Publications 2016

  1. A

    1. SS. Agada, S. Geiger, H. ElSheikh, and S. Oladyshkin, “Data-driven surrogates for rapid simulation and optimisation of WAG injection in fractured carbonate reservoirs,” Petroleum Geoscience, 2016, doi: 10.1144/petgeo2016-068.
    2. M. Alkämper, A. Dedner, R. Klöfkorn, and M. Nolte, “The DUNE-ALUGrid Module,” Archive of Numerical Software, vol. 4, pp. 1--28, 2016, doi: 10.11588/ans.2016.1.23252.
    3. E. Altan, A. Zöllner, O. Avci, and O. Röhrle, “Towards modelling skeletal muscle growth and adaptation,” Proceedings in Applied Mathematics and Mechanics, vol. 16, pp. 921--924, 2016, doi: 10.1002/pamm.201610448.
    4. S. Alvarez Barcia, M. Russ, J. Meisner, and J. Kästner, “Atom tunnelling in the reaction NH3+ + H2 --> NH4+ + H and its astrochemical relevance,” Faraday Discuss., 2016, doi: 10.1039/C6FD00096G.
  2. B

    1. A. Barth, R. Bürger, I. Kröker, and C. Rohde, “Computational uncertainty quantification for a clarifier-thickener model with several random perturbations: a hybrid stochastic Galerkin approach,” Computers & Chemical Engineering, vol. 89, pp. 11--26, 2016, doi: 10.1016/j.compchemeng.2016.02.016.
    2. A. Barth and T. Stüwe, “Weak convergence of Galerkin approximations of stochastic partial differential equations driven by additive Levy noise,” Mathematics and Computers in Simulation, 2016, [Online]. Available: http://arxiv.org/abs/1603.02422.
    3. A. Barth and F. Fuchs, “Uncertainty quantification for hyperbolic conservation laws with flux coefficients given by spatiotemporal random fields,” SISC: Meth. and Alg. for Scientific Computing, 2016, [Online]. Available: http://arxiv.org/abs/1402.2156.
    4. A. Barth, S. Moreno-Bromberg, and O. Reichmann, “A Non-Stationary Model of Dividend Distribution in A Stochastic Interest-Rate Setting,” Computational Economics, vol. 47, no. 3, Art. no. 3, 2016, doi: 10.1007/s10614-015-9502-y.
    5. A. Barth, C. Schwab, and J. Sukys, “Multilevel Monte Carlo Simulation of Statistical Solutions to the Navier--Stokes Equations,” in Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014, R. Cools and D. Nuyens, Eds. Cham: Springer International Publishing, 2016, pp. 209--227.
    6. P. Bastian et al., Advances Concerning Multiscale Methods and Uncertainty Quantification in “EXA-DUNE in Software for Exascale Computing -- SPPEXA 2013--2015.” Springer, 2016.
    7. P. Bastian et al., Hardware-Based Efficiency Advances in the EXA-DUNE Project in “Software for Exascale Computing -- SPPEXA 2013--2015.” Springer, 2016.
    8. F. Bayer, F. D. Brunner, M. Lazar, M. Wijnand, and F. Allgöwer, “A tube-based approach to nonlinear explicit MPC,” 55th IEEE Conference on Decision and Control (CDC), pp. 4059--4064, 2016, doi: 10.1109/CDC.2016.7798884.
    9. F. Bayer, M. A. Müller, and F. Allgöwer, “Min-max economic model predictive control approaches with guaranteed performance,” 55th IEEE Conference on Decision and Control (CDC), pp. 3210--3215, 2016, doi: 10.1109/CDC.2016.7798751.
