Publications 2017

  1. A

    1. SS. Agada, S. Geiger, A. ElSheikh, and S. Oladyshkin, “Data-driven surrogates for rapid simulation and optimization of WAG injection in fractured carbonate reservoirs,” Petroleum Geoscience, vol. 23, pp. 270--283, 2017, doi: 10.1144/petgeo2016-068.
    2. A. Alla, A. Schmidt, and B. Haasdonk, Model Order Reduction Approaches for Infinite Horizon Optimal Control Problems via the HJB Equation. Springer International Publishing, 2017.
  2. B

    1. A. Barth and A. Stein, “Approximation and simulation of infinite-dimensional Levy processes,” Stochastic and Partial Differential Equations, 2017, doi: 10.1007/s40072-017-0109-2.
    2. A. Barth and F. G. Fuchs, “Uncertainty quantification for linear hyperbolic equations  with stochastic process or random field coefficients,” Appl. Numer. Math., vol. 121, pp. 38--51, 2017, doi: 10.1016/j.apnum.2017.06.009.
    3. B. Baumann, D. Hamann, and P. Eberhard, “Time-dependent Parametric Model Order Reduction for Material-Removal Simulations,” Modeling, Simulation and Applications, vol. 17, 2017, doi: 10.1007/978-3-319-58786-8_30.
    4. P. U. Baur, “Comparison of methods for parametric model order reduction of instationary problem,” Chapter in P. Benner, A. Cohen, M. Ohlberger, K. Willcox (Eds.): Model Reduction and Approximation: Theory and Algorithms, pp. 377--407, 2017, doi: 10.1137/1.9781611974829.ch9.
    5. A. Bayer, S. Schmitt, M. Günther, and D. Haeufle, “The influence of biophysical muscle properties on simulating fast human arm movements,” Computer methods in biomechanics and biomedical engineering, vol. 20, no. 8, Art. no. 8, 2017.
    6. B. Becker, B. Guo, K. Bandilla, M. A. Celia, B. Flemisch, and R. Helmig, “A Pseudo-Vertical Equilibrium Model for Slow Gravity Drainage Dynamics,” Water Resources Research, vol. 53, no. 12, Art. no. 12, 2017, doi: 10.1002/2017WR021644.
    7. J. Behler, “First principles neural network potentials for reactive simulations of large molecular and condensed systems,” Angewandte Chemie International Edition, vol. 56, no. 42, Art. no. 42, 2017.
    8. C. Bradley et al., “Towards realistic HPC models of the neuromuscular system,” Frontiers in Physiology, 2017, [Online]. Available:
    9. M. Brehler, M. Schirwon, D. Göddeke, and P. M. Krummrich, “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, vol. 35, pp. 3622--3628, 2017, doi: 10.1109/JLT.2017.2715358.
    10. V. Bruder, S. Frey, and T. Ertl, “Prediction-Based Load Balancing and Resolution Tuning for Interactive Volume Raycasting,” Visual Informatics, 2017, doi: 10.1016/j.visinf.2017.09.001.
    11. F. D. Brunner, W. P. M. H. Heemels, and F. Allgöwer, “Robust Event-triggered MPC With Guaranteed Asymptotic Bound and Average Sampling Rate,” IEEE Transactions on Automatic Control, 2017, doi: 10.1109/TAC.2017.2702646.
    12. 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, [Online]. Available:
    13. M. Bußler et al., “Visualization of fracture progression in peridynamics,” Computers & Graphics, vol. 67, pp. 45--57, 2017, doi: 10.1016/j.cag.2017.05.003.
    14. L. Böger, M.-A. Keip, and C. Miehe, “Minimization and Saddle-Point Principles for the Phase-Field Modeling of Fracture in Hydrogels,” Computational Materials Science, vol. 138, pp. 474--485, 2017, doi: 10.1016/j.commatsci.2017.06.010.
    15. R. Bürger and I. Kröker, “Hybrid Stochastic Galerkin Finite Volumes for the Diffusively Corrected Lighthill-Whitham-Richards Traffic Model,” Springer Proceedings in Mathematics & Statistics, vol. 200, pp. 189--197, 2017, doi: 10.1007/978-3-319-57394-6_21.
  3. C

