2018

  1. A

    1. B. M. Afkham, A. Bhatt, B. Haasdonk, and J. S. Hesthaven, “Symplectic Model-Reduction with a Weighted Inner Product,” 2018.
  2. B

    1. A. Barth and T. Stüwe, “Weak convergence of Galerkin approximations of stochastic partial  differential equations driven by additive Lévy noise,” Math. Comput. Simulation, vol. 143, pp. 215--225, 2018.
    2. F. Bayer, M. A. Müller, and F. Allgöwer, “On optimal system operation in robust economic MPC,” Automatica, vol. 88, pp. 98--106, 2018.
    3. A. Bhatt, J. Fehr, and B. Haasdonk, “Model order reduction of an elastic body under large rigid motion,” Proceedings of ENUMATH 2017, 2018.
    4. A. Bhatt, J. Fehr, and B. Hassdonk, “Model Order Reduction of an Elastic Body under Large Rigid Motion,” in Proceedings of ENUMATH 2017, Voss, Norway, 2018.
    5. A. Bhatt, B. Haasdonk, and B. E. Moore, “Structure-preserving Integration and Model Order Reduction.” 2018.
    6. C. Bradley et al., Towards realistic HPC models of the neuromuscular system. 2018.
    7. M. Brehler, M. Schirwon, D. Göddeke, and P. Krummrich, “Modeling the Kerr-Nonlinearity in Mode-Division Multiplexing Fiber  Transmission Systems on GPUs,” in Proceedings of Advanced Photonics 2018, 2018.
    8. F. D. Brunner, D. Antunes, and F. Allgöwer, “Stochastic Thresholds in Event-Triggered Control: A Consistent Policy for Quadratic Control,” Automatica, vol. 89, pp. 376--381, 2018.
    9. T. Brünnette, G. Santin, and B. Haasdonk, “Greedy kernel methods for accelerating implicit integrators for parametric  ODEs,” 2018, vol. Proceedings of ENUMATH 2017.
    10. P. Buchfink, “Structure-preserving Model Reduction for Elasticity,” 2018.
  3. C

    1. K. Carlberg, L. Brencher, B. Haasdonk, and A. Barth, “Data-driven time parallelism via forecasting,” SIAM J. of Sci. Comp., 2018.
  4. D

    1. S. De Marchi, A. Iske, and G. Santin, “Image reconstruction from scattered Radon data by weighted positive  definite kernel functions,” Calcolo, vol. 55, no. 1, p. 2, 2018.
    2. C. Dibak, B. Haasdonk, A. Schmidt, F. Dürr, and K. Rothermel, “Enabling Interactive Mobile Simulations Through Distributed Reduced Models,” Pervasive and Mobile Computing, 2018.
    3. N.-A. Dreier, M. Altenbernd, C. Engwer, and D. Göddeke, “A high-level C++ approach to manage local errors, asynchrony and  faults in an MPI application,” in Proceedings of 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2018), 2018.
  5. E

    1. C. Engwer, M. Altenbernd, N.-A. Dreier, and D. G�ddeke, “A high-level C++ approach to manage local errors, asynchrony and  faults in an MPI application,” in Proceedings of the 26th Euromicro International Conference on Parallel,  Distributed and Network-Based Processing (PDP 2018), 2018.
    2. C. Engwer, M. Altenbernd, N.-A. Dreier, and D. Göddeke, “A high-level C++ approach to manage local errors, asynchrony and  faults in an MPI application,” in Proceedings of the 26th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2018), 2018.
  6. F