    10. F. Bayer, M. Lorenzen, M. A. Müller, and F. Allgöwer, “Robust economic Model Predictive Control using stochastic information,” Automatica, vol. 74, pp. 151--161, 2016, doi: 10.1016/j.automatica.2016.08.008.
    11. A. Beck, D. Flad, C. Tonhäuser, G. Gassner, and C.-D. Munz, “On the Influence of Polynomial De-aliasing on Subgrid Scale Models,” Flow Turbulence Combustion, vol. 97, pp. 475--511, 2016, doi: 10.1007/s10494-016-9704-y.
    12. A. D. Beck, D. G. Flad, C. Tonhäuser, G. Gassner, and C.-D. Munz, “On the Influence of Polynomial De-aliasing on Subgrid Scale Models,” Flow, Turbulence and Combustion, vol. 97, no. 2, Art. no. 2, 2016, doi: 10.1007/s10494-016-9704-y.
    13. S. Bidier and W. Ehlers, “A homogenisation strategy for micromorphic continua based on particle mechanics,” Proceedings in Applied Mathematics and Mechanics, vol. 16, pp. 515--516, 2016, doi: 10.1002/pamm.201610246.
    14. V. Bruder, S. Frey, and T. Ertl, “Real-Time Performance Prediction and Tuning for Interactive Volume Raycasting,” SIGGRAPH ASIA 2016 Symposium on Visualization, vol. 7, 2016, doi: 10.1145/3002151.3002156.
    15. F. D. Brunner, M. A. Müller, and F. Allgöwer, “Enhancing Output Feedback MPC for Linear Discrete-time Systems with Set-valued Moving Horizon Estimation,” 55th IEEE Conference on Decision and Control (CDC), pp. 2733--2738, 2016, doi: 10.1109/CDC.2016.7798675.
    16. F. D. Brunner, M. Heemels, and F. Allgöwer, “Robust self-triggered MPC for constrained linear systems: A tube-based approach,” Automatica, vol. 72, pp. 73--83, 2016, doi: 10.1016/j.automatica.2016.05.004.
    17. F. D. Brunner and F. Allgöwer, “A Lyapunov Function Approach to the Event-triggered Stabilization of the Minimal Robust Positively Invariant Set,” 6th IFAC Workshop on Distributed Estimation and Control in Networked Systems, pp. 25--30, 2016, doi: 10.1016/j.ifacol.2016.10.367.
    18. F. D. Brunner, F. A. Bayer, and F. Allgöwer, “Robust Steady State Optimization for Polytopic Systems,” 55th IEEE Conference on Decision and Control (CDC), pp. 4084--4089, 2016, doi: 10.1109/CDC.2016.7798888.
    19. F. D. Brunner, W. P. M. H. Heemels, and F. Allgöwer, ?“-invasive event-triggered and self-triggered control for perturbed linear systems,” 55th IEEE Conference on Decision and Control (CDC), pp. 1346--1351, 2016, doi: 10.1109/CDC.2016.7798453.
    20. F. D. Brunner, W. P. M. H. Heemels, and F. Allgöwer, “Dynamic Thresholds in Robust Event-Triggered Control for Discrete-Time Linear Systems,” Proceedings of the European Control Conference (2016), pp. 983--988, 2016, doi: 10.1109/ECC.2016.7810417.
    21. F. D. Brunner, W. P. M. H. Heemels, and F. Allgöwer, “Numerical Evaluation of a Robust Self-Triggered MPC Algorithm,” 6th IFAC Workshop on Distributed Estimation and Control in Networked Systems, pp. 151--156, 2016, doi: 10.1016/j.ifacol.2016.10.388.
  3. C