    1. B. W. Carabelli, R. Blind, F. Dürr, and K. Rothermel, “State-dependent priority scheduling for networked control systems,” Proceedings of the American Control Conference (ACC), pp. 1003--1010, 2017, doi: 10.23919/ACC.2017.7963084.
    2. C. Chalons, C. Rohde, and M. Wiebe, “A Finite Volume Method for Undercompressive Shock Waves in Two Space Dimensions,” ESAIM Math. Model. Numer. Anal., 2017, [Online]. Available:
    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, [Online]. Available:
    4. B. Christ et al., “Computational Modeling in Liver Surgery,” Frontiers in Physiology, vol. 8, p. 906, 2017, doi: 10.3389/fphys.2017.00906.
    5. S. Copplestone, P. Ortwein, and C.-D. Munz, “Complex-Frequency Shifted PMLs for Maxwell"s Equations With Hyperbolic Divergence Cleaning and Their Application in Particle-in-Cell Codes,” IEEE Transactions on Plasma Science, vol. 45, pp. 2--14, 2017, doi: 10.1109/TPS.2016.2637061.
  4. D

    1. L. Danish, D. Stöhr, P. Scheurich, and N. Pollak, “TRAIL-R3/R4 and Inhibition of TRAIL Signalling in Cancer,” in TRAIL, Fas Ligand, TNF and TLR3 in Cancer, O. Micheau, Ed. Cham: Springer International Publishing, 2017, pp. 27--57.
    2. C. Dibak, F. Dürr, and K. Rothermel, “Demo: Server-assisted interactive mobile simulations for pervasive applications,” Proceesings of the 15th IEEE International Conference on Pervasive Computing and Communications Workshops, 2017, doi: 10.1109/PERCOMW.2017.7917525.
    3. C. Dibak, A. Schmidt, F. Dürr, B. Haasdonk, and K. Rothermel, “Server-Assisted Interactive Mobile Simulations for Pervasive Applications,” Proceesings of the 15th IEEE International Conference on Pervasive Computing and Communications, 2017, doi: 10.1109/PERCOM.2017.7917857.
    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, [Online]. Available:
    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, [Online]. Available:
  5. E

