Publications 2013

  1. A

    1. A. Abdulle, A. Barth, and C. Schwab, “Multilevel Monte Carlo methods for stochastic elliptic multiscale PDEs,” Multiscale Model. Simul., vol. 11, pp. 1033--1070, 2013, doi: 10.1137/120894725.
    2. F. Alt, B. Pfleging, and A. Schmidt, “Sonify - A Platform for the Sonification of Text Messages,” Mensch & Computer 2013 - Tagungsband, pp. 149--158, 2013, doi: 10.1524/9783486781229.149.
    3. F. Alt, S. Schneegass, M. Girgis, and A. Schmidt, “Cognitive Effects of Interactive Public Display Applications,” Proceedings of the 2Nd ACM International Symposium on Pervasive Displays, 2013, doi: 10.1145/2491568.2491572.
    4. V. Andrikopoulos, S. Gómez Saez, D. Karastoyanova, and A. Weiß, “Towards Collaborative, Dynamic & Complex Systems,” Proceedings of the 6th International Conference on Service-Oriented Computing and Applications, pp. 241--245, 2013, doi: 10.1109/SOCA.2013.35.
    5. A. Arnold, K. Breitsprecher, F. Fahrenberger, S. Kesselheim, O. Lenz, and C. Holm, “Efficient Algorithms for Electrostatic Interactions Including Dielectric Contrasts,” Entropy, vol. 15, no. 11, Art. no. 11, 2013, doi: 10.3390/e15114569.
    6. A. Arnold et al., “Comparison of scalable fast methods for long-range interactions,” Physical Review E, vol. 88, no. 6, Art. no. 6, 2013, doi: 10.1103/PhysRevE.88.063308.
    7. A. Arnold et al., Espresso 3.1: Molecular dynamics software for coarse-grained models. Springer, 2013. doi: 10.1007/978-3-642-32979-1_1.
    8. M. Ashraf, S. Oladyshkin, and W. Nowak, “Geological storage of CO2: global sensitivity analysis and risk assessment using arbitrary polynomial chaos expansion,” International Journal of Greenhouse Gas Control, vol. 19, pp. 704--719, 2013, doi: 10.1016/j.ijggc.2013.03.023.

  2. B

    1. P. Baier, F. Dürr, and K. Rothermel, “Opportunistic Position Update Protocols for Mobile Devices,” Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013), 2013, doi: 10.1145/2493432.2493439.
    2. P. Baier, F. Dürr, and K. Rothermel, “Efficient Distribution of Sensing Queries in Public Sensing Systems,” Proceedings of the 10th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS 2013), 2013, doi: 10.1109/MASS.2013.11.
    3. F. Bayer, M. Bürger, and F. Allgöwer, “Discrete-time Incremental ISS: A Framework for Robust NMPC,” Proceedings of the 12th IEEE Euorpean Control Conference, pp. 2068--2073, 2013, [Online]. Available: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6669322
    4. F. Bayer, G. Notarstefano, and F. Allgöwer, “A Projected SQP Method for Nonlinear Optimal Control with Quadratic Convergence,” Proceedings of the 52nd IEEE Conference on Decision and Control, pp. 6463--6468, 2013, doi: 10.1109/CDC.2013.6760912.
    5. SP. Benson and J. Pleiss, “Incomplete mixing versus clathrate-like structures: a molecular view on hydrophobicity in methanol-water mixtures,” J Mol Model, vol. 19, pp. 3427--3436, 2013, doi: 10.1007/s00894-013-1857-1.
    6. S. Bidier and W. Ehlers, “Particle simulation of granular media and homogenisation towards continuum quantities,” Proceedings in Applied Mathematics and Mechanics, vol. 13, pp. 575--576, 2013, doi: 10.1002/pamm.201310269.
    7. W. Blajer, R. Seifried, and K. Ko?odziejczyk, “Diversity of Servo-Constraint Problems for Underactuated Mechanical Systems: A Case Study Illustration,” Solid State Phenomena, vol. 198, pp. 473--482, 2013, doi: 10.4028/www.scientific.net/SSP.198.473.
    8. C. Breindl, M. Chaves, and F. Allgöwer, “A linear reformulation of Boolean optimization problems and structure identi?cation of gene regulation networks,” Proceedings of the 52nd IEEE Conference on Decision and Control, pp. 733--738, 2013, doi: 10.1109/CDC.2013.6759969.
    9. F. D. Brunner, M. Lazar, and F. Allgöwer, “An Explicit Solution to Constrained Stabilization via Polytopic Tubes,” 52nd IEEE Conference on Decision and Control, pp. 7721--7727, 2013, doi: 10.1109/CDC.2013.6761115.
    10. M. Bürger, D. Zelazo, and F. Allgöwer, “Hierarchical Clustering of Dynamical Networks Using a Saddle-Point Analysis,” IEEE Transactions on Automatic Control, vol. 58, pp. 113--124, 2013, doi: 10.1109/TAC.2012.2206695.
    11. R. Bürger, I. Kröker, and C. Rohde, “A hybrid stochastic Galerkin method for uncertainty quantification applied to a conservation law modelling a clari,” Zamm, 2013, doi: 10.1002/zamm.201200174.