    1. S.-Y. Chong and O. Röhrle, “Exploring the Use of Non-Image-Based Ultrasound to Detect the Position of the Residual Femur within a Stump,” PLoS ONE, vol. 11, 2016, doi: 10.1371/journal.pone.0164583.
  4. D

    1. F. Drunsel and J. Gross, “Theory of model electrolyte solutions: Assessing the short- and long-ranged contributions by molecular simulations,” Fluid Phase Equilibria, vol. 430, pp. 195--206, 2016, doi: 10.1016/j.fluid.2016.09.026.
    2. F. Drunsel and J. Gross, “Chemical potential of model electrolyte solutions consisting of hard sphere ions and hard dipoles from molecular simulations,” Fluid Phase Equilibria, vol. 429, pp. 205--213, 2016, doi: 10.1016/j.fluid.2016.08.039.
  5. E

    1. W. Ehlers and K. Häberle, “Interfacial mass transfer during gas-liquid phase change in deformable porous media with heat transfer,” Transport in Porous Media, vol. 114, pp. 525--556, 2016, doi: 10.1007/s11242-016-0674-2.
    2. K. Eisenschmidt et al., “Direct numerical simulations for multiphase flows: An overview of the multiphase code FS3D.,” Applied Mathematics and Computation, vol. 272, pp. 508–517, 2016, [Online]. Available: http://dblp.uni-trier.de/db/journals/amc/amc272.html#EisenschmidtEGK16.
    3. L. Eurich, R. Schott, A. Wagner, A. Roth-Nebelsick, and W. Ehlers, “From functional properties of frost-resistant plant tissues towards customised construction materials - A continuum-mechanical approach,” PAMM, vol. 16, pp. 81--82, 2016, doi: 10.1002/pamm.201610029.
  6. F

    1. J. Fehr, J. Fuhrer, C. Kleinbach, M. Hanss, and P. Eberhard, “Fuzzy-Based Analysis of a Hill-Type Muscle Model,” Proceedings in Applied Mathematics and Mechanics, vol. 16, pp. 31--34, 2016, doi: 10.1002/pamm.201610009.
    2. J. Fehr, P. Holzwarth, and P. Eberhard, “Interface and Model Reduction for Efficient Explicit Simulations -a Case Study with Nonlinear Vehicle Crash Models,” Mathematical and Computer Modelling of Dynamical Systems, vol. 22, pp. 380--396, 2016, doi: 10.1080/13873954.2016.1198385.
    3. L. Feller, C. Kleinbach, J. Fehr, and S. Schmitt, Incorporating Muscle Activation Dynamics into the Global Human Body Model. 2016.
    4. O. Fernandes, S. Frey, and T. Ertl, “Interpolation-Based Extraction of Representative Isosurfaces,” Lecture Notes in Computer Science, 2016, doi: 10.1007/978-3-319-50835-1_37.
    5. J. Fernandez et al., “Multiscale musculoskeletal modelling, data--model fusion and electromyography-informed modelling,” Interface Focus, vol. 6, 2016, doi: 10.1098/rsfs.2015.0084.
    6. M. Fetzer and C. W. Scherer, “A General Integral Quadratic Constraints Theorem with Applications to a Class of Sampled-Data Systems,” SIAM Journal on Control and Optimization, vol. 54, no. 3, Art. no. 3, 2016, doi: 10.1137/140985482.
    7. M. Fetzer and C. W. Scherer, Stability and performance analysis on Sobolev spaces. 55th IEEE Conf. on Decision and Control, 2016.
    8. T. Fetzer, K. M. Smits, and R. Helmig, “Effect of Turbulence and Roughness on Coupled Porous-Medium/Free-Flow    Exchange Processes,” TRANSPORT IN POROUS MEDIA, vol. 114, no. 2, SI, Art. no. 2, SI, 2016, doi: 10.1007/s11242-016-0654-6.
    9. D. Fink and W. Ehlers, “Model reduction for multi-component porous-media models of biological materials using POD-DEIM,” PAMM, vol. 16, pp. 441--442, 2016, doi: 10.1002/pamm.201610209.
    10. D. Flad, A. Beck, and C.-D. Munz, “Simulation of underresolved turbulent flows by adaptive filtering using the high order discontinuous Galerkin spectral element method,” Journal of Computational Physics, vol. 313, pp. 1--12, 2016, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S002199911500827X.
    11. B. Flemisch, JM. Nordbotten, W. Nowak, and A. Raoof, “Special Issue on NUPUS: Non-linearities and Upscaling in Porous Media (Editorial),” Transport in Porous Media, vol. 114, pp. 237--2340, 2016, doi: 10.1007/s11242-016-0735-6.
    12. H. Frank and C.-D. Munz, “Direct aeroacoustic simulation of acoustic feedback phenomena on a side-view mirror,” Journal of Sound and Vibration, vol. 371, pp. 132--149, 2016, [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0022460X1600136X.
    13. S. Frey and T. Ertl, “Auto-Tuning Intermediate Representations for In Situ Visualization,” New York Scientific Data Summit, 2016, doi: 10.1109/NYSDS.2016.7747807.
    14. S. Frey and T. Ertl, “Flow-Based Temporal Selection for Interactive Volume Visualization,” Computer Graphics Forum, p. 11, 2016, doi: 10.1111/cgf.13070.
    15. F. Fritzen, L. Xia, M. Leuschner, and P. Breitkopf, “Topology optimization of multiscale elastoviscoplastic structures,” International Journal for Numerical Methods in Engineering, vol. 106, pp. 430--453, 2016, doi: 10.1002/nme.5122.
  7. G