    1. H. Ebel, E. Sharafian Ardakani, and P. Eberhard, “Distributed Model Predictive Formation Control with Discretization-Free Path Planning for Transporting a Load. Robotics and Autonomous Systems,” Robotics and Autonomous Systems, vol. 96, pp. 211--223, 2017, doi: 10.1016/j.robot.2017.07.007.
    2. 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, [Online]. Available:
    3. W. Ehlers and C. Luo, “A phase-field approach embedded in the Theory of Porous Media for the description of dynamic hydraulic fracturing,” Computer Methods in Applied Mechanics and Engineering, vol. 315, pp. 348--368, 2017, doi: 10.1016/j.cma.2016.10.045.
    4. M. P. Englert, “Learning Manipulation Skills from a Single Demonstration,” International Journal of Robotics Research, 2017, [Online]. Available:
  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,” Computers & Fluids, 2017, [Online]. Available:
    2. S. Fechter, C.-D. Munz, C. Rohde, and C. Zeiler, “A sharp interface method for compressible liquid-vapor flow with phase    transition and surface tension,” JOURNAL OF COMPUTATIONAL PHYSICS, vol. 336, pp. 347–374, 2017, doi: 10.1016/
    3. J. Fehr and C. Kleinbach, “Optimal Deceleration of Surrogate Models in a Generic Side Impact Setup. International Journal of Crashworthiness,” International Journal of Crashworthiness, 2017, doi: 10.1080/13588265.2017.1287525.
    4. O. Fernandes, S. Frey, and T. Ertl, “Transportation-based Visualization of Energy Conversion,” IVAPP, p. 12, 2017, [Online]. Available:
    5. M. Fetzer and C. W. Scherer, “Absolute stability analysis of discrete time feedback interconnections,” 20th IFAC World Congres, 2017, [Online]. Available:
    6. M. Fetzer and C. W. Scherer, “Full-block multipliers for repeated, slope restricted scalar nonlinearities,” Int. J. Robust Nonlin., 2017, doi: 10.1002/rnc.3751.
    7. M. Fetzer and C. W. Scherer, “Zames-Falb Multipliers for Invariance,” tIEEE Control Systems Letters, vol. 99, 2017, doi: 10.1109/LCSYS.2017.2718556.
    8. M. Fetzer, C. W. Scherer, and J. Veenman, “Invariance with Dynamic Multipliers,” IEE T. Automat. Contr., 2017, doi: 10.1109/TAC.2017.2762764.
    9. T. Fetzer, C. Grüninger, B. Flemisch, and R. Helmig, “On the Conditions for Coupling Free Flow and Porous-Medium Flow in a Finite Volume Framework,” Finite Volumes for Complex Applications VIII, pp. 347--356, 2017, doi: 10.1007/978-3-319-57394-6_37.
    10. T. Fetzer, J. Vanderborght, K. Mosthaf, K. M. Smits, and R. Helmig, “Heat and water transport in soils and across the soil-atmosphere interface: 2. Numerical analysis,” WATER RESOURCES RESEARCH, vol. 53, no. 2, Art. no. 2, 2017, doi: 10.1002/2016WR019983.
    11. S. Fischer and I. Steinwart, Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm. 2017.
    12. S. Frey, “Sampling and Estimation of Pairwise Similarity in Spatio-Temporal Data Based on Neural Networks,” Informatics, 2017, doi: 10.3390/informatics4030027.
    13. S. Frey and T. Ertl, “Fast Flow-based Distance Quantification and Interpolation for High-Resolution Density Distributions,” EuroGraphics 2017, Short Paper, 2017, doi: 10.2312/egsh.20171009.
    14. S. Frey and T. Ertl, “Progressive Direct Volume-to-Volume Transformation,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, pp. 921--930, 2017, doi: 10.1109/TVCG.2016.2599042.
    15. F. Fritzen and M. Hassani, “Space-time model order reduction for nonlinear viscoelastic systems subjected to long-term loading,” Meccanica, vol. 52, no. 276, Art. no. 276, 2017, doi: 10.1007/s11012-017-0734-x.
  7. G

    1. A. Gholami, A. Mang, K. Scheufele, C. Davatzikos, M. Mehl, and G. Biros, “A Framework for Scalable Biophysics-based Image Analysis,” Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis SC17, pp. 19:1--19:13, 2017, doi: 10.1145/3126908.3126930.
    2. D. Gläser, R. Helmig, B. Flemisch, and H. Class, “A discrete fracture model for two-phase flow in fractured porous media,” Advances in Water Resources, vol. 110, pp. 335--348, 2017, doi: 10.1016/j.advwatres.2017.10.031.
    3. G. Goebel and F. Allgöwer, “Semi-explicit MPC based on subspace clustering,” Automatica, vol. 83, pp. 309--316, 2017, doi: 10.1016/j.automatica.2017.06.036.
    4. G. Goebel and F. Allgöwer, “New results on semi-explicit and almost explicit MPC algorithms,” at-Automatisierungstechnik, vol. 65, pp. 245--259, 2017, doi: 10.1515/auto-2017-0006.
    5. M. Greis, H. Schuff, M. Kleiner, N. Henze, and A. Schmidt, Input Controls for Entering Uncertain Data: Probability Distribution Sliders. 2017.
    6. A. Guthke, “Defensible Model Complexity: A Call for Data-Based and Goal-Oriented Model Choice,” Groundwater, vol. 55, pp. 646--650, 2017, doi: 10.1111/gwat.12554.
    7. F. S. Göküzüm and M.-A. Keip, “An Algorithmically Consistent Macroscopic Tangent Operator for FFT-based Computational Homogenization,” International Journal for Numerical Methods in Engineering, 2017, doi: 10.1002/nme.5627.
  8. H

