 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
    9. Bianchi, L. A., Blömker, D., & Schneider, G. (2019). Modulation equation and SPDEs on unbounded domains. Commun. Math. Phys., 371(1), Article 1.
    10. Brünnette, T., Santin, G., & Haasdonk, B. (2019). Greedy Kernel Methods for Accelerating Implicit Integrators for Parametric ODEs. In F. A. Radu, K. Kumar, I. Berre, J. M. Nordbotten, & I. S. Pop (Hrsg.), Numerical Mathematics and Advanced Applications - ENUMATH 2017 (S. 889--896). Springer International Publishing.
    11. Buchfink, P., Bhatt, A., & Haasdonk, B. (2019). Symplectic Model Order Reduction with Non-Orthonormal Bases. Mathematical and Computational Applications, 24(2), 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. Höllig, K., & Hörner, J. (2019). Aufgaben und Lösungen zur Höheren Mathematik. - 1. [Aufgabensammlung]. In Aufgaben und Lösungen zur Höheren Mathematik ; 1 (2. Auflage, Bd. 1, S. x, 235 Seiten). Springer Spektrum.
    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 S. Rogosin & A. O. Celebi (Hrsg.), Analysis as a Life: Dedicated to Heinrich Begehr on the Occasion of his 80th Birthday (S. 237--260). Springer International Publishing. https://doi.org/10.1007/978-3-030-02650-9_12
    34. 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
    35. Kuhn, T., Dürrwächter, J., Meyer, F., Beck, A., Rohde, C., & Munz, C.-D. (2019). Uncertainty quantification for direct aeroacoustic simulations of cavity flows. J. Theor. Comput. Acoust., 27(1), Article 1. https://doi.org/10.1142/S2591728518500445
    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. Comput. Geosci., 2(23), Article 23. 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. Miller, C. T., Gray, W. G., Kees, C. E., Rybak, I. V., & Shepherd, B. J. (2019). Modeling sediment transport in three-phase surface water systems. J. Hydraul. Res., 57. https://doi.org/10.1080/00221686.2019.1581673
    39. 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.
    40. 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
    41. Ostrowski, L., & Massa, F. (2019). An incompressible-compressible approach for droplet impact. In G. Cossali & S. Tonini (Hrsg.), Proceedings of the DIPSI Workshop 2019: Droplet ImpactPhenomena & Spray Investigations, Bergamo, Italy, 17th May 2019 (S. 18–21). Università degli studi di Bergamo. https://doi.org/10.6092/DIPSI2019_pp18-21
    42. Rösinger, C. A., & Scherer, C. W. (2019). 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
    43. 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
    44. Santin, G., & Haasdonk, B. (2019). Kernel Methods for Surrogate Modelling. University of Stuttgart.
    45. Santin, G., & Haasdonk, B. (2019). Kernel Methods for Surrogate Modeling (ArXiv No. 1907.10556; Nummer 1907.10556). https://arxiv.org/abs/1907.10556
    46. Santin, G., Wittwar, D., & Haasdonk, B. (2019). Sparse approximation of regularized kernel interpolation by greedy algorithms.
    47. 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
    48. 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
    49. 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
    50. 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
    51. 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
    52. Sharanya, V., Sekhar, G. P. R., & Rohde, C. (2019). Surfactant-induced migration of a spherical droplet in non-isothermal Stokes flow. Physics of Fluids, 31(1), Article 1. https://doi.org/10.1063/1.5064694
    53. Steinwart, I. (2019). A Sober Look at Neural Network Initializations. Fakultät für Mathematik und Physik, Universität Stuttgart.
    54. 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
    55. Wenzel, T., Santin, G., & Haasdonk, B. (2019). A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution.
    56. Wittwar, D., & Haasdonk, B. (2019). Greedy Algorithms for Matrix-Valued Kernels. In F. A. Radu, K. Kumar, I. Berre, J. M. Nordbotten, & I. S. Pop (Hrsg.), Numerical Mathematics and Advanced Applications ENUMATH 2017 (S. 113--121). Springer International Publishing.