    1. J. Gebhardt and N. Hansen, “Calculation of binding affinities for linear alcohols to alpha-cyclodextrin by twin-system enveloping distribution sampling simulations,” Fluid Phase Equilibria, vol. 422, pp. 1--17, 2016, doi: 10.1016/j.fluid.2016.02.001.
    2. E.-M. Geissen, J. Hasenauer, S. Heinrich, S. Hauf, F. J. Theis, and N. E. Radde, “MEMO: multi-experiment mixture model analysis of censored data,” Bioinformatics, 2016, doi: 10.1093/bioinformatics/btw190.
    3. M. Geveler, B. Reuter, V. Ayzinger, D. Göddeke, and S. Turek, “Energy efficiency of the simulation of three-dimensional coastal ocean circulation on modern commodity and mobile processors -- A case study based on the Haswell and Cortex-A15 microarchitectures,” Computer Science - Research and Development, vol. 31, pp. 225--234, 2016, doi: 10.1007/s00450-016-0324-5.
    4. D. Grunert and J. Fehr, “Identification of Nonlinear Behavior with Clustering Techniques in Car Crash Simulations for Better Model Reduction,” Advanced Modeling and Simulation in Engineering Sciences, vol. 1, pp. 1--19, 2016, doi: 10.1186/s40323-016-0072-x.
    5. D. Göddeke and M. Altenbernd, “Soft fault detection and correction for multigrid,” The International Journal of High Performance Computing Applications, 2016, doi: 10.1177/1094342016684006.
  8. H

    1. M. Hahn, D. Karastoyanova, and F. Leymann, “Data-Aware Service Choreographies through Transparent Data Exchange,” Lecture Notes in Computer Science (LNCS), vol. 9671, pp. 357--364, 2016, doi: 10.1007/978-3-319-38791-8_20.
    2. M. Hahn, D. Karastoyanova, and F. Leymann, “A Management Life Cycle for Data-Aware Service Choreographies,” Proceedings of the the 23rd International Conference on Web Services (ICWS), pp. 364--371, 2016, doi: 10.1109/ICWS.2016.54.
    3. T. Heidlauf et al., “A multi-scale continuum model of skeletal muscle mechanics predicting force enhancement based on actin--titin interaction,” Biomechanics and Modeling in Mechanobiology, vol. 15, pp. 1423--1437, 2016, doi: 10.1007/s10237-016-0772-7.
    4. F. Hempert, M. Hoffmann, U. Iben, and C.-D. Munz, “On the simulation of industrial gas dynamic applications with the discontinuous Galerkin spectral element method,” Journal of Thermal Science, vol. 25, pp. 250--257, 2016, [Online]. Available: https://link.springer.com/article/10.1007/s11630-016-0857-8.
    5. A. Hofmann, N.-P. Walz, and M. Hanss, “An Approach to Feed-Forward Controller Design for Underactuated Multibody Systems in the Presence of Uncertainty,” Proceedings in Applied Mathematics and Mechanics, vol. 16http://onli, no. 1, Art. no. 1, 2016, doi: 10.1002/pamm.201610018.
    6. T. Holicki and C. W. Scherer, Controller synthesis for distributed systems over undirected graphs. 55th IEEE Conf. on Decision and Control, 2016.
    7. M.-T. Hütt and N. Radde, “The Physics behind Systems Biology,” Eur Phys J Nonlin Biomed Phys, vol. 4, no. 1, Art. no. 1, 2016, doi: 10.1140/epjnbp/s40366-016-0034-8.
  9. J

    1. S. Jamei, P. Asgharzadeh, and W. Ehlers, “Partitioned treatment of surface-coupled problems with application to the fluid-porous-media interaction,” PAMM, vol. 16, pp. 507--508, 2016, doi: 10.1002/pamm.201610242.
  10. K

