Wende, M., &; Kenig, E. (2020). Numerische Simulation von Gravidestillationsapparaten mit Metallschäumen. In Jahrestreffen der ProcessNet-Fachgruppen Fluidverfahrenstechnik, Adsorption und Extraktion. Berchtesgaden.
LibreCat: /record/16248

Szubartowicz, E., &; Schryen, G. (2020). Timing in Information Security: An Event Study on the Impact of Information Security Investment Announcements. Journal of Information System Security, 16(1), 3–31.
LibreCat: /record/16249

Hoyer, B., van Huizen, T., Keijzer, L., Rezaei, S., Rosenkranz, S., &; Westbrock, B. (2020). Gender, competitiveness, and task difficulty: Evidence from the field. Labour Economics.https://doi.org/10.1016/j.labeco.2020.101815 
LibreCat: /record/16273
DOI: 10.1016/j.labeco.2020.101815

Jovanovikj, I., Nagaraj, A., Yigitbas, E., Anjorin, A., Sauer, S., &; Engels, G. (2020). Validating Test Case Migration via Mutation Analysis . In Proceedings of the 1st IEEE/ACM International Conference on Automation of Software Test AST (pp. 31–40). Seoul: ACM.
LibreCat: /record/16274

Nomikos, N., Michailidis, E. T., Trakadas, P., Vouyioukas, D., Karl, H., Martrat, J.,https://doi.org/10.1016/j.vehcom.2020.100250__Voliotis, S. (2020). A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT devices. Vehicular Communications.https://doi.org/10.1016/j.vehcom.2020.100250 
LibreCat: /record/16278
DOI: 10.1016/j.vehcom.2020.100250

Li, F., Yu, D., Yang, H., Yu, J., Karl, H., &; Cheng, X. (2020). Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey. IEEE Wireless Communications, 24–30.https://doi.org/10.1109/mwc.001.1900280 
LibreCat: /record/16280
DOI: 10.1109/mwc.001.1900280

zur Heiden, P., Berendes, C. I., &; Beverungen, D. (2020). Designing City Center Area Recommendation Systems . In Proceedings of the 15th International Conference on Wirtschaftsinformatik. Potsdam.https://doi.org/doi.org/10.30844/wi_2020_e1-heiden 
LibreCat: /record/16285
DOI: doi.org/10.30844/wi_2020_e1-heiden

Klus, S., Nüske, F., Peitz, S., Niemann, J.-H., Clementi, C., &; Schütte, C. (2020). Data-driven approximation of the Koopman generator: Model reduction, system identification, and control. Physica D: Nonlinear Phenomena, 406.https://doi.org/10.1016/j.physd.2020.132416 
LibreCat: /record/16288
DOI: 10.1016/j.physd.2020.132416

Peitz, S., &; Klus, S. (2020). Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator. In Lecture Notes in Control and Information Sciences (Vol. 484, pp. 257–282). Cham: Springer.https://doi.org/10.1007/978-3-030-35713-9_10 
LibreCat: /record/16289
DOI: 10.1007/978-3-030-35713-9_10

Bieker, K., Peitz, S., Brunton, S. L., Kutz, J. N., &; Dellnitz, M. (2020). Deep model predictive flow control with limited sensor data and online learning. Theoretical and Computational Fluid Dynamics, 34, 577–591.https://doi.org/10.1007/s00162-020-00520-4 
LibreCat: /record/16290
DOI: 10.1007/s00162-020-00520-4