  3. C

    1. 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.
    2. A. Chanda, A. Fischer, P. Eberhard, and S. K. Dwivedy, “Stability analysis of a thin-walled cylinder in turning operation using the semi- discretization method,” Acta Mechanica Sinica, 2013, doi: 10.1007/s10409-013-0097-z.
    3. J. Chaudenson et al., “Stability analysis of pulse-modulated systems with an application to space launchers,” 19th IFAC Symposium on Automatic Control in Aerospace, 2013, doi: 10.3182/20130902-5-DE-2040.00082.
    4. J. Chaudenson et al., “Stability analysis of pulse-modulated systems with an application to space launchers,” IFAC Proc. Vol., vol. 46, no. 19, Art. no. 19, 2013, doi: 10.3182/20130902-5-DE-2040.00082.

  4. D

    1. M. Daub, “An Appropriate Bounded Invariant Region for a Bistable Reaction-Diffusion Model of the Caspase-3/8 Feedback Loop,” Bulletin of Mathematical Biology, vol. 75, no. 11, Art. no. 11, 2013, doi: 10.1007/s11538-013-9892-8.
    2. M. Dihlmann and B. Haasdonk, “Certified PDE-constrained parameter optimization using reduced basis surrogate models for evolution problems,” Computational Optimization and Applications, 2013, doi: 10.1007/s10589-014-9697-1.
    3. M. Dihlmann and B. Haasdonk, “Certifed Nonlinear Parameter Optimization with Reduced Basis Surrogate Models,” Proc. Appl. Math. Mech., vol. 13, pp. 3--6, 2013, doi: 10.1002/pamm.201310002.
    4. Y. Dorozhko, K. Kratzer, Y. Yudin, A. Arnold, C. W. Glass, and M. Resch, Rare Event Sampling using the Science Experimental Grid Laboratory. Civil Comp Press, 2013. doi: 10.4203/ccp.102.207.
    5. M. Dumbser, U. Iben, and C.-D. Munz, “Efficient implementation of high order unstructured WENO schemes for cavitating flows,” Computers & Fluids, vol. 86, pp. 141--168, 2013, doi: 10.1016/j.compfluid.2013.07.011.
    6. H. Dürr, M. Stakonvic, K. Johansson, and C. Ebenbauer, “Lie bracket approximation of extremum seeking systems,” Automatica, vol. 49, p. 1538?1552, 2013, doi: 10.1016/j.automatica.2013.02.016.