    1. M.-A. Keip and M. Rambausek, “A multiscale approach to the computational characterization of magnetorheological elastomers,” International Journal for Numerical Methods in Engineering, vol. 107, pp. 338--360, 2016, doi: 10.1002/nme.5178.
    2. J. Kirch, C. Thomaseth, A. Jensch, and N. Radde, “The effect of model rescaling and normalization on sensitivity analysis on an example of a MAPK pathway model,” EPJ Nonlinear Biomedical Physics, vol. 4:3, 2016, doi: 10.1140/epjnbp/s40366-016-0030-z.
    3. J. Koch and W. Nowak, “Identification of contaminant source architectures - A statistical inversion that emulates multi-phase physics in a computationally practicable manner,” Water Resources Research, vol. 52, pp. 1009--1025, 2016, doi: 10.1002/2015WR017894.
    4. T. Koeppl, E. Vidotto, and B. Wohlmuth, “A local error estimate for the Poisson equation with a line source term,” Numerical Mathematics and Advanced Applications ENUMATH 2015, pp. 421--429, 2016, [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-319-39929-4_40.
    5. A. N. Krishnamoorthy, J. Zeman, C. Holm, and J. Smiatek, “Preferential solvation and ion association properties in aqueous dimethyl sulfoxide solutions,” PCCP, vol. 18, pp. 31312--31322, 2016, doi: 10.1039/C6CP05909K.
    6. P. N. Köhler, M. A. Müller, and F. Allgöwer, “A distributed economic MPC scheme for coordination of self-interested systems,” Proceedings of the American Control Conference, pp. 889--894, 2016, doi: 10.1109/ACC.2016.7525027.
  11. L

    1. S. Linsenmayer, D. V. Dimarogonas, and F. Allgöwer, “A non-monotonic approach to periodic event-triggered control with packet loss,” Proceedings of the 55th IEEE Conference on Decision and Control (CDC), pp. 507--512, 2016, doi: 10.1109/CDC.2016.7798319.
    2. C. Luo and W. Ehlers, “A three-dimensional model of hydraulic fracturing,” PAMM, vol. 16, pp. 465--466, 2016, doi: 10.1002/pamm.201610221.
    3. O. Lötgering-Lin, A. Schöniger, W. Nowak, and J. Gross, “Bayesian Model Selection Helps To Choose Objectively between Thermodynamic Models: A Demonstration of Selecting a Viscosity Model Based on Entropy Scaling,” Industrial & Engineering Chemistry Research, vol. 55, no. 38, Art. no. 38, 2016, doi: 10.1021/acs.iecr.6b02671.
    4. O. Lötgering-Lin, A. Schwinger, W. Nowak, and J. Gross, “Bayesian Model Selection Helps To Choose Objectively between Thermodynamic Models: A Demonstration of Selecting a Viscosity Model Based on Entropy Scaling,” I&EC research, vol. 55, pp. 10191--10207, 2016, doi: 10.1021/acs.iecr.6b02671.
  12. M

    1. S. Mauthe and C. Miehe, “Hydraulic fracture in poro-hydro-elastic media,” Mechanics Research Communications, 2016, doi: 10.1016/j.mechrescom.2016.09.009.
    2. J. Meisner and J. Kästner, “Atom-Tunneling in Chemistry,” Angewandte Chemie International Edition, vol. 55, pp. 5400--5413, 2016, doi: 10.1002/anie.201511028.
    3. J. Meisner and J. Kästner, “Reaction rates and kinetic isotope effects of H2 + OH ? H2O + H,” The Journal of Chemical Physics, vol. 144, p. 174303, 2016, doi: 10.1063/1.4948319.
    4. J. M. Montenbruck and F. Allgöwer, “Asymptotic Stabilization of Submanifolds Embedded in Riemannian Manifolds,” Automatica, vol. 74, pp. 349--359, 2016, doi: 10.1016/j.automatica.2016.07.026.
    5. J. M. Montenbruck, M. Bürger, and F. Allgöwer, “Compensating Drift Vector Fields with Gradient Vector Fields for Asymptotic Submanifold Stabilization,” IEEE Transactions on Automatic Control, vol. 61, 2016, doi: 10.1109/TAC.2015.2434032.
    6. J. M. Montenbruck and F. Allgöwer, “Some problems arising in controller design from big data via input-output methods,” in 2016 IEEE 55th Conference on Decision and Control (CDC), 2016, pp. 6525--6530.
    7. S. Most, B. Bijeljic, and W. Nowak, “Evolution and persistence of cross-directional statistical dependence during finite-Pu00e9clet transport through a real porous medium,” Water Resources Research, vol. 52, pp. 8920--8937, 2016, doi: 10.1002/2016WR018969.
    8. S. Most, B. Bijeljic, and W. Nowak, “Evolution and persistence of cross-directional statistical dependence during finite-Peclet transport through a real porous medium,” Water Resources Research Internet, 2016, doi: 10.1002/2016WR018969.
  13. N