    1. J. Haas et al., “Challenges and trends of energy storage expansion planning for flexibility provision in power systems - a review,” Renewable and Sustainable Energy Reviews, vol. 80, pp. 603–619, 2017, doi: 10.1016/j.rser.2017.05.201.
    2. B. Haasdonk and G. Santin, “Greedy Kernel Approximation for Sparse Surrogate Modelling,” Reduced-Order Modeling (ROM) for Simulation and Oprimization, 2017, [Online]. Available:
    3. M. Hahn, U. Breitenbücher, O. Kopp, and F. Leymann, “Modeling and execution of data-aware choreographies: an overview,” Computer Science - Research and Development, 2017, doi: 10.1007/s00450-017-0387-y.
    4. M. Hahn, U. Breitenbücher, F. Leymann, and A. Weiß, “TraDE - A Transparent Data Exchange Middleware for Service Choreographies,” Lecture Notes in Computer Science (LNCS), vol. 10573, pp. 252--270, 2017, doi: 10.1007/978-3-319-69462-7_16.
    5. W. Halter, J. M. Montenbruck, and F. Allgöwer, “Systems with integral resource consumption,” Proc. 56th IEEE Conf. Decision and Control (CDC), pp. 2667--2673, 2017, doi: 10.1109/CDC.2017.8264046.
    6. D. Hamann, N.-P. Walz, A. Fischer, M. Hanss, and P. Eberhard, “Fuzzy arithmetical stability analysis of uncertain machining systems,” Mechanical Systems and Signal Processing, vol. 98, no. 1, Art. no. 1, 2017, doi: 10.1016/j.ymssp.2017.05.012.
    7. H. Hang and I. Steinwart, A Bernstein-type Inequality for Some Mixing Processes and Dynamical Systems with an Application to Learning. 2017.
    8. F. Hempert et al., “Simulation of real gas effects in supersonic methane jets using a tabulated equation of state with a discontinuous Galerkin spectral element method,” Computers & Fluids, vol. 145, pp. 167--179, 2017, [Online]. Available:
    9. M. Herschel, R. Diestelkämper, and H. Ben Lahmar, “A survey on provenance: What for? What form? What from?,” International Journal on Very Large Data Bases, vol. 26, no. 6, Art. no. 6, 2017, doi: 10.1007/s00778-017-0486-1.
    10. A. Hessenthaler, O. Röhrle, and D. Nordsletten, “Validation of a non-conforming monolithic fluid-structure interaction method using phase-contrast MRI,” International Journal for Numerical Methods in Biomedical Engineering, vol. 33, 2017, doi: 10.1002/cnm.2845.
    11. A. Hessenthaler, N. Gaddum, O. Holub, R. Sinkus, O. Röhrle, and D. Nordsletten, “Experiment for validation of fluid-structure interaction models and algorithms,” International Journal for Numerical Methods in Biomedical Engineering, vol. 33, 2017, doi: 10.1002/cnm.2848.
    12. M. Hintermüller, C. N. Rautenberg, T. Wu, and A. Langer, “Optimal Selection of the Regularization Function in a Weighted Total Variation Model. Part II: Algorithm, Its Analysis and Numerical Tests,” Journal of Mathematical Imaging and Vision, vol. 59, pp. 515--533, 2017, doi: 10.1007/s10851-017-0736-2.
    13. S. Hirschmann, M. Brunn, M. Lahnert, M. Mehl, C. W. Glass, and D. Pflüger, “Load balancing with p4est for Short-Range Molecular Dynamics with ESPResSo,” Advances in Parallel Computing, vol. 32, pp. 455--464, 2017, doi: 10.3233/978-1-61499-843-3-455.
    14. S. Hocker, D. Rapp, and S. Schmauder, “Molecular dynamics simulations of strengthening due to silver precipitates in copper matrix,” physica status solidi (b), 2017, doi: 10.1002/pssb.201600479.
    15. A. Hofmann and M. Hanss, “Fuzzy arithmetical controller design for active road vehicle suspension in the presence of uncertainties,” 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 582--587, 2017, doi: 10.1109/MMAR.2017.8046893.
  9. J