    1. S. Fechter, C.-D. Munz, C. Rohde, and C. Zeiler, “Approximate Riemann solver for compressible liquid vapor flow with  phase transition and surface tension,” Comput. & Fluids, vol. 169, pp. 169–185, 2018.
    2. J. Fehr, D. Grunert, A. Bhatt, and B. Haasdonk, “A sensitivity study of error estimation in reduced elastic multibody systems,” Proceedings of MATHMOD 2018, 2018.
    3. V. Ferrario, N. Hansen, and J. Pleiss, “Interpretation of cytochrome P450 monooxygenase kinetics by modeling of thermodynamic activity,” J Inorg Biochem, 2018.
    4. B. Flemisch et al., “Benchmarks for single-phase flow in fractured porous media,” Advances in Water Resources, vol. 111, pp. 239--258, 2018.
    5. F. Fritzen, B. Haasdonk, D. Ryckelynck, and S. Schöps, “An algorithmic comparison of the Hyper-Reduction and the Discrete  Empirical Interpolation Method for a nonlinear thermal problem,” Math. Comput. Appl. 2018, vol. 23, no. 1, 2018.
  7. G

    1. J. Giesselmann, N. Kolbe, M. Lukacova-Medvidova, and N. Sfakianakis, “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., 2018.
    2. H. Gimperlein, F. Meyer, C. �zdemir, and E. P. Stephan, “Time domain boundary elements for dynamic contact problems,” Computer Methods in Applied Mechanics and Engineering, vol. 333, pp. 147–175, 2018.
    3. H. Gimperlein, F. Meyer, C. �zdemir, D. Stark, and E. P. Stephan, “Boundary elements with mesh refinements for the wave equation.,” Numer. Math., p. (accepted), 2018.
    4. C. Y. Guo, “Robust Gain-Scheduled Controller Design with a Hierarchical Structure,” 9th IFAC Symposium on Robust Control Design, 2018.
  8. H

    1. B. Haasdonk and G. Santin, “Greedy Kernel Approximation for Sparse Surrogate Modeling,” in Reduced-Order Modeling (ROM) for Simulation and Optimization: Powerful  Algorithms as Key Enablers for Scientific Computing, W. Keiper, A. Milde, and S. Volkwein, Eds. Cham: Springer International Publishing, 2018, pp. 21--45.
    2. S. Haesaert, S. Weiland, and C. W. Scherer, A separation theorem for guaranteed $H_2$ performance through matrix inequalities. Automatica, 2018.
    3. T. Holicki and C. W. Scherer, “A Swapping Lemma for Switched Systems,” 9th IFAC Symposium on Robust Control Design, 2018.
    4. T. Holicki and C. W. Scherer, “An IQC theorem for relations: Towards stability analysis of data-integrated systems,” 9th IFAC Symposium on Robust Control Design, 2018.
  9. J

    1. A. Jensch et al., “The tumor suppressor protein DLC1 maintains protein kinase D activity and Golgi secretory function,” Journal of Biological Chemistry, vol. 293, no. 37, pp. 14407–14416, 2018.
  10. K