  5. E

    1. C. Eck, M. Kutter, A.-M. Sändig, and C. Rohde, “A two scale model for liquid phase epitaxy with elasticity: An iterative procedure,” ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, vol. 93, pp. 745--761, 2013, doi: 10.1002/zamm.201200238.
    2. W. Ehlers and A. Wagner, “Multi-component Modelling of Human Brain Tissue,” Computer Methods in Biomechanics and Biomedical Engineering, 2013, doi: 10.1080/10255842.2013.853754.
    3. W. Ehlers and A. Wagner, “Constitutive and Computational Aspects in Tumour Therapies of Multiphasic Brain Tissue,” G. A. Holzapfel & E. Kuhl (eds.): Computer Models in Biomechanics, vol. 0, pp. 263--276, 2013, doi: 10.1007/978-94-007-5464-5_19.
    4. W. Ehlers, S. Zinatbakhsh, and B. Markert, “Stability analysis of finite difference schemes revisited: A study of decoupled solution strategies for coupled multi-field problems,” International Journal for Numerical Methods in Engineering, vol. 94, pp. 758--786, 2013, doi: 10.1002/nme.4480.
    5. M. Ertl, N. Roth, G. Brenn, H. Gomaa, and B. Weigand, “Simulations and Experiments on Shape Oscillations of Newtonian and Non-Newtonian Liquid Droplets,” Proceedings of the ILASS - Europe 2013, 2013, [Online]. Available: http://ilasseurope.org/events/25th-ilass-europe/

  6. F

    1. M. Falk, M. Krone, and T. Ertl, “Atomistic Visualization of Mesoscopic Whole-Cell Simulations using Ray-Casted Instancing,” Computer Graphics Forum, vol. 32, no. 8, Art. no. 8, 2013, doi: 10.1111/cgf.12197.
    2. C. Feller and C. Ebenbauer, “A barrier function based continuous-time algorithm for linear model predictive control,” Proceedings of  the 12th IEEE Euorpean Control Conference, pp. 19--26, 2013, [Online]. Available: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6669710&tag=1
    3. C. Feller and C. Ebenbauer, “Ein zeitkontinuierlicher Optimierungsalgorithmus für die modellprädiktive Regelung linearer Systeme,” Tagungsband zum 18. Steirischen Seminar ueber Regelungstechnik und Prozessautomatisierung, pp. 1--28, 2013, [Online]. Available: http://portal.tugraz.at/portal/page/portal/TU_Graz/Einrichtungen/Institute/Homepages/i4430/Veranstaltungen/Retzhof
    4. A. Fischer, P. Eberhard, and J. Ambr?sio, “Parametric flexible multibody model for material removal during turning,” Journal of Computational and Nonlinear Dynamics, vol. 9, 2013, doi: 10.1115/1.4025283.
    5. C. Fischer, K.-P. Fritz, P. Eberhard, and H. Kück, “Investigation and design of an impact actuated micro shift valve,” Archive of Applied Mechanics, vol. 83, pp. 1171--1192, 2013, doi: 10.1007/s00419-013-0741-x.
    6. S. Frey, F. Sadlo, and T. Ertl, “Explorable Volumetric Depth Images from Raycasting,” SIBGRAPI 13 Proceedings of the 2013 XXVI Conference on Graphics, Patterns and Images, pp. 123--130, 2013, doi: 10.1109/SIBGRAPI.2013.26.
    7. S. Frey, F. Sadlo, and T. Ertl, “Mesh Generation From Layered Depth Images Using Isosurface Raycasting,” Lecture Notes in Computer Science, vol. 8034, pp. 373--383, 2013, doi: 10.1007/978-3-642-41939-3_36.
    8. M. Funk, A. Schmidt, and L. E. Holmquist, Antonius: a mobile search engine for the physical world. 2013. doi: 10.1145/2494091.2494149.

  7. G

    1. T. Gorius, R. Seifried, and P. Eberhard, “Approximate End-Effector Tracking Control of Flexible Multibody Systems Using Singular Perturbations,” Journal of Computational and Nonlinear Dynamics, vol. 9, no. 1, Art. no. 1, 2013, doi: 10.1115/1.4025635.
    2. S. Göttlich, S. Hoher, P. Schindler, V. Schleper, and A. Verl, “Modeling, simulation and validation of material flow on conveyor belts,” Applied Mathematical Modelling, 2013, doi: 10.1016/j.apm.2013.11.039.