    57. Wittwar, D., Santin, G., & Haasdonk, B. (2019). Part II on matrix valued kernels including analysis.
    58. 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
    59. 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
    60. 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. Altenbernd, M., & Göddeke, D. (2018). Soft fault detection and correction for multigrid. The International Journal of High Performance Computing Applications, 32(6), Article 6. https://doi.org/10.1177/1094342016684006
    3. Barth, A., & Kröker, I. (2018). Finite Volume Methods for Hyperbolic Partial Differential Equations with Spatial Noise. In C. Klingenberg & M. Westdickenberg (Hrsg.), Theory, Numerics and Applications of Hyperbolic Problems I (S. 125--135). Springer International Publishing.
    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., & 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
    6. 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
    7. Bhatt, A., Fehr, J., Grunert, D., & Haasdonk, B. (2018). A Posteriori Error Estimation in Model Order Reduction of Elastic Multibody Systems with Large Rigid Motion. In J. Fehr & B. Haasdonk (Hrsg.), IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22–25, 2018. Springer. https://doi.org/DOI:10.1007/978-3-030-21013-7_7
    8. Bhatt, A., & Haasdonk, B. (2018). Certified and structure-preserving model order reduction of EMBS. In RAMSA 2017, New Delhi.
    9. Bhatt, A., Haasdonk, B., & Moore, B. E. (2018). Structure-preserving Integration and Model Order Reduction. In Invited online talk in Department of Mathematics, IIT Roorkee.
    10. 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
    11. Bradley, C. P., Emamy, N., Ertl, T., Göddeke, D., Hessenthaler, A., Klotz, T., Krämer, A., Krone, M., Maier, B., Mehl, M., Tobias, R., & Röhrle, O. (2018). Enabling Detailed, Biophysics-Based Skeletal Muscle Models on HPC Systems. Frontiers in Physiology, 9(816), Article 816. https://doi.org/10.3389/fphys.2018.00816
    12. Brehler, M., Schirwon, M., Göddeke, D., & Krummrich, P. (2018, Juli). Modeling the Kerr-Nonlinearity in Mode-Division Multiplexing Fiber  Transmission Systems on GPUs. Proceedings of Advanced Photonics 2018.
    13. Brünnette, T., Santin, G., & Haasdonk, B. (2018). Greedy kernel methods for accelerating implicit integrators for parametric ODEs. Proc. ENUMATH 2017.
    14. Buchfink, P. (2018). Structure-preserving Model Reduction for Elasticity [Diploma thesis].
    15. Chalons, C., Magiera, J., Rohde, C., & Wiebe, M. (2018). A finite-volume tracking scheme for two-phase compressible flow. Springer Proc. Math. Stat., 309--322. https://doi.org/10.1007/978-3-319-91545-6_25
    16. 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
    17. 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
    18. 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
    19. 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
    20. 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
    21. 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
    22. 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
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    24. 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
    25. 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
    26. 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.
    27. Dürrwächter, J., Kuhn, T., Meyer, F., Schlachter, L., & Schneider, F. (2018). A hyperbolicity-preserving discontinuous stochastic Galerkin scheme  for uncertain hyperbolic systems of equations. Journal of Computational and Applied Mathematics, 112602. https://doi.org/10.1016/j.cam.2019.112602
    28. Engwer, C., Altenbernd, M., Dreier, N.-A., & Göddeke, D. (2018, März). A high-level C++ approach to manage local errors, asynchrony and  faults in an MPI application. Proceedings of the 26th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2018).
    29. 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
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    31. 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.