    1. A. Jensch, C. Thomaseth, and N. Radde, “Sampling-based Bayesian approaches reveal the importance of quasi-bistable behavior in cellular decision processes on the example of the MAPK signaling pathway in PC-12 cell lines,” BMC Systems Biology, vol. 11:11, 2017, [Online]. Available:
  10. K

    1. G. Karch et al., “Visual analysis of inclusion dynamics in two-phase flow,” IEEE Transactions on Visualization and Computer Graphics, 2017, doi: 10.1109/TVCG.2017.2692781.
    2. M.-A. Keip and O. Nadgir, “An electro-elastic phase-field model for nematic liquid crystal elastomers based on Landau-de-Gennes theory,” GAMM-Mitteilungen, vol. 40, pp. 102--124, 2017, doi: 10.1002/gamm.201720003.
    3. M.-A. Keip and M. Rambausek, “Computational and analytical investigations of shape effects in the experimental characterization of magnetorheological elastomers,” International Journal of Solids and Structures, 2017, doi: 10.1016/j.ijsolstr.2017.04.012.
    4. C. Kleinbach, O. Martynenko, J. Promies, D. F. B. Haeufle, J. Fehr, and S. Schmitt, “Implementation and validation of the extended Hill-type muscle model with robust routing capabilities in LS-DYNA for active human body models,” BioMedical Engineering OnLine, vol. 16:109, p. 28, 2017, doi: 10.1186/s12938-017-0399-7.
    5. T. Koeppl, M. Fedoseyev, and R. Helmig, Simulation of surge reduction systems using dimensionally reduced models. 2017.
    6. B. Kolb, L. C. Lentz, and A. M. Kolpak, “Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods,” Scientific reports, vol. 7, no. 1, Art. no. 1, 2017.
    7. M. Koy et al., “High Oxidation State Molybdenum N-Heterocyclic Carbene Alkylidyne Complexes: Synthesis, Mechanistic Studies, and Reactivity,” Chemistry – A European Journal, vol. 23, no. 61, Art. no. 61, 2017, doi: 10.1002/chem.201703313.
    8. K. Kuritz, D. Stöhr, N. Pollak, and F. Allgöwer, “On the relationship between cell cycle analysis with ergodic principles and age-structured cell population models,” Journal of Theoretical Biology, vol. 414, pp. 91--102, 2017, doi: 10.1016/j.jtbi.2016.11.024.
    9. J. Köhler, M. A. Müller, N. Li, and F. Allgöwer, “Real Time Economic Dispatch for Power Networks: A Distributed Economic Model Predictive Control Approach,” Proceedings of 56th Annual Conference on Decision and Control (CDC), pp. 6340--6345, 2017, doi: 10.1109/CDC.2017.8264615.
    10. P. N. Köhler, M. A. Müller, J. Pannek, and F. Allgöwer, “On Exploitation of Supply Chain Properties by Sequential Distributed MPC,” Proceedings of the 20th IFAC World Congress, vol. 50, no. 1, Art. no. 1, 2017, doi: 10.1016/j.ifacol.2017.08.706.
    11. M. Köppel, I. Kröker, and C. Rohde, “Intrusive Uncertainty Quantification for Hyperbolic-Elliptic Systems  Governing Two-Phase Flow in Heterogeneous Porous Media,” Computational Geosciences, vol. 21, no. 4, Art. no. 4, 2017, doi: 10.1007/s10596-017-9662-z.
    12. M. Köppel et al., Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario. 2017.
  11. L

