Publications 2018

  1. B

    1. F. Bayer, M. A. Müller, and F. Allgöwer, “On optimal system operation in robust economic MPC,” Automatica, vol. 88, pp. 98--106, 2018, doi: 10.1016/j.automatica.2017.11.007.
    2. B. Becker, B. Guo, K. Bandilla, M. A. Celia, B. Flemisch, and R. Helmig, “An Adaptive Multiphysics Model Coupling Vertical Equilibrium and Full Multidimensions for Multiphase Flow in Porous Media,” Water Resources Research, vol. 54, no. 7, Art. no. 7, 2018, doi: 10.1029/2017wr022303.
    3. A. Bhatt, J. Fehr, and B. Haasdonk, “Model order reduction of an elastic body under large rigid motion,” Proceedings of ENUMATH 2017, 2018, [Online]. Available:
    4. C. Bradley et al., Towards realistic HPC models of the neuromuscular system. 2018. [Online]. Available:
    5. K. Breitsprecher, C. Holm, and S. Kondrat, “Charge Me Slowly, I Am in a Hurry: Optimizing Charge–Discharge Cycles in Nanoporous Supercapacitors,” ACS Nano, vol. 12, no. 10, Art. no. 10, Aug. 2018, doi: 10.1021/acsnano.8b04785.
    6. 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, doi: 10.1016/j.automatica.2017.12.043.
  2. C

    1. K. Carlberg, L. Brencher, B. Haasdonk, and A. Barth, “Data-driven time parallelism via forecasting,” SIAM J. of Sci. Comp., 2018, [Online]. Available:
    2. K. Carlberg, L. Brencher, B. Haasdonk, and A. Barth, “Data-driven time parallelism via forecasting,” SIAM Journal on Scientific Computing, vol. 41, no. 3, Art. no. 3, 2019, doi: 10.1137/18M1174362.
  3. D

    1. A. Denzel and J. Kästner, “Gaussian process regression for geometry optimization,” Journal of chemical physics, vol. 148, no. 9, Art. no. 9, 2018, doi: 10.1063/1.5017103.
    2. A. Denzel and J. Kästner, “Gaussian Process Regression for Transition State Search,” Journal of chemical theory and computation, vol. 14, no. 11, Art. no. 11, 2018, doi: 10.1021/acs.jctc.8b00708.
    3. C. Dibak, B. Haasdonk, A. Schmidt, F. Dürr, and K. Rothermel, “Enabling Interactive Mobile Simulations Through Distributed Reduced Models,” Pervasive and Mobile Computing, 2018, doi: 10.1016/j.pmcj.2018.02.002.
    4. D. Driess et al., “Learning to control redundant musculoskeletal systems with neural networks and SQP: exploiting muscle properties,” in 2018 IEEE International Conference on Robotics and Automation (ICRA), in 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018, pp. 6461--6468.
  4. E

    1. J. Escher and C. Lienstromberg, “Travelling waves in dilatant non-Newtonian thin films,” J. Differential Equations, vol. 264, no. 3, Art. no. 3, 2018, doi: 10.1016/j.jde.2017.10.015.
  5. F

    1. 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, [Online]. Available:
    2. V. Ferrario, N. Hansen, and J. Pleiss, “Interpretation of cytochrome P450 monooxygenase kinetics by modeling of thermodynamic activity,” J Inorg Biochem, 2018, [Online]. Available:
    3. D. Fink, A. Wagner, and W. Ehlers, “Application-driven model reduction for the simulation of therapeutic infusion processes in multi-component brain tissue,” JOURNAL OF COMPUTATIONAL SCIENCE, vol. 24, pp. 101–115, Jan. 2018, doi: 10.1016/j.jocs.2017.10.002.
    4. B. Flemisch et al., “Benchmarks for single-phase flow in fractured porous media,” Advances in Water Resources, vol. 111, pp. 239--258, 2018, doi: 10.1016/j.advwatres.2017.10.036.
  6. G

    1. C. Y. Guo, “Robust Gain-Scheduled Controller Design with a Hierarchical Structure,” 9th IFAC Symposium on Robust Control Design, 2018, [Online]. Available:
    2. Y. Guo and C. W. Scherer, “Robust Gain-Scheduled Controller Design with a Hierarchical Structure,” in IFAC-PapersOnline, in IFAC-PapersOnline, vol. 51. 2018, pp. 228–233. doi: 10.1016/j.ifacol.2018.11.110.
  7. H

