Publications 2019

  1. B

    1. T. L. Bauer, P. C. F. Buchholz, and J. Pleiss, “The modular structure of $\upalpha$/$\upbeta$-hydrolases,” The FEBS Journal, vol. 287, no. 5, Art. no. 5, 2019, doi: 10.1111/febs.15071.
    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, no. 12, Art. no. 12, 2019, doi: 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.
  2. C

    1. 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. D

    1. D. Driess, S. Schmitt, and M. Toussaint, “Active Inverse Model Learning with Error and Reachable Set Estimates.,” 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, 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. 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.
    3. F. S. Göküzüm, L. T. K. Nguyen, and M.-A. Keip, “An Artificial Neural Network Based Solution Scheme for Periodic Computational Homogenization of Electrostatic Problems,” Mathematical and Computational Applications, vol. 24, no. 2, Art. no. 2, 2019, doi: 10.3390/mca24020040.
    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, 2019, doi: 10.1002/pamm.201900271.
  7. J

    1. B. J, H. C, P. J, and H. N, “Thermophysical properties of glyceline-water mixtures investigated by molecular modelling.,” Phys Chem Chem Phys, vol. 21, pp. 6467–6476, 2019.
  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, 1850044, Art. no. 1, 1850044, 2019, doi: https://doi.org/10.1142/S2591728518500445.
  9. L

    1. 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, Cape Town, South Africa, 2019, pp. 304–307, doi: 10.1201/9780429426506-52.
  10. M

    1. 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), 2019, pp. 251–255, doi: 10.1109/VISUAL.2019.8933606.
  11. R

    1. T. Ricken and L. Lambers, “On computational approaches of liver lobule function and perfusion simulation,” GAMM-Mitteilungen, vol. 42, no. 4, Art. no. 4, 2019, doi: 10.1002/gamm.201900016.
    2. 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.
    3. A. Romer, J. Berberich, J. Köhler, and F. Allgöwer, “One-shot verification of dissipativity properties from input--output data,” IEEE Control Systems Letters, vol. 3, no. 3, Art. no. 3, 2019.
    4. A. Romer, S. Trimpe, and F. Allgöwer, “Data-driven inference of passivity properties via Gaussian process optimization,” in 2019 18th European Control Conference (ECC), 2019, pp. 29--35.
  12. 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. 130, no. 2, Art. no. 2, 2019.
  13. T

    1. 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,” Proceedings of the Royal Society B, vol. 286, no. 1903, Art. no. 1903, 2019.
  14. X

    1. X. Xu, J. Range, G. Gygli, and J. Pleiss, “Analysis of thermophysical properties of deep eutectic solvents by data integration,” J Chem Eng Data, vol. 65, pp. 1172–1179, 2019.
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