    1. A. Langer, “Automated Parameter Selection in the $L^1$-$L^2$-$TV$ Model for Removing Gaussian Plus Impulse Noise,” Inverse Problems, vol. 33, 2017, doi: 10.1088/1361-6420/33/7/074002.
    2. A. Langer, “Automated Parameter Selection for Total Variation Minimization in Image Restoration,” Journal of Mathematical Imaging and Vision, vol. 57, pp. 239--268, 2017, doi: 10.1007/s10851-016-0676-2.
    3. M. Leuschner and F. Fritzen, “Reduced order homogenization for viscoplastic composite materials including dissipative imperfect interfaces,” Mechanics of Materials, vol. 104, pp. 121--138, 2017, doi: 10.1016/j.mechmat.2016.10.008.
    4. S. Linsenmayer, D. V. Dimarogonas, and F. Allgöwer, “Event-Based Vehicle Coordination Using Nonlinear Unidirectional Controllers,” IEEE Transactions on Control of Network Systems, 2017, doi: 10.1109/TCNS.2017.2733959.
    5. S. Linsenmayer, R. Blind, and F. Allgöwer, “Delay-dependent data rate bounds for containability of scalar systems,” Proceedings of the 20th IFAC World Congress, pp. 7875--7880, 2017, doi: 10.1016/j.ifacol.2017.08.742.
    6. S. Linsenmayer and F. Allgöwer, “Stabilization of Networked Control Systems with weakly hard real-time dropout description,” Proceedings of the 56th IEEE Conference on Decision and Control (CDC), pp. 4765--4770, 2017, doi: 10.1109/CDC.2017.8264364.
    7. M. Lorenzen, M. A. Müller, and F. Allgöwer, “Stabilizing Stochastic MPC without Terminal Constraints,” Proceedings of the American Control Conference, pp. 5636--5641, 2017, doi: 10.23919/ACC.2017.7963832.
    8. M. Lorenzen, F. Dabbene, R. Tempo, and F. Allgöwer, “Stochastic MPC with offline uncertainty sampling,” Automatica, vol. 81, pp. 176--183, 2017, doi: 10.1016/j.automatica.2017.03.031.
    9. M. Lorenzen, F. Dabbene, R. Tempo, and F. Allgöwer, “Constraint-Tightening and Stability in Stochastic Model Predictive Control,” IEEE Transactions on Automatic Control, vol. 62, pp. 3165--3177, 2017, doi: 10.1109/TAC.2016.2625048.
    10. M. Lorenzen, F. Allgöwer, and M. Cannon, “Adaptive Model Predictive Control with Robust Constraint Satisfaction,” Proceedings of the IFAC World Congress, vol. 50, no. 1, Art. no. 1, 2017, doi: 10.1016/j.ifacol.2017.08.512.
    11. M. Lorenzen, M. A. Müller, and F. Allgöwer, “Stochastic Model Predictive Control without Terminal Constraints,” International Journal of Robust and Nonlinear Control, 2017, doi: 10.1002/rnc.3912.
  12. M

    1. D. Markthaler, J. Zeman, J. Baz, J. Smiatek, and N. Hansen, “Validation of Trimethylamine-N-Oxide (TMAO) Force Fields Based on Thermophysical Properties of Aqueous TMAO Solutions,” The Journal of Physical Chemistry B, 2017, doi: 10.1021/acs.jpcb.7b07774.
    2. D. Markthaler, J. Gebhardt, S. Jakobtorweihen, and N. Hansen, “Molecular Simulations of Thermodynamic Properties for the System alpha-Cyclodextrin/Alcohol in Aqueous Solution,” Chemie Ingenieur Technik, 2017, doi: 10.1002/cite.201700057.
    3. J. Mehne and W. Nowak, “Improving temperature predictions for Li-ion batteries: data assimilation with a stochastic extension of a physically-based, thermo-electrochemical model,” Journal of Energy Storage, vol. 12, pp. 288--296, 2017, doi: 10.1016/j.est.2017.05.013.
    4. J. Meisner, T. Lamberts, and J. Kästner, “Atom Tunneling in the Water Formation Reaction H2 + OH -> H2O + H on an Ice Surface,” ACS Earth and Space Chemistry, vol. 1, no. 7, Art. no. 7, 2017, doi: 10.1021/acsearthspacechem.7b00052.
    5. J. Meisner, M. N. Markmeyer, M. U. Bohner, and J. Kästner, “Comparison of classical reaction paths and tunneling paths studied with the semiclassical instanton theory,” Phys. Chem. Chem. Phys., vol. 19, pp. 23085--23094, 2017, doi: 10.1039/C7CP03722H.
    6. F. Meyer, J. Giesselmann, and C. Rohde, A posteriori error analysis for random scalar conservation laws using the Stochastic Galerkin method. 2017.
    7. J. M. Montenbruck, D. Zelazo, and F. Allgöwer, “Fekete Points, Formation Control, and the Balancing Problem,” IEEE Trans. Automat. Control, vol. 62, pp. 5069--5081, 2017, doi: 10.1109/TAC.2017.2679073.
    8. J. M. Montenbruck and F. Allgöwer, “Separable matrices and minimum complexity controllers,” Proc. 56th IEEE Conf. Decision and Control (CDC), pp. 4187--4192, 2017, doi: 10.1109/CDC.2017.8264275.
    9. J. M. Montenbruck and F. Allgöwer, “An Input-Output Framework for Submanifold Stabilization,” IEEE Trans. Automat. Control, vol. 62, pp. 5170--5184, 2017, doi: 10.1109/TAC.2017.2679480.
    10. M. Mordhorst, T. Strecker, D. Wirtz, T. Heidlauf, and O. Röhrle, “POD-DEIM reduction of computational EMG models,” Journal of Computational Science, vol. 19, pp. 86--96, 2017, doi: 10.1016/j.jocs.2017.01.009.
    11. M. A. Müller, Additional material to the paper “Nonlinear moving horizon estimation in the presence of bounded disturbances.” 2017.
  13. N

