S. Klus et al., “Data-Driven Model Reduction and Transfer Operator Approximation,” Journal of Nonlinear Science, pp. 985–1010, 2018.
LibreCat: /record/21941
DOI: 10.1007/s00332-017-9437-7

L. Boninsegna, F. Nüske, and C. Clementi, “Sparse learning of stochastic dynamical equations,” The Journal of Chemical Physics, 2018.
LibreCat: /record/21942
DOI: 10.1063/1.5018409

E. Hruska, J. R. Abella, F. Nüske, L. E. Kavraki, and C. Clementi, “Quantitative comparison of adaptive sampling methods for protein dynamics,” The Journal of Chemical Physics, 2018.
LibreCat: /record/21943
DOI: 10.1063/1.5053582

B. Gebken, S. Peitz, and M. Dellnitz, “A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems,” in Numerical and Evolutionary Optimization – NEO 2017, 2018.
LibreCat: /record/8750
DOI: 10.1007/978-3-319-96104-0_2

S. Peitz and M. Dellnitz, “A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction,” Mathematical and Computational Applications, vol. 23, no. 2, 2018.
LibreCat: /record/8751
DOI: 10.3390/mca23020030

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

S. Klus, P. Gelß, S. Peitz, and C. Schütte, “Tensor-based dynamic mode decomposition,” Nonlinearity, vol. 31, no. 7, pp. 3359–3380, 2018.
LibreCat: /record/8755
DOI: 10.1088/1361-6544/aabc8f

D. Beermann, M. Dellnitz, S. Peitz, and S. Volkwein, “POD-based multiobjective optimal control of PDEs with non-smooth objectives,” in PAMM, 2018, pp. 51–54.
LibreCat: /record/8757
DOI: 10.1002/pamm.201710015

C. Gölz et al., “Improved Neural Control of Movements Manifests in Expertise-Related Differences in Force Output and Brain Network Dynamics,” Frontiers in Physiology, 2018.
LibreCat: /record/16713
DOI: 10.3389/fphys.2018.01540

S. Vieluf et al., “Age- and Expertise-Related Differences of Sensorimotor Network Dynamics during Force Control,” Neuroscience, pp. 203–213, 2018.
LibreCat: /record/16714
DOI: 10.1016/j.neuroscience.2018.07.025

A. Bittracher, P. Koltai, S. Klus, R. Banisch, M. Dellnitz, and C. Schütte, “Transition Manifolds of Complex Metastable Systems,” Journal of Nonlinear Science, vol. 28, pp. 471–512, 2018.
LibreCat: /record/16715
DOI: 10.1007/s00332-017-9415-0

S. Peitz, “Controlling nonlinear PDEs using low-dimensional bilinear approximations  obtained from data,” arXiv:1801.06419. 2018.
LibreCat: /record/16292

S. Klus, S. Peitz, and I. Schuster, “Analyzing high-dimensional time-series data using kernel transfer  operator eigenfunctions,” arXiv:1805.10118. 2018.
LibreCat: /record/16293

S. Peitz, S. Ober-Blöbaum, and M. Dellnitz, “Multiobjective Optimal Control Methods for the Navier-Stokes Equations Using Reduced Order Modeling,”10.1007/s10440-018-0209-7__Acta Applicandae Mathematicae, vol. 161, no. 1, pp. 171–199, 2018, doi:10.1007/s10440-018-0209-7 .
LibreCat: /record/8753
DOI: 10.1007/s10440-018-0209-7

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

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

S. Peitz and M. Dellnitz, “Gradient-Based Multiobjective Optimization with Uncertainties,” in NEO 2016, 2017, pp. 159–182.
LibreCat: /record/8752
DOI: 10.1007/978-3-319-64063-1_7

M. Dellnitz and S. Klus, “Sensing and control in symmetric networks,” Dynamical Systems, pp. 61–79, 2017.
LibreCat: /record/16540
DOI: 10.1080/14689367.2016.1215410

M. Dellnitz, S. Klus, and A. Ziessler, “A Set-Oriented Numerical Approach for Dynamical Systems with Parameter Uncertainty,” SIAM Journal on Applied Dynamical Systems, pp. 120–138, 2017.
LibreCat: /record/16581
DOI: 10.1137/16m1072735

S. Peitz, K. Schäfer, S. Ober-Blöbaum, J. Eckstein, U. Köhler, and M. Dellnitz, “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, pp. 8674–8679, 2017.
LibreCat: /record/16657
DOI: 10.1016/j.ifacol.2017.08.1526