Publications 2019

  1. B

    1. G. Baggio, S. Zampieri, and C. W. Scherer, “Gramian Optimization with Input-Power Constraints,” in 58th IEEE Conf. Decision and Control, in 58th IEEE Conf. Decision and Control. 2019, pp. 5686–5691. doi: 10.1109/CDC40024.2019.9029169.
    2. J. Baz, C. Held, J. Pleiss, and N. Hansen, “Thermophysical properties of glyceline-water mixtures investigated by molecular modelling.,” Phys Chem Chem Phys, vol. 21, pp. 6467–6476, 2019, doi: https://doi.org/10.1039/C9CP00036D.
    3. A. Beck, D. Flad, and C.-D. Munz, “Deep neural networks for data-driven LES closure models.,” J. Comput. Physics, vol. 398, 2019, [Online]. Available: http://dblp.uni-trier.de/db/journals/jcphy/jcphy398.html#BeckFM19
    4. L. Bilke, B. Flemisch, T. Kalbacher, O. Kolditz, R. Helmig, and T. Nagel, “Development of Open-Source Porous Media Simulators: Principles and Experiences,” Transport in Porous Media, vol. 130, no. 1, Art. no. 1, Oct. 2019, doi: 10.1007/s11242-019-01310-1.
    5. D. Brodbeck et al., “Asymmetric Carboxycyanation of Aldehydes by Cooperative AlF-/Onium Salt Catalysts: from Cyanoformate to KCN as Cyanide Source,” Chem. Eur. J., vol. 25, pp. 1515–1524, 2019, doi: 10.1002/chem.201804388.

  2. 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. D. Carral, C. S. Sarap, K. Liu, A. Radenovic, and M. Fyta, “2D MoS2 nanopores: ionic current blockade height for clustering DNA events,” 2D Materials, vol. 6, no. 4, Art. no. 4, 2019.
    3. X. Chu, G. Yang, S. Pandey, and B. Weigand, “Direct numerical simulation of convective heat transfer in porous media,” International Journal of Heat and Mass Transfer, vol. 133, pp. 11--20, Apr. 2019, doi: 10.1016/j.ijheatmasstransfer.2018.11.172.

  3. D

    1. A. Denzel, B. Haasdonk, and J. Kästner, “Gaussian Process Regression for Minimum Energy Path Optimization and Transition State Search,” J. Phys. Chem. A, vol. 123, no. 44, Art. no. 44, 2019, doi: 10.1021/acs.jpca.9b08239.
    2. D. Driess, S. Schmitt, and M. Toussaint, “Active Inverse Model Learning with Error and Reachable Set Estimates.,” in IROS, in IROS. 2019, pp. 1826--1833.

  4. E

    1. I. Eisenkolb et al., “Kinetic modeling of biocatalytic reactions: A workflow for model calibration, selection and validation using Bayesian statistics.,” AIChE J, vol. 66, p. 16866, 2019.

  5. F

    1. V. Ferrario, M. Fischer, Y. Zhu, and J. Pleiss, “Modelling of substrate access and substrate binding to cephalosporin acylases,” Scientific Reports, vol. 9, no. 1, Art. no. 1, Aug. 2019, doi: 10.1038/s41598-019-48849-z.

  6. G

    1. E.-M. Geissen, J. Hasenauer, and N. E. Radde, “Inference of finite mixture models and the effect of binning,” Statistical applications in genetics and molecular biology, vol. 18, no. 4, Art. no. 4, 2019.
    2. D. Gläser, B. Flemisch, R. Helmig, and H. Class, “A hybrid-dimensional discrete fracture model for non-isothermal two-phase flow in fractured porous media,” GEM - International Journal on Geomathematics, vol. 10, no. 1, Art. no. 1, 2019, doi: 10.1007/s13137-019-0116-8.
    3. B. Grabowski et al., “Ab initio vibrational free energies including anharmonicity for multicomponent alloys,” npj Computational Materials, vol. 5, no. 1, Art. no. 1, 2019.
    4. F. S. Göküzüm, L. T. K. Nguyen, and M.-A. Keip, “An Artificial Neural Network based Solution Scheme to periodic Homogenization,” PAMM, vol. 19, no. 1, Art. no. 1, Nov. 2019, doi: 10.1002/pamm.201900271.