  8. H

    1. B. Haasdonk, “Convergence Rates of the POD-Greedy Method,” M2AN, vol. 47, pp. 859--873, 2013, doi: 10.1051/m2an/2012045.
    2. B. Haasdonk, K. Urban, and B. Wieland, “Reduced Basis Methods for Parameterized Partial Differential Equations with Stochastic Influences Using the Karhunen-Lou00e8ve Expansion,” SIAM/ASA Journal on Uncertainty Quantification, vol. 1, pp. 79--105, 2013, doi: 10.1137/120876745.
    3. J. D. Halverson et al., “ESPResSo++: A modern multiscale simulation package for soft matter systems,” Computer Physics Communications, vol. 184, no. 4, Art. no. 4, 2013, doi: 10.1016/j.cpc.2012.12.004.
    4. T. Heidlauf and O. Röhrle, “Modeling the Chemoelectromechanical Behavior of Skeletal Muscle Using the Parallel Open-Source Software Library OpenCMISS,” Computational and Mathematical Methods in Medicine, vol. 2013, pp. 1--14, 2013, doi: 10.1155/2013/517287.
    5. T. Heidlauf and O. Röhrle, “On the treatment of active behaviour in continuum muscle mechanics,” PAMM, vol. 13, pp. 71--72, 2013, doi: 10.1002/pamm.201310031.
    6. S. Heinrich et al., “Determinants of robustness in spindle assembly checkpoint signalling,” Nature Cell Biology, vol. 15, pp. 1328--1339, 2013, doi: 10.1038/ncb2864.
    7. R. Helmig, B. Flemisch, M. Wolff, A. Ebigbo, and H. Class, “Model coupling for multiphase flow in porous media,” Advances in Water Resources, vol. 51, pp. 52--66, 2013, doi: 10.1016/j.advwatres.2012.07.003.
    8. M. Hlawatsch, F. Sadlo, M. Burch, and D. Weiskopf, “Scale-Stack Bar Charts,” Computer Graphics Forum, vol. 32, no. 3, Art. no. 3, 2013, doi: 10.1111/cgf.12105.
    9. M. Hlawatsch, F. Sadlo, and D. Weiskopf, “Predictability-Based Adaptive Mouse Interaction and Zooming for Visual Flow Exploration,” International Journal for Uncertainty Quantification, vol. 3, pp. 225--240, 2013, doi: 10.1615/Int.J.UncertaintyQuantification.2012003943.
    10. M. Hofacker and C. Miehe, “A phase field model of dynamic fracture: Robust field updates for the analysis of complex crack patterns,” International Journal for Numerical Methods in Engineering, vol. 93, pp. 276--301, 2013, doi: 10.1002/nme.4387.
    11. K. Häberle and W. Ehlers, “Carbon-dioxide storage and phase transitions: on the numerical modelling of injection and leakage,” Proceedings in Applied Mathematics and Mechanics, vol. 13, pp. 195--196, 2013, doi: 10.1002/pamm.201310093.

  9. K

    1. D. Karastoyanova and V. Andrikopoulos, “eScienceSWaT -- Towards an eScience Software Engineering Methodology,” Enterprise Distributed Object Computing Conference Workshops, pp. 229--238, 2013, doi: 10.1109/EDOCW.2013.32.
    2. G. K. Karch et al., “Visualization of Piecewise Linear Interface Calculation,” Proceedings of Pacific Visualization Symposium (PacificVis) 2013, pp. 121--128, 2013, doi: 10.1109/PacificVis.2013.6596136.
    3. T. Kempka et al., “A Dynamic Flow Simulation Code Intercomparison based on the Revised Static Model of the Ketzin Pilot Site,” Energy Procedia, vol. 40, pp. 418--427, 2013, doi: 10.1016/j.egypro.2013.08.048.
    4. M. Klinkigt, R. Weeber, S. Kantorovich, and C. Holm, “Cluster formation in systems of shifted-dipole particles,” Soft Matter, 2013, doi: 10.1039/C2SM27290C.
    5. J. Knittel, A. Sahami Shirazi, N. Henze, and A. Schmidt, “Utilizing contextual information for mobile communication,” CHI 13 Extended Abstracts on Human Factors in Computing Systems, 2013, doi: 10.1145/2468356.2468601.
    6. D. Koch and W. Ehlers, “On heat exchange and heat transport in a geothermal plant,” Proceedings in Applied Mathematics and Mechanics, vol. 13, pp. 201--202, 2013, doi: 10.1002/pamm.201310096.
    7. K. Kratzer, A. Arnold, and R. J. Allen, “Automatic, optimized interface placement in forward flux sampling simulations,” The Journal of Chemical Physics, vol. 138, no. 16, Art. no. 16, 2013, doi: 10.1063/1.4801866.
    8. J. Kästner, “The Path Length Determines the Tunneling Decay of Substituted Carbenes,” Chem. Eur. J., vol. 19, p. 8207, 2013, doi: 10.1002/chem.201203651.
    9. T. Köppl, B. I. Wohlmuth, and R. Helmig, “Reduced one-dimensional modelling and numerical simulation for mass transport in fluids,” International Journal for Numerical Methods in Fluids, vol. 72, pp. 135--156, 2013, doi: 10.1002/fld.3728.

