S. Klus et al., “Data-Driven Model Reduction and Transfer Operator Approximation,” Journal of Nonlinear Science, pp. 985–1010, 2018.

L. Boninsegna, F. Nüske, and C. Clementi, “Sparse learning of stochastic dynamical equations,” The Journal of Chemical Physics, 2018.

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.

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.

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.

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.

S. Klus, P. Gelß, S. Peitz, and C. Schütte, “Tensor-based dynamic mode decomposition,” Nonlinearity, vol. 31, no. 7, pp. 3359–3380, 2018.

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.

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.

S. Vieluf et al., “Age- and Expertise-Related Differences of Sensorimotor Network Dynamics during Force Control,” Neuroscience, pp. 203–213, 2018.

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.

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

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

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 .

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.

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.

S. Peitz and M. Dellnitz, “Gradient-Based Multiobjective Optimization with Uncertainties,” in NEO 2016, 2017, pp. 159–182.

M. Dellnitz and S. Klus, “Sensing and control in symmetric networks,” Dynamical Systems, pp. 61–79, 2017.

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.

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.