  7. H

    1. L. Harzenetter, U. Breitenbücher, F. Leymann, K. Saatkamp, B. Weder, and M. Wurster, “Automated Generation of Management Workflows for Applications Based on Deployment Models,” in 2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC), in 2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC). Oct. 2019, pp. 216–225. doi: 10.1109/EDOC.2019.00034.
    2. M. Hertneck, S. Linsenmayer, and F. Allgöwer, “Nonlinear Dynamic Periodic Event-Triggered Control with Robustness to Packet Loss Based on Non-Monotonic Lyapunov Functions,” in Proc. 58th IEEE Conf. Decision and Control (CDC), in Proc. 58th IEEE Conf. Decision and Control (CDC). Nice, France, 2019, pp. 1680–1685. doi: 10.1109/CDC40024.2019.9029770.
    3. T. Holicki and C. W. Scherer, “Stability Analysis and Output-Feedback Synthesis of Hybrid Systems Affected by Piecewise Constant Parameters via Dynamic Resetting Scalings,” Nonlinear Anal. Hybri., vol. 34, pp. 179–208, 2019, doi: https://doi.org/10.1016/j.nahs.2019.06.003.
    4. T. Holicki and C. W. Scherer, “A Homotopy Approach for Robust Output-Feedback Synthesis,” in Proc. 27th. Med. Conf. Control Autom., in Proc. 27th. Med. Conf. Control Autom. 2019, pp. 87–93. doi: 10.1109/MED.2019.8798536.
    5. M. Hopp and J. Gross, “Thermal Conductivity from Entropy Scaling: A Group-Contribution Method,” Industrial & Engineering Chemistry Research, vol. 58, no. 44, Art. no. 44, Oct. 2019, doi: 10.1021/acs.iecr.9b04289.

  8. K

    1. T. Kuhn, J. Dürrwächter, F. Meyer, A. Beck, C. Rohde, and C.-D. Munz, “Uncertainty quantification for direct aeroacoustic simulations of cavity flows,” J. Theor. Comput. Acoust., vol. 27, no. 1, Art. no. 1, 2019, doi: https://doi.org/10.1142/S2591728518500445.
    2. M. Köppel et al., “Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario,” Computational Geosciences, vol. 23, pp. 339–354, 2019, doi: 10.1007/s10596-018-9785-x.
    3. M. Köppel et al., “Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario,” Computational Geosciences, vol. 23, no. 2, Art. no. 2, Apr. 2019, doi: 10.1007/s10596-018-9785-x.

  9. L

    1. L. Lambers, T. Ricken, and M. König, “Model Order Reduction (MOR) of Function--Perfusion--Growth Simulation in the Human Fatty Liver via Artificial Neural Network (ANN),” PAMM, vol. 19, no. 1, Art. no. 1, 2019, doi: 10.1002/pamm.201900429.
    2. L. Lambers, T. Ricken, and M. König, “A multiscale and multiphase model for the description of function-perfusion processes in the human liver,” in Advances in Engineering Materials, Structures and Systems : Innovations, Mechanics and Applications : Proceedings of the 7th International Conference on Structural Engineering, Mechanics and Computation (SEMC 2019), September 2-4, 2019, Cape Town, South Africa, in Advances in Engineering Materials, Structures and Systems : Innovations, Mechanics and Applications : Proceedings of the 7th International Conference on Structural Engineering, Mechanics and Computation (SEMC 2019), September 2-4, 2019, Cape Town, South Africa. CRC Press, 2019, pp. 304–307. doi: 10.1201/9780429426506-52.

  10. M

    1. M. N. Markmeyer, T. Lamberts, J. Meisner, and J. Kästner, “HOCO formation in astrochemical environments by radical-induced H-abstraction from formic acid,” Mon. Not. R. Astron. Soc., vol. 482, no. 1, Art. no. 1, 2019, doi: 10.1093/mnras/sty2620.
    2. T. Martin and F. Allgöwer, “Nonlinearity Measures for Data-Driven System Analysis and Control,” in Proc. 58th IEEE Conf. Decision and Control (CDC), in Proc. 58th IEEE Conf. Decision and Control (CDC). Nice, France, 2019, pp. 3605–3610. doi: 10.1109/CDC40024.2019.9029804.
    3. J. Meisner, I. Kamp, W.-F. Thi, and J. Kästner, “The role of atom tunneling in gas-phase reactions in planet-forming disks,” Astron. Astrophys., vol. 627, p. A45, 2019, doi: 10.1051/0004-6361/201834974.
    4. T. Munz, M. Burch, T. van Benthem, Y. Poels, F. Beck, and D. Weiskopf, “Overlap-Free Drawing of Generalized Pythagoras Trees for Hierarchy Visualization,” in 2019 IEEE Visualization Conference (VIS), in 2019 IEEE Visualization Conference (VIS). Oct. 2019, pp. 251–255. doi: 10.1109/VISUAL.2019.8933606.
    5. T. Munz, L. L. Chuang, S. Pannasch, and D. Weiskopf, “VisME: Visual microsaccades explorer,” Journal of Eye Movement Research, vol. 12, no. 6, Art. no. 6, Dec. 2019, doi: 10.16910/jemr.12.6.5.