    1. S. M. Najmabadi et al., “Analyzing the Effect and Performance of Lossy Compression on Aeroacoustic Simulation of Gas Injector,” Computation, vol. 5, 2017, doi: 10.3390/computation5020024.
    2. A. Namhata, L. Zhang, R. M. Dilmore, S. Oladyshkin, and D. V. Nakles, “Modeling Pressure Changes due to Migration of Fluids into the Above Zone Monitoring interval of a Geologic Carbon Storage Site,” International Journal of Greenhouse Gas Control, vol. 56, pp. 30--42, 2017, doi: 10.1016/j.ijggc.2016.11.012.
    3. N. Neupert, H. Gomaa, F. Joos, and B. Weigand, “Investigation and modeling of two phase flow through a compressor stage: Analysis of film breakup,” European Journal of Mechanics-B/Fluids, vol. 61, pp. 279--288, 2017.
  14. P

    1. D. Pedroso, Y. Zhang, and W. Ehlers, “Solution of Liquid-Gas-Solid Coupled Equations for Porous Media Considering Dynamics and Hysteretic Retention Behavior,” Journal of Engineering Mechanics, vol. 143, p. 04017021, 2017, doi: 10.1061/(ASCE)EM.1943-7889.0001208.
  15. R

    1. A. Romer, J. M. Montenbruck, and F. Allgöwer, “Sampling strategies for data-driven inference of passivity properties,” Proc. 56th IEEE Conf. Decision and Control (CDC), pp. 6389--6394, 2017, doi: 10.1109/CDC.2017.8264623.
    2. A. Romer, J. M. Montenbruck, and F. Allgöwer, “Determining dissipation inequalities from input-output samples,” Proc. 20th IFAC World Congress, pp. 7789--7794, 2017, doi: 10.1016/j.ifacol.2017.08.1053.
    3. A. Romer, J. M. Montenbruck, and F. Allgöwer, “Sampling strategies for data-driven inference of passivity properties,” in 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017, pp. 6389--6394.
    4. A. Romer, J. M. Montenbruck, and F. Allgöwer, “Determining dissipation inequalities from input-output samples,” IFAC-PapersOnLine, vol. 50, no. 1, Art. no. 1, 2017.
    5. C. A. Rösinger and C. W. Scherer, “Structured Controller Design With Applications to Networked Systems,” 2017, doi: 10.1109/CDC.2017.8264365.
  16. S

    1. O. Sander, T. Koch, N. Schröder, and B. Flemisch, “The Dune FoamGrid implementation for surface and network grids,” Archive of Numerical Software, vol. 5, no. 1, Art. no. 1, 2017, doi: 10.11588/ans.2017.1.28490.
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