Klus, S., Nüske, F., Koltai, P., Wu, H., Kevrekidis, I., Schütte, C., &; Noé, F. (2018). Data-Driven Model Reduction and Transfer Operator Approximation. Journal of Nonlinear Science, 985–1010.https://doi.org/10.1007/s00332-017-9437-7 
LibreCat: /record/21941
DOI: 10.1007/s00332-017-9437-7

Boninsegna, L., Nüske, F., &; Clementi, C. (2018). Sparse learning of stochastic dynamical equations. The Journal of Chemical Physics.https://doi.org/10.1063/1.5018409 
LibreCat: /record/21942
DOI: 10.1063/1.5018409

Hruska, E., Abella, J. R., Nüske, F., Kavraki, L. E., &; Clementi, C. (2018). Quantitative comparison of adaptive sampling methods for protein dynamics. The Journal of Chemical Physics.https://doi.org/10.1063/1.5053582 
LibreCat: /record/21943
DOI: 10.1063/1.5053582

Gebken, B., Peitz, S., &; Dellnitz, M. (2018). A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems. In Numerical and Evolutionary Optimization – NEO 2017. Cham.https://doi.org/10.1007/978-3-319-96104-0_2 
LibreCat: /record/8750
DOI: 10.1007/978-3-319-96104-0_2

Peitz, S., &; Dellnitz, M. (2018). A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction. Mathematical and Computational Applications, 23(2).https://doi.org/10.3390/mca23020030 
LibreCat: /record/8751
DOI: 10.3390/mca23020030

Beermann, D., Dellnitz, M., Peitz, S., &; Volkwein, S. (2018). Set-Oriented Multiobjective Optimal Control of PDEs Using Proper Orthogonal Decomposition. In Reduced-Order Modeling (ROM) for Simulation and Optimization (pp. 47–72). Cham.https://doi.org/10.1007/978-3-319-75319-5_3 
LibreCat: /record/8754
DOI: 10.1007/978-3-319-75319-5_3

Klus, S., Gelß, P., Peitz, S., &; Schütte, C. (2018). Tensor-based dynamic mode decomposition. Nonlinearity, 31(7), 3359–3380.https://doi.org/10.1088/1361-6544/aabc8f 
LibreCat: /record/8755
DOI: 10.1088/1361-6544/aabc8f

Beermann, D., Dellnitz, M., Peitz, S., &; Volkwein, S. (2018). POD-based multiobjective optimal control of PDEs with non-smooth objectives. In PAMM (pp. 51–54).https://doi.org/10.1002/pamm.201710015 
LibreCat: /record/8757
DOI: 10.1002/pamm.201710015

Gölz, C., Voelcker-Rehage, C., Mora, K., Reuter, E.-M., Godde, B., Dellnitz, M.,https://doi.org/10.3389/fphys.2018.01540__Vieluf, S. (2018). Improved Neural Control of Movements Manifests in Expertise-Related Differences in Force Output and Brain Network Dynamics. Frontiers in Physiology.https://doi.org/10.3389/fphys.2018.01540 
LibreCat: /record/16713
DOI: 10.3389/fphys.2018.01540

Vieluf, S., Mora, K., Gölz, C., Reuter, E.-M., Godde, B., Dellnitz, M.,https://doi.org/10.1016/j.neuroscience.2018.07.025__Voelcker-Rehage, C. (2018). Age- and Expertise-Related Differences of Sensorimotor Network Dynamics during Force Control. Neuroscience, 203–213.https://doi.org/10.1016/j.neuroscience.2018.07.025 
LibreCat: /record/16714
DOI: 10.1016/j.neuroscience.2018.07.025

Bittracher, A., Koltai, P., Klus, S., Banisch, R., Dellnitz, M., &; Schütte, C. (2018). Transition Manifolds of Complex Metastable Systems. Journal of Nonlinear Science, 28, 471–512.https://doi.org/10.1007/s00332-017-9415-0 
LibreCat: /record/16715
DOI: 10.1007/s00332-017-9415-0

Peitz, S. (2018). Controlling nonlinear PDEs using low-dimensional bilinear approximations  obtained from data. ArXiv:1801.06419.
LibreCat: /record/16292

Klus, S., Peitz, S., &; Schuster, I. (2018). Analyzing high-dimensional time-series data using kernel transfer  operator eigenfunctions. ArXiv:1805.10118.
LibreCat: /record/16293

Peitz, S., Ober-Blöbaum, S., &; Dellnitz, M. (2018). Multiobjective Optimal Control Methods for the Navier-Stokes Equations Using Reduced Order Modeling. Acta Applicandae Mathematicae, 161(1), 171–199.https://doi.org/10.1007/s10440-018-0209-7 
LibreCat: /record/8753
DOI: 10.1007/s10440-018-0209-7

Nüske, F., Wu, H., Prinz, J.-H., Wehmeyer, C., Clementi, C., &; Noé, F. (2017). Markov state models from short non-equilibrium simulations—Analysis and correction of estimation bias. The Journal of Chemical Physics.https://doi.org/10.1063/1.4976518 
LibreCat: /record/21938
DOI: 10.1063/1.4976518

Wu, H., Nüske, F., Paul, F., Klus, S., Koltai, P., &; Noé, F. (2017). Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations. The Journal of Chemical Physics.https://doi.org/10.1063/1.4979344 
LibreCat: /record/21939
DOI: 10.1063/1.4979344

Peitz, S., &; Dellnitz, M. (2017). Gradient-Based Multiobjective Optimization with Uncertainties. In NEO 2016 (pp. 159–182). Cham.https://doi.org/10.1007/978-3-319-64063-1_7 
LibreCat: /record/8752
DOI: 10.1007/978-3-319-64063-1_7

Dellnitz, M., &; Klus, S. (2017). Sensing and control in symmetric networks. Dynamical Systems, 61–79.https://doi.org/10.1080/14689367.2016.1215410 
LibreCat: /record/16540
DOI: 10.1080/14689367.2016.1215410

Dellnitz, M., Klus, S., &; Ziessler, A. (2017). A Set-Oriented Numerical Approach for Dynamical Systems with Parameter Uncertainty. SIAM Journal on Applied Dynamical Systems, 120–138.https://doi.org/10.1137/16m1072735 
LibreCat: /record/16581
DOI: 10.1137/16m1072735

Peitz, S., Schäfer, K., Ober-Blöbaum, S., Eckstein, J., Köhler, U., &; Dellnitz, M. (2017). A Multiobjective MPC Approach for Autonomously Driven Electric Vehicles * *This research was funded by the German Federal Ministry of Education and Research (BMBF) within the Leading-Edge Cluster Intelligent Technical Systems OstWestfalenLippe (it’s OWL). IFAC-PapersOnLine, 8674–8679.https://doi.org/10.1016/j.ifacol.2017.08.1526 
LibreCat: /record/16657
DOI: 10.1016/j.ifacol.2017.08.1526