  10. L

    1. P. Leube, F. P. J. de Barros, W. Nowak, and R. Rajagopal, “Towards optimal allocation of computer resources: Trade-offs between uncertainty quantification, discretization and model reduction,” Environmental Modelling & Software, vol. 50, pp. 97--107, 2013, doi: 10.1016/j.envsoft.2013.08.008.
    2. C. Linder and A. Raina, “A strong discontinuity approach on multiple levels to model solids at failure,” Computer Methods in Applied Mechanics and Engineering, vol. 253, pp. 558--583, 2013, doi: 10.1016/j.cma.2012.07.005.

  11. M

    1. J. Mabuma, B. Markert, and W. Ehlers, “Towards a Standardised Method for Visualisation of Stress Distribution at the Cartilage-Bone Interface,” Proceedings in Applied Mathematics and Mechanics, vol. 13, pp. 69--70, 2013, doi: 10.1002/pamm.201310030.
    2. N. M. Mascarenhas and J. Kästner, “How maltose influences structural changes to bind to maltose binding protein: Results from umbrella sampling simulation,” Proteins, vol. 81, p. 185, 2013, doi: 10.1002/prot.24174.
    3. C. Miehe, F. Aldakheel, and S. Mauthe, “Mixed variational principles and robust finite element implementations of gradient plasticity at small strains,” International Journal for Numerical Methods in Engineering, vol. 94, pp. 1037--1074, 2013, doi: 10.1002/nme.4486.
    4. J. M. Montenbruck, M. Bürger, and F. Allgöwer, “Practical Cluster Synchronization of Heterogeneous Systems on Graphs with Acyclic Topology,” Proc. 52nd IEEE Conference on Decision and Control, pp. 692--697, 2013, doi: 10.1109/CDC.2013.6759962.
    5. J. M. Montenbruck, G. S. Seyboth, and F. Allgöwer, “Practical and Robust Synchronization of Systems with Additive Linear Uncertainties,” Proc. 9th IFAC Symposium on Nonlinear Control Systems, pp. 743--748, 2013, doi: 10.3182/20130904-3-FR-2041.00134.
    6. M. Mottahedi, S. Röck, and A. Verl, “Simulation-Based Parameter Identification for Online Condition Monitoring of Spindle Nut Drive,” Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives, pp. 193--205, 2013, doi: 10.1007/978-3-642-34471-8_16.
    7. M. A. Müller and F. Allgöwer, “Economic model predictive control with self-tuning terminal cost,” European Journal of Control, vol. 19, pp. 408--416, 2013, doi: 10.1016/j.ejcon.2013.05.019.
    8. M. A. Müller, D. Angeli, and F. Allgöwer, “Economic model predictive control with self-tuning terminal weight,” Proceedings of the European Control Conference (ECC), pp. 2044--2049, 2013, [Online]. Available: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6669271
    9. M. A. Müller, D. Angeli, and F. Allgöwer, “Economic model predictive control with transient average constraints,” Proceedings of the 52nd IEEE Conference on Decision and Control (CDC), pp. 5119--5124, 2013, doi: 10.1109/CDC.2013.6760693.
    10. M. A. Müller, D. Angli, and F. Allgöwer, “On convergence of averagely constrained economic MPC and necessity of dissipativity for optimal steady-state operation,” Proceedings of the American Control Conference (ACC), pp. 3147--3152, 2013, doi: 10.1109/ACC.2013.6580314.
    11. M. A. Müller, D. Liberzon, and F. Allgöwer, “Norm-controllability, or how a nonlinear system responds to large inputs,” Proceedings of the 9th IFAC Symposium on Nonlinear Control Systems (NOLCOS), pp. 104--109, 2013, doi: 10.3182/20130904-3-FR-2041.00052.