  11. O

    1. S. Oladyshkin and W. Nowak, “The connection between Bayesian Inference and Information Theory for model selection, information gain and experimental design,” Entropy, vol. 21, p. 1081, 2019, doi: doi:10.3390/e21111081.

  12. P

    1. P. Partovi-Azar, C. S. Sarap, and M. Fyta, “In silico Complexes of Amino Acids and Diamondoids,” ChemPhysChem, vol. 20, no. 17, Art. no. 17, Jul. 2019, doi: 10.1002/cphc.201900394.

  13. R

    1. J. Reutzsch et al., “Direct Numerical Simulations of Oscillating Liquid Droplets: a Method to Extract Shape Characteristics,” ILASS-Europe 2019, 29th Conference on Liquid Atomization and Spray Systems, vol. Paris, France, 2019.
    2. T. Ricken and L. Lambers, “On computational approaches of liver lobule function and perfusion simulation,” GAMM-Mitteilungen, vol. 42, no. 4, Art. no. 4, May 2019, doi: 10.1002/gamm.201900016.
    3. R. Roddan et al., “The acceptance and kinetic resolution of alpha-methyl substituted aldehydes by norcoclaurine synthases.,” ACS Catal, vol. 9, pp. 9640–9649, 2019.
    4. A. Romer, J. Berberich, J. Köhler, and F. Allgöwer, “One-shot verification of dissipativity properties from input-output data,” IEEE Control Systems Lett., vol. 3, pp. 709–714, 2019, doi: 10.1109/LCSYS.2019.2917162.
    5. A. Romer, S. Trimpe, and F. Allgöwer, “Data-driven inference of passivity properties via Gaussian process optimization,” in 2019 18th European Control Conference (ECC), in 2019 18th European Control Conference (ECC). IEEE, 2019, pp. 29--35.
    6. C. A. Rösinger and C. W. Scherer, “A Scalings Approach to $H_2$-Gain-Scheduling Synthesis without Elimination,” in IFAC-PapersOnLine, in IFAC-PapersOnLine, vol. 52. 2019, pp. 50–57. doi: 10.1016/j.ifacol.2019.12.347.

  14. S

    1. H. Steeb and J. Renner, “Mechanics of Poro-Elastic Media: A Review with Emphasis on Foundational State Variables,” Transport in Porous Media, vol. 120, no. 2, Art. no. 2, 2019, doi: 10.1007/s11242-019-01319-6.

  15. T

    1. A. Terzis et al., “Microscopic velocity field measurements inside a regular porous medium adjacent to a low Reynolds number channel flow,” Physics of Fluids, vol. 31, no. 4, Art. no. 4, Apr. 2019, doi: 10.1063/1.5092169.
    2. G. Tkachev, S. Frey, and T. Ertl, “Local Prediction Models for Spatiotemporal Volume Visualization,” IEEE Transactions on Visualization and Computer Graphics, 2019, doi: 10.1109/TVCG.2019.2961893.
    3. A. Tomalka, O. Röhrle, J. C. Han, T. Pham, A. J. Taberner, and T. Siebert, “Extensive eccentric contractions in intact cardiac trabeculae: revealing compelling differences in contractile behaviour compared to skeletal muscles,” Proc Biol Sci, vol. 286, no. 1903, Art. no. 1903, 2019, doi: 10.1098/rspb.2019.0719.

  16. W

    1. R. Weeber, P. Kreissl, and C. Holm, “Studying the field-controlled change of shape and elasticity of magnetic gels using particle-based simulations,” Archive of Applied Mechanics, vol. 89, no. 1, Art. no. 1, Jan. 2019, doi: 10.1007/s00419-018-1396-4.
    2. R. Weeber, F. Nestler, F. Weik, M. Pippig, D. Potts, and C. Holm, “Accelerating the calculation of dipolar interactions in particle based simulations with open boundary conditions by means of the P2NFFT method,” Journal of Computational Physics, vol. 391, pp. 243--258, Aug. 2019, doi: 10.1016/j.jcp.2019.01.044.

  17. X

    1. S. Xiao, S. Reuschen, G. Köse, S. Oladyshkin, and W. Nowak, “Estimation of small failure probabilities based on thermodynamic integration and parallel tempering,” Mechanical Systems and Signal Processing, vol. 133, p. 106248, Nov. 2019, doi: 10.1016/j.ymssp.2019.106248.

  18. Z

    1. J. Zeman, C. Holm, and J. Smiatek, “The Effect of Small Organic Cosolutes on Water Structure and Dynamics,” Journal of Chemical & Engineering Data, vol. 65, no. 3, Art. no. 3, Aug. 2019, doi: 10.1021/acs.jced.9b00577.
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