    32. 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
    33. 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
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    37. 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
    38. Gimperlein, H., Meyer, F., Özdemir, C., Stark, D., & Stephan, E. P. (2018). Boundary elements with mesh refinements for the wave equation. Numer. Math., 139(4), Article 4. https://doi.org/10.1007/s00211-018-0954-6
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    49. Holicki, T., & Scherer, C. W. (2018). Output-Feedback Gain-Scheduling Synthesis for a Class of Switched Systems via Dynamic Resetting $D$-Scalings. 57th IEEE Conf. Decision and Control, 6440–6445. https://doi.org/10.1109/CDC.2018.8619128
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    52. Kohr, M., & Wendland, W. L. (2018). Variational approach for the Stokes and Navier-Stokes systems with nonsmooth coefficients in Lipschitz domains on compact Riemannian manifolds. Calc. Var. Partial Differential Equations, 57(6), Article 6. https://doi.org/10.1007/s00526-018-1426-7
    53. 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
    54. 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
    55. 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
    56. Köppel, M., Martin, V., Jaffré, J., & Roberts, J. E. (2018). A Lagrange multiplier method for a discrete fracture model for flow  in porous media. (submitted). https://hal.archives-ouvertes.fr/hal-01700663v2
    57. Köppel, 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. Köppl, T., Santin, G., Haasdonk, B., & Helmig, R. (2018). Numerical modelling of a peripheral arterial stenosis using dimensionally  reduced models and kernel methods. International Journal for Numerical Methods in Biomedical Engineering, 0(ja), Article ja. https://doi.org/10.1002/cnm.3095
    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). 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
    61. 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
    62. 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
    63. Magiera, J., & Rohde, C. (2018). A particle-based multiscale solver for compressible liquid-vapor flow. Springer Proc. Math. Stat., 291--304. https://doi.org/10.1007/978-3-319-91548-7_23
    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 Péclet number. International Journal of Multiphase Flow, 107, 82–103. 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., 3, Article 3. 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 (Hrsg.), New trends and results in mathematical description of fluid flows (S. 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 No. 1807.09575; Nummer 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 Appl. Mech. Eng., 333, 331--355. https://doi.org/10.1016/j.cma.2018.01.029
    81. Seus, D., Pop, I. S., Rohde, C., Mitra, K., & Radu, F. (2018). A linear domain decompostition method for partially saturated flow in porous media. Comput. Methods Appl. Mech. Eng., 333, 331–355. https://doi.org/10.1016/j.cma.2018.01.029
    82. 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, Article 107. https://doi.org/10.1016/j.ijmultiphaseflow.2018.05.008
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    84. 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
    85. 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
    86. 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. 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
    2. Alkämper, M., Klöfkorn, R., & Gaspoz, F. (2017). A Weak Compatibility Condition for Newest Vertex Bisection in any  Dimension. http://arxiv.org/abs/1711.03141
    3. 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
    4. 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.
    5. 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
    6. Alla, A., Schmidt, A., & Haasdonk, B. (2017). Model Order Reduction Approaches for Infinite Horizon Optimal Control  Problems via the HJB Equation. In P. Benner, M. Ohlberger, A. Patera, G. Rozza, & K. Urban (Hrsg.), Model Reduction of Parametrized Systems (S. 333--347). Springer International Publishing. https://doi.org/10.1007/978-3-319-58786-8_21
    7. Barth, A., & Fuchs, F. G. (2017). Uncertainty quantification for linear hyperbolic equations with    stochastic process or random field coefficients. APPLIED NUMERICAL MATHEMATICS, 121, 38–51. https://doi.org/10.1016/j.apnum.2017.06.009
    8. 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
    9. 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
    10. Barth, A., & Stein, A. (2017). A study of elliptic partial differential equations with jump diffusion  coefficients.
    11. 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 (Hrsg.), Model Reduction and Approximation: Theory and Algorithms. SIAM Philadelphia. https://www2.mpi-magdeburg.mpg.de/preprints/2015/MPIMD15-01.pdf
    12. Bhatt, A., & VanGorder, R. (2017). Chaos in a non-autonomous nonlinear system describing asymmetric  water wheels.
    13. 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
    14. 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
    15. Bürger, R., & Kröker, I. (2017). Hybrid Stochastic Galerkin Finite Volumes for the Diffusively Corrected  Lighthill-Whitham-Richards Traffic Model. In C. Cancès & P. Omnes (Hrsg.), Finite Volumes for Complex Applications VIII - Hyperbolic, Elliptic  and Parabolic Problems: FVCA 8, Lille, France, June 2017 (S. 189--197). Springer International Publishing. https://doi.org/10.1007/978-3-319-57394-6_21
    16. 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
    17. Chalons, C., Rohde, C., & Wiebe, M. (2017). A finite volume method for undercompressive shock waves in two space dimensions. ESAIM Math. Model. Numer. Anal., 51(5), Article 5. https://doi.org/10.1051/m2an/2017027
    18. 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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    20. De Marchi, S., Iske, A., & Santin, G. (2017). Image Reconstruction from Scattered Radon Data by Weighted Positive  Definite Kernel Functions.
    21. 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
    22. 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
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