1. 2019

    1. P. Buchfink, A. Bhatt, and B. Haasdonk, “Symplectic Model Order Reduction with Non-Orthonormal Bases,” 2019.
    2. D. Grunert, J. Fehr, and B. Haasdonk, “Well-scaled, A-posteriori Error Estimation for Model Order Reduction of Large Second-order Mechanical Systems,” International Journal for Numerical Methods in Engineering, 2019.
    3. T. Reichenbach, P. Tempel, A. Verl, and A. Pott, “Static Analysis of a Two-Platform Planar Cable-Driven Parallel Robot with Unlimited Rotation,” in Proceedings of the Fourth International Conference on Cable-Driven Parallel Robots, 2019.
    4. G. Santin and B. Haasdonk, “Kernel Methods for Surrogate Modeling,” 2019.
    5. P. Tempel, D. Lee, F. Trautwein, and A. Pott, “Modeling of Elastic-Flexible Cables with Time-Varying Length for Cable-Driven Parallel Robotsj,” in Proceedings of the Fourth International Conference on Cable-Driven Parallel Robots, 2019.
  2. 2018

    1. A. Bhatt, J. Fehr, and B. Haasdonk, “Model order reduction of an elastic body under large rigid motion,” Proceedings of ENUMATH 2017, 2018.
    2. C. Bradley et al., Towards realistic HPC models of the neuromuscular system. 2018.
    3. K. Carlberg, L. Brencher, B. Haasdonk, and A. Barth, “Data-driven time parallelism via forecasting,” SIAM J. of Sci. Comp., 2018.
    4. 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.
    5. V. Ferrario, N. Hansen, and J. Pleiss, “Interpretation of cytochrome P450 monooxygenase kinetics by modeling of thermodynamic activity,” J Inorg Biochem, 2018.
    6. C. Y. Guo, “Robust Gain-Scheduled Controller Design with a Hierarchical Structure,” 9th IFAC Symposium on Robust Control Design, 2018.
    7. S. Haesaert, S. Weiland, and C. W. Scherer, A separation theorem for guaranteed $H_2$ performance through matrix inequalities. Automatica, 2018.
    8. T. Holicki and C. W. Scherer, “A Swapping Lemma for Switched Systems,” 9th IFAC Symposium on Robust Control Design, 2018.
    9. 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.
    10. 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.
    11. T. Kuhn, J. Dürrwächter, F. Meyer, A. Beck, C. Rohde, and C.-D. Munz, Uncertainty Quantification for Direct Aeroacoustic Simulations of Cavity Flows. 2018.
    12. J. Köhler, M. A. Müller, and F. Allgöwer, A nonlinear tracking model predictive control scheme using reference generic terminal ingredients. 2018.
    13. 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.
    14. M. Köppel, V. Martin, and J. E. Roberts, A stabilized Lagrange multiplier finite-element method for flow in porous media with fractures. 2018.
    15. M. Lotti, J. Pleiss, F. Valero, and P. Ferrer, “Enzymatic production of biodiesel: strategies to overcome methanol inactivation,” Biotechnol J, 2018.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. A. Schmidt and B. Haasdonk, “Data-driven surrogates of value functions and applications to feedback control for dynamical systems,” MathMod 2018, 2018.
    21. 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.
    22. 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.
    23. 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.
    24. J. Valentin and D. Pflüger, “Fundamental Splines on Sparse Grids and Their Application to Gradient-Based Optimization,” Sparse Grids and Applications - Miami 2016, 2018.
    25. D. Wittwar and B. Haasdonk, Greedy Algorithms for Matrix-Valued Kernels. 2018.
  3. 2017

    1. C. Bradley et al., “Towards realistic HPC models of the neuromuscular system,” Frontiers in Physiology, 2017.
    2. T. Brünnette, G. Santin, and B. Haasdonk, “Greedy kernel methods for accelerating implicit integrators for parametric ODEs,” Numerical Mathematics and Advanced Applications - ENUMATH 2017, 2017.
    3. C. Chalons, J. Magiera, C. Rohde, and M. Wiebe, “A Finite-Volume Tracking Scheme for Two-Phase Compressible Flow,” Springer Proc. Math. Stat., 2017.
    4. W.-P. Düll, B. Hilder, and G. Schneider, “Analysis of the embedded cell method in 1D for the numerical homogenization of metal-ceramic composite materials.,” J. Appl. Anal., 2017.
    5. W.-P. Düll, B. Hilder, and G. Schneider, “Analysis of the embedded cell method in 2D for the numerical homogenization of metal-ceramic composite materials.,” European J. Appl. Math., 2017.
    6. H. Ebel, E. Sharafian Ardakani, and P. Eberhard, “Comparison of Distributed Model Predictive Control Approaches for Transporting a Load by a Formation of Mobile Robots,” Proceedings of the 8th ECCOMAS Thematic Conference on Multibody Dynamics, 2017.
    7. M. P. Englert, “Learning Manipulation Skills from a Single Demonstration,” International Journal of Robotics Research, 2017.
    8. O. Fernandes, S. Frey, and T. Ertl, “Transportation-based Visualization of Energy Conversion,” IVAPP, p. 12, 2017.
    9. S. Fischer and I. Steinwart, Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm. 2017.
    10. B. Haasdonk and G. Santin, “Greedy Kernel Approximation for Sparse Surrogate Modelling,” Reduced-Order Modeling (ROM) for Simulation and Oprimization, 2017.
    11. H. Hang and I. Steinwart, A Bernstein-type Inequality for Some Mixing Processes and Dynamical Systems with an Application to Learning. 2017.
    12. T. Koeppl, M. Fedoseyev, and R. Helmig, Simulation of surge reduction systems using dimensionally reduced models. 2017.
    13. M. Köppel et al., Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario. 2017.
    14. F. Meyer, J. Giesselmann, and C. Rohde, A posteriori error analysis for random scalar conservation laws using the Stochastic Galerkin method. 2017.
    15. M. A. Müller, Additional material to the paper “Nonlinear moving horizon estimation in the presence of bounded disturbances.” 2017.
    16. K. Scharnowski, S. Frey, B. Raffin, and T. Ertl, “Spline-based Decomposition of Streamed Particle Trajectories for Efficient Transfer and Analysis Conversion,” EuroVis 2017, Short Paper, 2017.
    17. I. Steinwart, B. Sriperumbudur, and P. Thomann, Adaptive Clustering Using Kernel Density Estimators. 2017.
    18. I. Steinwart and P. Thomann, liquidSVM: A Fast and Versatile SVM package. 2017.
    19. I. Steinwart and J. Ziegel, Strictly proper kernel scores and characteristic kernels on compact spaces. 2017.
    20. P. Thomann, I. Steinwart, I. Blaschzyk, and M. Meister, Spatial Decompositions for Large Scale SVMs. 2017.
    21. D. Wittwar, A. Schmidt, and B. Haasdonk, “Reduced Basis Approximation for the Discrete-time Parametric Algebraic Riccati Equation,” SIAM Journal on Matrix Analysis and Applications, 2017.
    22. J. Zeman, F. Uhlig, J. Smiatek, and C. Holm, “A coarse-grained polarizable force field for the ionic liquid 1-butyl-3-methylimidazolium hexafluorophosphate,” Journal of Physics: Condensed Matter, 2017.
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