  12. N

    1. W. Nowak and A. Litvinenko, “Kriging and spatial design accelerated by orders of magnitude: combining low-rank covariance approximations with FFT-techniques,” Mathematical Geosciences, vol. 45, no. 4, Art. no. 4, 2013, doi: 10.1007/s11004-013-9453-6.

  13. O

    1. S. Oladyshkin, H. Class, and W. Nowak, “Bayesian updating via Bootstrap filtering combined with data-driven polynomial chaos expansions: methodology and application to history matching for carbon dioxide storage in geological formations,” Computational Geosciences, vol. 17, no. 4, Art. no. 4, 2013, doi: 10.1007/s10596-013-9350-6.
    2. S. Oladyshkin, P. Schroeder, H. Class, and W. Nowak, “Chaos expansion based Bootstrap filter to calibrate CO2 injection models,” Energy Procedia, vol. 40, pp. 398--407, 2013, doi: 10.1016/j.egypro.2013.08.046.
    3. B. Ottenwälder, B. Koldehofe, K. Rothermel, and U. Ramachandran, “MigCEP: Operator Migration for Mobility Driven Distributed Complex Event Processing,” Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems (DEBS), 2013, doi: 10.1145/2488222.2488265.

  14. P

    1. B. Pfleging, N. Henze, A. Schmidt, D. Rau, and B. Reitschuster, “Influence of subliminal cueing on visual search tasks,” CHI 13 Extended Abstracts on Human Factors in Computing Systems, 2013, doi: 10.1145/2468356.2468583.
    2. B. Pfleging, S. Schneegass, and A. Schmidt, “Exploring User Expectations for Context and Road Video Sharing While Calling and Driving,” Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 132--139, 2013, doi: 10.1145/2516540.2516547.
    3. D. Philipp, J. Stachowiak, P. Alt, F. Dürr, and K. Rothermel, “DrOPS: Model-Driven Optimization for Public Sensing Systems,” 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom 2013), 2013, doi: 10.1109/PerCom.2013.6526731.
    4. D. Philipp, J. Stachowiak, F. Dürr, and K. Rothermel, “Model-Driven Public Sensing in Sparse Networks,” Proceedings of the 10th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2013, doi: 10.1007/978-3-319-11569-6_2.

  15. R

    1. P. Rauschenberger et al., “Comparative assessment of Volume-of-Fluid and Level-Set methods by relevance to dendritic ice growth in supercooled water,” Journal of Computers and Fluids, vol. 79, pp. 44--52, 2013, doi: 10.1016/j.compfluid.2013.03.010.
    2. M. Reble, E. Quevedo, and F. Allgöwer, “Control over Erasure Channels: Stochastic Stability and Performance of Packetized Unconstrained Model Predictive Control,” International Journal of Robust and Nonlinear Control, pp. 1151--1167, 2013, doi: 10.1002/rnc.2853.
    3. P. Reimann and H. Schwarz, “Datenmanagementpatterns in Simulationsworkflows,” 15. GI-Fachtagung Datenbanksysteme für Business, Technologie und Web (BTW 2013), pp. 279--293, 2013, [Online]. Available: http://www.btw-2013.de/proceedings/Datenmanagementpatterns%20in%20Simulationsworkflows.pdf
    4. T. Ricken, U. Dahmen, O. Dirsch, and D. Q. Werner, “A Biphasic 3D-FEM model for the remodeling of microcirculation in liver lobes,” in Computer Models in Biomechanics, in Computer Models in Biomechanics. , Springer, 2013, pp. 277--292.
    5. C. Rohde and F. Xie, “Decay Rates to Viscous Contact Waves for a 1D Compressible Radiation Hydrodynamics Model,” Mathematical Models and Methods in Applied Sciences, vol. 23, no. 3, Art. no. 3, 2013, doi: 10.1142/S0218202512500522.
    6. N. Roth et al., “Collision of Droplets: An Experimental, Numerical and Analytical Approach,” Proceedings of the ILASS - Europe 2013, 2013, [Online]. Available: http://ilasseurope.org/events/25th-ilass-europe/
    7. O. Röhrle, M. Sprenger, E. Ramasamy, and T. Heidlauf, “Multiscale Skeletal Muscle Modeling: From Cellular Level to a Multi-segment Skeletal Muscle Model of the Upper Limb,” Computer Models in Biomechanics, pp. 103--116, 2013, doi: 10.1007/978-94-007-5464-5_8.

