Publications 2016

  1. A

    1. 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.
    2. L. Allerhand, E. Gershon., and U. Shaked, “Robust state-feedback control of stochastic state-multiplicative discrete-time linear switched systems with dwell time,” Int. J. Robust Nonlin., vol. 26, no. 2, Art. no. 2, 2016, doi: 10.1002/rnc.3301.
    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. K. Baber, B. Flemisch, and R. Helmig, “Modeling drop dynamics at the interface between free and porous-medium    flow using the mortar method,” International Journal of Heat and Mass Transfer, vol. 99, pp. 660–671, 2016, doi: 10.1016/j.ijheatmasstransfer.2016.04.014.
    2. 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.
    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 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
    6. 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., in Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014. , Cham: Springer International Publishing, 2016, pp. 209--227. doi: 10.1007/978-3-319-33507-0_8.
    7. P. Bastian et al., Advances Concerning Multiscale Methods and Uncertainty Quantification in “EXA-DUNE in Software for Exascale Computing -- SPPEXA 2013--2015.” Springer, 2016. doi: 10.1007/978-3-319-40528-5_2.
    8. P. Bastian et al., Hardware-Based Efficiency Advances in the EXA-DUNE Project in “Software for Exascale Computing -- SPPEXA 2013--2015.” Springer, 2016. doi: 10.1007/978-3-319-40528-5_1.
    9. 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.
    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. 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.
    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, Sep. 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 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.
    16. 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.
    17. 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.
    18. F. D. Brunner, W. P. M. H. Heemels, and F. Allgöwer, “γ-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.
    19. 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.
    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, 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.

  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. 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. J. Escher and C. Lienstromberg, “Finite-time singularities of solutions to microelectromechanical systems with general permittivity,” Ann. Mat. Pura Appl. (4), vol. 195, no. 6, Art. no. 6, 2016, doi: 10.1007/s10231-016-0549-8.
    4. J. Escher and C. Lienstromberg, “A qualitative analysis of solutions to microelectromechanical systems with curvature and nonlinear permittivity profile,” Comm. Partial Differential Equations, vol. 41, no. 1, Art. no. 1, 2016, doi: 10.1080/03605302.2015.1105259.
    5. 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. [Online]. Available: http://www.ircobi.org/wordpress/downloads/irc16/pdf-files/71.pdf
    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 J. Contr. Optim., 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,” in 55th IEEE Conf. Decision and Control, in 55th IEEE Conf. Decision and Control. 2016, pp. 7264–7269. doi: 10.1109/CDC.2016.7799390.
    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, Sep. 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, “Flow-Based Temporal Selection for Interactive Volume Visualization,” Computer Graphics Forum, p. 11, 2016, doi: 10.1111/cgf.13070.
    14. 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.
    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,” in 55th IEEE Conf. Decision and Control, in 55th IEEE Conf. Decision and Control. 2016, pp. 5238–5244. doi: 10.1109/CDC.2016.7799071.
    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. 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.
    2. 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.
    3. 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
    4. 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.
    5. 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. C. Lienstromberg, “On qualitative properties of solutions to microelectromechanical systems with general permittivity,” Monatsh. Math., vol. 179, no. 4, Art. no. 4, 2016, doi: 10.1007/s00605-015-0744-5.
    2. 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.
    3. C. Luo and W. Ehlers, “A three-dimensional model of hydraulic fracturing,” PAMM, vol. 16, pp. 465--466, 2016, doi: 10.1002/pamm.201610221.
    4. 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, 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), in 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE, 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.

  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. doi: 10.1109/FPGA4GPC.2016.7518529.
    3. S. M. Najmabadi, Z. Wang, Y. Baroud, and S. Simon, Online bandwidth reduction using dynamic partial reconfiguration. 2016. doi: 10.1109/FCCM.2016.49.
    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. H. Nguyen Tien, C. W. Scherer, J. M. A. Scherpen, and V. Müller, “Linear Parameter Varying Control of Doubly Fed Induction Machines,” IEEE Trans. Ind. Electron., vol. 63, no. 1, Art. no. 1, 2016, doi: 10.1109/TIE.2015.2465895.
    6. 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.
    7. 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, Oct. 2016, doi: 10.1093/imamat/hxw023.
    4. 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.
    5. 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.

  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 $H_ınfty$-synthesis for 2D systems (special issue JCW),” Syst. Control Lett., vol. 95, pp. 25–35, 2016, doi: 10.1016/j.sysconle.2016.02.011.
    3. 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.
    4. 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.
    5. 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.
    6. M. Schneider, B. Flemisch, and R. Helmig, “Monotone nonlinear finite‐volume method for nonisothermal two‐phase two‐component flow in porous media,” International Journal for Numerical Methods in Fluids, vol. 84, no. 6, Art. no. 6, 2016, doi: 10.1002/fld.4352.
    7. 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
    8. 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.
    9. 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.
    10. 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.
    11. A. V. Shapeev, “Moment tensor potentials: A class of systematically improvable interatomic potentials,” Multiscale Modeling & Simulation, vol. 14, no. 3, Art. no. 3, 2016.
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