    1. S. Haesaert, S. Weiland, and C. W. Scherer, A separation theorem for guaranteed $H_2$ performance through matrix inequalities. Automatica, 2018. [Online]. Available:
    2. D. F. Haeufle, B. Schmortte, H. Geyer, R. Müller, and S. Schmitt, “The benefit of combining neuronal feedback and feed-forward control for robustness in step down perturbations of simulated human walking depends on the muscle function,” Frontiers in computational neuroscience, vol. 12, p. 80, 2018.
    3. S. Hocker, H. Lipp, E. Eisfeld, S. Schmauder, and J. Roth, “Precipitation strengthening in Cu--Ni--Si alloys modeled with ab initio based interatomic potentials,” The Journal of chemical physics, vol. 149, no. 2, Art. no. 2, 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, [Online]. Available:
    5. T. Holicki and C. W. Scherer, “A Swapping Lemma for Switched Systems,” 9th IFAC Symposium on Robust Control Design, 2018, [Online]. Available:
    6. T. Holicki and C. W. Scherer, “Output-Feedback Gain-Scheduling Synthesis for a Class of Switched Systems via Dynamic Resetting $D$-Scalings,” in 2018 IEEE Conference on Decision and Control (CDC), in 2018 IEEE Conference on Decision and Control (CDC). Dec. 2018, pp. 6440–6445. doi: 10.1109/CDC.2018.8619128.
  8. 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, [Online]. Available:
    2. J. Kaufmann et al., “Direct numerical simulations of one- and two-component droplet wall-film interactions within the crown-type splashing regime,” in ID 266, ICLASS 2018, 14th Triennial International Conference on Liquid Atomization and Spray Systems, in ID 266, ICLASS 2018, 14th Triennial International Conference on Liquid Atomization and Spray Systems. Chicago, USA: University of Illinois, Jul. 2018.
    3. A. N. Krishnamoorthy, C. Holm, and J. Smiatek, “Influence of Cosolutes on Chemical Equilibrium: a Kirkwood–Buff Theory for Ion Pair Association–Dissociation Processes in Ternary Electrolyte Solutions,” The Journal of Physical Chemistry C, vol. 122, no. 19, Art. no. 19, Apr. 2018, doi: 10.1021/acs.jpcc.7b12255.
    4. K. Kuritz, W. Halter, and F. Allgöwer, “Passivity-based ensemble control for cell cycle synchronization,” Lecture Notes in Control and Information Sciences - Proceedings, 2018, [Online]. Available:
    5. J. Köhler, M. A. Müller, and F. Allgöwer, A nonlinear tracking model predictive control scheme using reference generic terminal ingredients. 2018. [Online]. Available:
    6. J. Köhler, M. A. Müller, and F. Allgöwer, “Nonlinear reference tracking: An economic model predictive control perspective,” IEEE Transactions on Automatic Control, 2018, doi: 10.1109/TAC.2018.2800789.
    7. 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. [Online]. Available:
    8. M. Köppel, V. Martin, and J. E. Roberts, A stabilized Lagrange multiplier finite-element method for flow in porous media with fractures. 2018. [Online]. Available:
  9. L

    1. 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, doi: 10.1109/LCSYS.2018.2847449.
    2. M. Lotti, J. Pleiss, F. Valero, and P. Ferrer, “Enzymatic production of biodiesel: strategies to overcome methanol inactivation,” Biotechnol J, 2018, [Online]. Available:
  10. M

    1. 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, Art. no. 2, 2018, doi: 10.3390/molecules23020432.
    2. J. Michalowsky, J. Zeman, C. Holm, and J. Smiatek, “A polarizable MARTINI model for monovalent ions in aqueous solution,” The Journal of Chemical Physics, vol. 149, no. 16, Art. no. 16, Oct. 2018, doi: 10.1063/1.5028354.
  11. N

    1. A. Narayanan Krishnamoorthy, C. Holm, and J. Smiatek, “Specific ion effects for polyelectrolytes in aqueous and non-aqueous media: the importance of the ion solvation behavior,” Soft Matter, vol. 14, no. 30, Art. no. 30, 2018, doi: 10.1039/C8SM00600H.
    2. 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, doi: 10.1007/s00161-017-0612-8.
    3. K. Nguyen and M.-A. Keip, “A data-driven approach to nonlinear elasticity,” Computers & Structures, vol. 194, pp. 97--115, 2018, doi: 10.1016/j.compstruc.2017.07.031.
  12. P