    1. B. Kane, R. Kloefkorn, and A. Dedner, “Adaptive Discontinuous Galerkin Methods for flow in porous media,” Proceedings of ENUMATH 2017, the 12th European conference on numerical mathematics and advanced applications, 2018.
    2. T. Kuhn, J. Dürrwächter, A. Beck, C.-D. Munz, F. Meyer, and C. Rohde, “Uncertainty Quantification for Direct Aeroacoustic Simulations of  Cavity Flows,” 2018.
    3. T. Kuhn, J. Dürrwächter, F. Meyer, A. Beck, C. Rohde, and C.-D. Munz, Uncertainty Quantification for Direct Aeroacoustic Simulations of Cavity Flows. 2018.
    4. K. Kuritz and F. Allgöwer, “Broadcast control of oscillating cell populations.” 2018.
    5. K. Kuritz and F. Allgöwer, “Therapy design by broadcast control of oscillating cell populations.” 2018.
    6. K. Kuritz, W. Halter, and F. Allgöwer, “Passivity-based ensemble control for cell cycle synchronization,” Lecture Notes in Control and Information Sciences - Proceedings, 2018.
    7. K. Kuritz, D. Imig, M. Dyck, and F. Allgöwer, “Ensemble control for cell cycle synchronization of heterogeneous cell populations,” IFAC-PapersOnLine, vol. 51, no. 19, pp. 44–47, 2018.
    8. K. Kuritz, W. Halter, and F. Allgöwer, “Passivity-based ensemble control for cell cycle synchronization,” in Emerg. Appl. Control Syst. Theory, 1st ed., R. Tempo, S. Yurkovich, and P. Misra, Eds. Springer International Publishing, 2018.
    9. J. Köhler, M. A. Müller, and F. Allgöwer, “Nonlinear reference tracking: An economic model predictive control perspective,” IEEE Transactions on Automatic Control, 2018.
    10. J. Köhler, M. A. Müller, and F. Allgöwer, A nonlinear tracking model predictive control scheme using reference generic terminal ingredients. 2018.
    11. M. Köppel, V. Martin, J. Jaffre, and J. E. Roberts, A Lagrange multiplier method for a discrete fracture model for flow in porous media. 2018.
    12. M. Köppel, V. Martin, and J. E. Roberts, A stabilized Lagrange multiplier finite-element method for flow in porous media with fractures. 2018.
    13. T. Köppl, G. Santin, B. Haasdonk, and R. Helmig, “Numerical modelling of a peripheral arterial stenosis using dimensionally  reduced models and kernel methods,” International Journal for Numerical Methods in Biomedical Engineering, vol. 0, no. ja, p. e3095, 2018.
    14. M. K�ppel, V. Martin, J. Jaffré, and J. E. Roberts, “A Lagrange multiplier method for a discrete fracture model for flow  in porous media,” (submitted), 2018.
    15. M. K�ppel, V. Martin, and J. E. Roberts, “A stabilized Lagrange multiplier finite-element method for flow in  porous media with fractures,” (submitted), 2018.
  11. L

    1. A. Langer, “Investigating the influence of box-constraints on the solution of  a total variation model via an efficient primal-dual method,” Journal of Imaging, vol. 4, p. 1, 2018.
    2. A. Langer, “Locally adaptive total variation for removing mixed Gaussian-impulse  noise,” International Journal of Computer Mathematics, p. 19, 2018.
    3. A. Langer, “Overlapping domain decomposition methods for total variation denoising,” 2018.
    4. S. Linsenmayer, H. Ishii, and F. Allgöwer, “Containability With Event-Based Sampling for Scalar Systems With Time-Varying Delay and Uncertainty,” IEEE Control Systems Letters, 2018.
    5. S. Linsenmayer and F. Allgöwer, “Performance oriented triggering mechanisms with guaranteed traffic characterization for linear discrete-time systems,” in Proc. European Control Conf. (ECC), Limassol, Cyprus, 2018, pp. 1474–1479.
    6. M. Lotti, J. Pleiss, F. Valero, and P. Ferrer, “Enzymatic production of biodiesel: strategies to overcome methanol inactivation,” Biotechnol J, 2018.
  12. M

    1. B. Maboudi Afkham and J. S. Hesthaven, “Structure-Preserving Model-Reduction of Dissipative Hamiltonian Systems,” Journal of Scientific Computing, pp. 1–19, 2018.
    2. J. Meisner, J. Karwounopoulos, P. Walther, J. Kästner, and S. Naumann, “The Lewis Pair Polymerization of Lactones Using Metal Halides and N-Heterocyclic Olefins: Theoretical Insights,” Molecules, vol. 23, no. 2, 2018.
    3. F. Meyer, L. Schlachter, and F. Schneider, “A hyperbolicity-preserving discontinuous stochastic Galerkin scheme  for uncertain hyperbolic systems of equations,” 2018.
  13. N

    1. A. Nateghi, H. Dal, M.-A. Keip, and C. Miehe, “An affine microsphere approach to modeling strain-induced crystallization in rubbery polymers,” Continuum Mechanics and Thermodynamics, pp. 1--23, 2018.
    2. K. Nguyen and M.-A. Keip, “A data-driven approach to nonlinear elasticity,” Computers & Structures, vol. 194, pp. 97--115, 2018.
  14. P