  16. S

    1. V. Schauer and C. Linder, “All-electron Kohn-Sham density functional theory on hierarchic finite element spaces,” Journal of Computational Physics, vol. 250, pp. 644--664, 2013, doi: 10.1016/j.jcp.2013.04.020.
    2. D. Scheer, Computersimulationen in politischen Entscheidungsprozessen: Zur Politikrelevanz von Simulationswissen am Beispiel der CO2-Speicherung. Springer SV, Wiesbaden, 2013. doi: 10.1007/978-3-658-03394-1.
    3. D. Scheer, “Risk governance and emerging technologies: learning from case study integration,” Journal of Risk Research, vol. 16/3–4, pp. 355--368, 2013, doi: 10.1080/13669877.2012.729519.
    4. M. Schenke, B. Markert, and W. Ehlers, “Liquefaction in fluid-saturated soils,” Proceedings in Applied Mathematics and Mechanics, vol. 13, pp. 145--146, 2013, doi: 10.1002/pamm.201310068.
    5. C. W. Scherer, Gain-scheduled synthesis with dynamic generalized strictly positive real multipliers: A complete solution. 52nd IEEE Conf. Decision and Control, Firenze, 2013. doi: 10.1109/CDC.2013.6760520.
    6. C. W. Scherer, “Gain-scheduled synthesis with dynamic stable strictly positive real multipliers: A complete solution,” ECC, 2013, [Online]. Available: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6669184&tag=1
    7. C. W. Scherer, “Structured H∞-Optimal Control for Nested Interconnections: A State-Space Solution,” Syst. Contr. Letters, vol. 62, pp. 1105--1113, 2013, doi: 10.1016/j.sysconle.2013.09.001.
    8. C. W. Scherer, “Structured $H_ınfty$-Optimal Control for Nested Interconnections: A State-Space Solution,” Syst. Control Lett., vol. 62, no. 12, Art. no. 12, 2013, doi: 10.1016/j.sysconle.2013.09.001.
    9. C. W. Scherer, “Structured $H_ınfty$-Optimal Control for Nested Interconnections: A State-Space Solution,” Syst. Control Lett., vol. 62, no. 12, Art. no. 12, 2013, doi: 10.1016/j.sysconle.2013.09.001.
    10. C. W. Scherer and I. E. Köse, “From transfer matrices to realizations: Convergence properties and parametrization of robustness analysis conditions,” Syst. Control Lett., vol. 62, no. 8, Art. no. 8, 2013, doi: 10.1016/j.sysconle.2013.04.001.
    11. A. Schlaich, S. Tyagi, S. Kesselheim, M. Sega, and C. Holm, “Renormalized charge and dielectric effects in colloidal interactions: a numerical solution of the nonlinear Poisson--Boltzmann equation for unknown boundary conditions,” The European Physical Journal E, vol. 46, no. 9, Art. no. 9, Sep. 2023, doi: 10.1140/epje/s10189-023-00334-2.
    12. G. S. Schmidt, S. Michalowsky, C. Ebenbauer, and F. Allgöwer, “Global Output Regulation for the Rotational Dynamics of a Rigid Body / Globale Ausgangsregelung für die Drehbewegung eines Starrkörpersystems,” at - Automatisierungstechnik, vol. 61, no. 8, Art. no. 8, 2013, doi: 10.1524/auto.2013.1013.
    13. G. S. Schmidt, C. Ebenbauer, and F. Allgower, “Output regulation for attitude control: A global approach,” American Control Conference, pp. 5251--5256, 2013, doi: 10.1109/ACC.2013.6580656.
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