    1. O. Nadgir, M.-A. Keip, and C. Miehe, “An anisotropic phase-field model for transversely isotropic barium titanate with bounded moduli,” Proceedings in Applied Mathematics and Mechanics, vol. 16, pp. 467--468, 2016, doi: 10.1002/pamm.201610222.
    2. S. M. Najmabadi, Z. Wang, Y. Baroud, and S. Simon, A self-adaptive dynamic partial reconfigurable architecture for online data stream compression. 2016.
    3. S. M. Najmabadi, Z. Wang, Y. Baroud, and S. Simon, Online bandwidth reduction using dynamic partial reconfiguration. 2016.
    4. A. Namhata, S. Oladyshkin, RM. Dilmore, L. Zhang, and DV. Nakles, “Probabilistic Assessment of Above Zone Pressure Predictions at a Geologic Carbon Storage Site,” Scientific Reports, vol. 6, p. 39536, 2016, doi: 10.1038/srep39536.
    5. I. Notarnicola, F. Bayer, G. Notarstefano, and F. Allgöwer, “Final-State Constrained Optimal Control via a Projection Operator Approach,” European Control Conference (ECC), pp. 148--153, 2016, doi: 10.1109/ECC.2016.7810278.
    6. W. Nowak and A. Guthke, “Entropy-based experimental design for optimal model discrimination in the geosciences,” Entropy, vol. 18, no. 11, Art. no. 11, 2016, doi: 10.3390/e18110409.
  14. P

    1. D. Paul and N. Radde, “Robustness and filtering properties of ubiquitous signaling network motifs,” IFAC-PapersOnLine, vol. 49, no. 26, Art. no. 26, 2016, [Online]. Available: http://www.sciencedirect.com/science/article/pii/S2405896316327768.
  15. R

    1. C. Rahmann, V. Vittal, J. Ascui, and J. Haas, “Mitigation Control against Partial Shading Effects in Large-scale PV Power Plants,” IEEE Transactions on Sustainable Energy, vol. 7, no. 1, Art. no. 1, 2016, doi: 10.1109/TSTE.2015.2484261.
    2. M. Rambausek, M.-A. Keip, and C. Miehe, “A multiscale view on shape effects in the computational characterization of magnetorheological elastomers,” Proceedings in Applied Mathematics and Mechanics, vol. 16, pp. 383--384, 2016, doi: 10.1002/pamm.201610180.
    3. M. Redeker, C. Rohde, and I. S. Pop, “Upscaling of a tri-phase phase-field model for precipitation in porous    media,” IMA JOURNAL OF APPLIED MATHEMATICS, vol. 81, no. 5, Art. no. 5, 2016, doi: 10.1093/imamat/hxw023.
    4. O. Röhrle, M. Sprenger, and S. Schmitt, “A two-muscle, continuum-mechanical forward simulation of the upper limb,” Biomechanics and Modeling in Mechanobiology, 2016, doi: 10.1007/s10237-016-0850-x.
    5. O. Röhrle, V. Neumann, and T. Heidlauf, “The Role of Parvalbumin, Sarcoplasmatic Reticulum Calcium Pump Rate, Rates of Cross-Bridge Dynamics, and Ryanodine Receptor Calcium Current on Peripheral Muscle Fatigue: A Simulation Study,” Computational and Mathematical Methods in Medicine, 2016, doi: 10.1155/2016/3180205.
  16. S