    1. D. Paul and N. Radde, “The role of stochastic sequestration dynamics for intrinsic noise filtering in signaling network motifs,” Journal of Theoretical Biology, vol. 455, pp. 86–96, 2018.
    2. D. Pfander, M. Brunn, and D. Pflüger, “AutoTuneTMP: Auto-Tuning in C++ With Runtime Template Metaprogramming,” 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2018.
    3. D. Pfander, G. Daiß, D. Pflüger, D. Marcello, and H. Kaiser, “Accelerating Octo-Tiger: Stellar Mergers on Intel Knights Landing with HPX,” Proceedings of the 6th International Workshop on OpenCL, 2018.
  15. R

    1. G. P. Raja Sekhar, V. Sharanya, and C. Rohde, “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, 2018.
    2. P. Rehner and J. Gross, “Surface tension of droplets and Tolman lengths of real substances and mixtures from density functional theory,” THE JOURNAL OF CHEMICAL PHYSICS, vol. 148, p. 164703, 2018.
    3. C. Rohde and C. Zeiler, “On Riemann Solvers and Kinetic Relations for Isothermal Two-Phase  Flows with Surface Tension,” Z. Angew. Math. Phys., p. 69:76, 2018.
  16. S

    1. C. W. Scherer and J. Veenman, “Stability analysis by dynamic dissipation inequalities: On merging frequency-domain techniques with time-domain conditions,” Syst. Contr. Letters, 2018.
    2. C. W. Scherer and T. Holicki, “Output-Feedback Gain-Scheduling for a Class of Switched Systems via Dynamic Resetting D-Scalings,” 57th IEEE Conf. Decision and Control, 2018.
    3. A. Schmidt and B. Haasdonk, “Data-driven surrogates of value functions and applications to feedback control for dynamical systems,” MathMod 2018, 2018.
    4. A. Schmidt, D. Wittwar, and B. Haasdonk, “Rigorous and effective a-posteriori error bounds for nonlinear problems - Application to RB methods,” University of Stuttgart, 2018.
    5. M. Schneider, T. Koeppl, R. Helmig, R. Steinle, and R. Hilfer, “Stable Propagation of Saturation Overshoots for Two-Phase Flow in Porous Media,” Transport in Porous Media, vol. 121, pp. 621--641, 2018.
    6. D. Seus, K. Mitra, I. S. Pop, F. A. Radu, and C. Rohde, “A linear domain decomposition method for partially saturated flow  in porous media,” Comp. Methods in Appl. Mech. Eng, vol. 333, pp. 331--355, 2018.
  17. T

    1. P. Tempel, F. Trautwein, and A. Pott, Experimental Validation of Cable Strain Dynamics Models of UHMWPE Dyneema Fibers for Improving Cable Tension Control Strategies. Springer Verlag; Springer International Publishing, 2018.
    2. P. Tempel, D. Lee, and A. Pott, Elastic-Flexible Cable Models with Time-Varying Length for Cable-Driven Parallel Robots - A Rayleigh-Ritz Approach. IEEE, 2018.
  18. V

    1. J. Valentin, M. Sprenger, D. Pflüger, and O. Röhrle, “Gradient-Based Optimization with B-Splines on Sparse Grids for Solving Forward-Dynamics Simulations of Three-Dimensional, Continuum-Mechanical Musculoskeletal System Models,” International Journal for Numerical Methods in Biomedical Engineering, 2018.
    2. J. Valentin and D. Pflüger, “Fundamental Splines on Sparse Grids and Their Application to Gradient-Based Optimization,” Sparse Grids and Applications - Miami 2016, 2018.
  19. W

    1. C. Waibel and J. Gross, “Modification of the Wolf Method and Evaluation for Molecular Simulation of Vapor-Liquid Equilibria,” Journal of Chemical Theory and Computation, vol. 14, no. 4, pp. 2198--2206, 2018.
    2. C. Waibel, R. Stierle, and J. Gross, “Transferability of cross-interaction pair potentials: Vapor-liquid phase equilibria of n-alkane/nitrogen mixtures using the TAMie force field,” Fluid Phase Equilibria, vol. 456, pp. 124--130, 2018.
    3. D. Wittwar and B. Haasdonk, “Greedy Algorithms for Matrix-Valued Kernels,” University of Stuttgart, 2018.
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