Flaßkamp, K., Ober-Blöbaum, S., &; Peitz, S. (2020). Symmetry in Optimal Control: A Multiobjective Model Predictive Control Approach. In O. Junge, O. Schütze, G. Froyland, S. Ober-Blöbaum, &; K. Padberg-Gehle (Eds.), Advances in Dynamics, Optimization and Computation. Cham: Springer.https://doi.org/10.1007/978-3-030-51264-4_9 
LibreCat: /record/17411
DOI: 10.1007/978-3-030-51264-4_9

Klus, S., Nüske, F., &; Hamzi, B. (2020). Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator. Entropy.https://doi.org/10.3390/e22070722 
LibreCat: /record/21819
DOI: 10.3390/e22070722

Schütze, O., Cuate, O., Martín, A., Peitz, S., &; Dellnitz, M. (2020). Pareto Explorer: a global/local exploration tool for many-objective optimization problems. Engineering Optimization, 52(5), 832–855.https://doi.org/10.1080/0305215x.2019.1617286 
LibreCat: /record/10596
DOI: 10.1080/0305215x.2019.1617286

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

Peitz, S., Otto, S. E., &; Rowley, C. W. (2020). Data-Driven Model Predictive Control using Interpolated Koopman  Generators. SIAM Journal on Applied Dynamical Systems, 19(3), 2162–2193.https://doi.org/10.1137/20M1325678 
LibreCat: /record/16309
DOI: 10.1137/20M1325678

Gerlach, R., Ziessler, A., Eckhardt, B., &; Dellnitz, M. (2020). A Set-Oriented Path Following Method for the Approximation of Parameter Dependent Attractors. SIAM Journal on Applied Dynamical Systems, 705–723.https://doi.org/10.1137/19m1247139 
LibreCat: /record/16710
DOI: 10.1137/19m1247139

Hernández Castellanos, C. I., Ober-Blöbaum, S., &; Peitz, S. (2020). Explicit Multi-objective Model Predictive Control for Nonlinear Systems  Under Uncertainty. International Journal of Robust and Nonlinear Control, 30(17), 7593–7618.https://doi.org/10.1002/rnc.5197 
LibreCat: /record/16297
DOI: 10.1002/rnc.5197

Gerlach, R., &; Ziessler, A. (2020). The Approximation of Invariant Sets in Infinite Dimensional Dynamical Systems. In O. Junge, O. Schütze, S. Ober-Blöbaum, &; K. Padberg-Gehle (Eds.), Advances in Dynamics, Optimization and Computation (Vol. 304, pp. 66–85). Springer International Publishing.https://doi.org/10.1007/978-3-030-51264-4_3 
LibreCat: /record/17994
DOI: 10.1007/978-3-030-51264-4_3

Dellnitz, M., Gebken, B., Gerlach, R., &; Klus, S. (2020). On the equivariance properties of self-adjoint matrices. Dynamical Systems, 35(2), 197–215.https://doi.org/10.1080/14689367.2019.1661355 
LibreCat: /record/16712
DOI: 10.1080/14689367.2019.1661355

Nüske, F., Boninsegna, L., &; Clementi, C. (2019). Coarse-graining molecular systems by spectral matching. The Journal of Chemical Physics.https://doi.org/10.1063/1.5100131 
LibreCat: /record/21944
DOI: 10.1063/1.5100131

Sahai, T., Ziessler, A., Klus, S., &; Dellnitz, M. (2019). Continuous relaxations for the traveling salesman problem. Nonlinear Dynamics.https://doi.org/10.1007/s11071-019-05092-5 
LibreCat: /record/16709
DOI: 10.1007/s11071-019-05092-5

Peitz, S., &; Klus, S. (2019). Koopman operator-based model reduction for switched-system control of PDEs. Automatica, 106, 184–191.https://doi.org/10.1016/j.automatica.2019.05.016 
LibreCat: /record/10593
DOI: 10.1016/j.automatica.2019.05.016

Gebken, B., Peitz, S., &; Dellnitz, M. (2019). On the hierarchical structure of Pareto critical sets. Journal of Global Optimization, 73(4), 891–913.https://doi.org/10.1007/s10898-019-00737-6 
LibreCat: /record/10595
DOI: 10.1007/s10898-019-00737-6

Hanke, S., Peitz, S., Wallscheid, O., Böcker, J., &; Dellnitz, M. (2019). Finite-Control-Set Model Predictive Control for a Permanent Magnet Synchronous Motor Application with Online Least Squares System Identification. In 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE).https://doi.org/10.1109/precede.2019.8753313 
LibreCat: /record/10597
DOI: 10.1109/precede.2019.8753313

Gerlach, R., Koltai, P., &; Dellnitz, M. (2019). Revealing the intrinsic geometry of finite dimensional invariant sets of  infinite dimensional dynamical systems. In arXiv:1902.08824.
LibreCat: /record/16711

Ziessler, A., Dellnitz, M., &; Gerlach, R. (2019). The Numerical Computation of Unstable Manifolds for Infinite Dimensional Dynamical Systems by Embedding Techniques. SIAM Journal on Applied Dynamical Systems, 18(3), 1265–1292.https://doi.org/10.1137/18m1204395 
LibreCat: /record/16708
DOI: 10.1137/18m1204395

Hanke, S., Peitz, S., Wallscheid, O., Klus, S., Böcker, J., &; Dellnitz, M. (2018). Koopman Operator-Based Finite-Control-Set Model Predictive Control for  Electrical Drives. ArXiv:1804.00854.
LibreCat: /record/21634

Litzinger, F., Boninsegna, L., Wu, H., Nüske, F., Patel, R., Baraniuk, R.,https://doi.org/10.1021/acs.jctc.8b00089__Clementi, C. (2018). Rapid Calculation of Molecular Kinetics Using Compressed Sensing. Journal of Chemical Theory and Computation, 2771–2783.https://doi.org/10.1021/acs.jctc.8b00089 
LibreCat: /record/21940
DOI: 10.1021/acs.jctc.8b00089