    1. M. Schenke and W. Ehlers, “Numerical investigation of vacuum-assisted resin transfer moulding (VARTM) within deformable fibre fabrics,” PAMM, vol. 16, pp. 479--480, 2016, doi: 10.1002/pamm.201610228.
    2. C. W. Scherer, “Lossless H?-synthesis for 2D systems (special issue JCW),” Syst. Contr. Letters, vol. 35, pp. 25--35, 2016, doi: 10.1016/j.sysconle.2016.02.011.
    3. A. Schmidt and B. Haasdonk, “Reduced basis method for H2 optimal feedback control problems,” Proceedings of CPDE2016, vol. 49, pp. 327--332, 2016, doi: 10.1016/j.ifacol.2016.07.462.
    4. P. Schröder, A. Wagner, and W. Ehlers, “Multi-component modelling and simulation of metastases proliferation within brain tissue,” PAMM, vol. 15, pp. 101--102, 2016, [Online]. Available: http://onlinelibrary.wiley.com/doi/10.1002/pamm.201610039/full.
    5. DO. Schulte, W. Rühaak, S. Oladyshkin, B. Welsch, and I. Sass, “Optimization of Medium Deep Borehole Thermal Energy Storages,” Energy Technology, vol. 4, pp. 104--113, 2016, doi: 10.1002/ente.201500254.
    6. A. Schöll, C. Braun, M. A. Kochte, and H.-J. Wunderlich, “Efficient Algorithm-Based Fault Tolerance for Sparse Matrix Operations,” Proceedings of the 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), pp. 251--262, 2016, doi: 10.1109/DSN.2016.31.
    7. A. Schöll, C. Braun, and H.-J. Wunderlich, “Applying Efficient Fault Tolerance to Enable the Preconditioned Conjugate Gradient Solver on Approximate Computing Hardware,” Proceedings of the International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFTS), pp. 21--26, 2016, doi: 10.1109/DFT.2016.7684063.
    8. A. V. Shapeev, “Moment tensor potentials: A class of systematically improvable interatomic potentials,” Multiscale Modeling & Simulation, vol. 14, no. 3, Art. no. 3, 2016.
    9. L. Smith, W. F. van Gunsteren, and N. Hansen, “On the use of time-averaging restraints when deriving biomolecular structure from 3J-coupling values obtained from NMR experiments,” Journal of Biomolecular NMR, vol. 66, pp. 69--83, 2016, doi: 10.1007/s10858-016-0058-5.
    10. A. Sridhar, M.-A. Keip, and C. Miehe, “Homogenization in micro-magneto-mechanics,” Computational Mechanics, vol. 58, pp. 151--169, 2016, doi: 10.1007/s00466-016-1286-y.
  17. T

    1. P. Tempel, “Improved Modeling of Cables for Kinematics and Dynamics of Lightweight Robots (iCaMDyRo),” Im Blickpunkt 2016, 2016, [Online]. Available: http://www.isw.uni-stuttgart.de/files/institut/Blickpunkt-ISW-2016.pdf.
    2. P. Tempel and A. Pott, “Parallele Seilroboter in Theorie und Praxis - Leichtbau, Energieeffizienz und Hohe Dynamiken als Potential, Elastizität als Hauptherausforderung,” wt Werkstattstechnik online Jahrgang 106 (2016), vol. 9, pp. 643--647, 2016, [Online]. Available: http://www.werkstattstechnik.de/wt/get_article.php?dataarticle_id=86418.
    3. P. Tempel, A. Verl, and A. Pott, “On the Dynamics and Emergency Stop Behavior of Cable-Driven Parallel Robots,” ROMANSY 21 Robot Design, Dynamics and Control, vol. 569, pp. 431--438, 2016, doi: 10.1007/978-3-319-33714-2_48.
    4. C. Thomaseth and N. Radde, “Normalization of Western blot data affects the statistics of estimators,” IFAC-PapersOnLine, vol. 49, no. 26, Art. no. 26, 2016, doi: 10.1016/j.ifacol.2016.12.103.
    5. E. Trottemant, M. Mazo, and C. W. Scherer, “Synthesis of Robust Piecewise Affine Output-Feedback Strategies,” J. Guid. Control Dynam., vol. 39, pp. 1461--1469, 2016, doi: 10.2514/1.G001343.
  18. V

    1. W. F. van?Gunsteren et al., “Deriving Structural Information from Experimentally Measured Data on Biomolecules,” Angewandte Chemie International Edition, vol. 55, pp. 15990--16010, 2016, doi: 10.1002/anie.201601828.
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