    1. 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, [Online]. Available:
    2. 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, [Online]. Available:
  13. R

    1. 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, doi: 10.1063/1.5020421.
    2. T. Ricken, N. Waschinsky, and D. Werner, “Simulation of steatosis zonation in liver lobule—a continuummechanical bi-scale, tri-phasic, multi-component approach,” in Biomedical technology, in Biomedical technology. , Springer, 2018, pp. 15--33.
    3. A. Romer, J. M. Montenbruck, and F. Allgöwer, “Data-driven inference of conic relations via saddle-point dynamics,” in Proc. 9th IFAC Symp. Robust Control Design (ROCOND), in Proc. 9th IFAC Symp. Robust Control Design (ROCOND). Florianópolis, Brazil, 2018, pp. 586–591. doi: 10.1016/j.ifacol.2018.11.139.
    4. A. Romer, J. M. Montenbruck, and F. Allgöwer, “Some ideas on sampling strategies for data-driven inference of passivity properties for MIMO systems,” in 2018 Annual American Control Conference (ACC), in 2018 Annual American Control Conference (ACC). IEEE, 2018, pp. 6094--6100.
  14. S

    1. G. Santin, D. Wittwar, and B. Haasdonk, “Greedy regularized kernel interpolation,” arXiv preprint arXiv:1807.09575, 2018.
    2. 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, [Online]. Available:
    3. A. Schmidt and B. Haasdonk, “Data-driven surrogates of value functions and applications to feedback control for dynamical systems,” MathMod 2018, 2018, [Online]. Available:
    4. M. Schneider, B. Flemisch, R. Helmig, K. Terekhov, and H. Tchelepi, “Monotone nonlinear finite-volume method for challenging grids,” Computational Geosciences, 2018, doi: 10.1007/s10596-017-9710-8.
    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, doi: 10.1007/s11242-017-0977-y.
    6. R. Soloperto, M. A. Müller, S. Trimpe, and F. Allgöwer, “Learning-based robust model predictive control with state-dependent uncertainty,” IFAC-PapersOnLine, vol. 51, no. 20, Art. no. 20, 2018.
  15. T

    1. 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. [Online]. Available:
    2. 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. [Online]. Available:
  16. U

    1. F. Uhlig, J. Zeman, J. Smiatek, and C. Holm, “First-Principles Parametrization of Polarizable Coarse-Grained Force Fields for Ionic Liquids,” Journal of Chemical Theory and Computation, vol. 14, no. 3, Art. no. 3, Jan. 2018, doi: 10.1021/acs.jctc.7b00903.
  17. V

    1. J. Valentin and D. Pflüger, “Fundamental Splines on Sparse Grids and Their Application to Gradient-Based Optimization,” Sparse Grids and Applications - Miami 2016, 2018, [Online]. Available:
    2. 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, doi: 10.1002/cnm.2965.
  18. 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, Art. no. 4, 2018, doi: 10.1021/acs.jctc.7b01190.
    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, doi: 10.1016/j.fluid.2017.09.024.
    3. R. Weeber, M. Hermes, A. M. Schmidt, and C. Holm, “Polymer architecture of magnetic gels: a review,” Journal of Physics: Condensed Matter, vol. 30, no. 6, Art. no. 6, Jan. 2018, doi: 10.1088/1361-648x/aaa344.
    4. A. Weyman, M. Bier, C. Holm, and J. Smiatek, “Microphase separation and the formation of ion conductivity channels in poly(ionic liquid)s: A coarse-grained molecular dynamics study,” The Journal of Chemical Physics, vol. 148, no. 19, Art. no. 19, May 2018, doi: 10.1063/1.5016814.
    5. D. Wittwar and B. Haasdonk, Greedy Algorithms for Matrix-Valued Kernels. 2018. [Online]. Available:
  19. Y

    1. G. Yang, B. Weigand, A. Terzis, K. Weishaupt, and R. Helmig, “Numerical Simulation of Turbulent Flow and Heat Transfer in a Three-Dimensional Channel Coupled with Flow Through Porous Structures,” Transport In Porous Media, vol. 122, no. 1, Art. no. 1, 2018, doi: 10.1007/s11242-017-0995-9.
  20. Z

    1. H. Zong, G. Pilania, X. Ding, G. J. Ackland, and T. Lookman, “Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning,” npj Computational Materials, vol. 4, no. 1, Art. no. 1, 2018.
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