On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation
K. Bieker, B. Gebken, S. Peitz, IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (2022) 7797–7808.

Tensor-based computation of metastable and coherent sets
F. Nüske, P. Gelß, S. Klus, C. Clementi, Physica D: Nonlinear Phenomena (2021).

Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry
S. Klus, P. Gelß, F. Nüske, F. Noé, Machine Learning: Science and Technology (2021).

Electrophysiological signatures of dedifferentiation differ between fit and less fit older adults
C. Goelz, K. Mora, J.K. Stroehlein, F.K. Haase, M. Dellnitz, C. Reinsberger, S. Vieluf, Cognitive Neurodynamics (2021).

Derivative-Free Multiobjective Trust Region Descent Method Using Radial  Basis Function Surrogate Models
M.B. Berkemeier, S. Peitz, Mathematical and Computational Applications 26 (2021).

Spectral Properties of Effective Dynamics from Conditional Expectations
F. Nüske, P. Koltai, L. Boninsegna, C. Clementi, Entropy (2021).

An efficient descent method for locally Lipschitz multiobjective optimization problems
B. Gebken, S. Peitz, Journal of Optimization Theory and Applications 188 (2021) 696–723.

Inverse multiobjective optimization: Inferring decision criteria from data
B. Gebken, S. Peitz, Journal of Global Optimization 80 (2021) 3–29.

The Computation and Analysis of Invariant Sets of Infinite-Dimensional Systems
R. Gerlach, The Computation and Analysis of Invariant Sets of Infinite-Dimensional Systems, 2021.

Explicit multiobjective model predictive control for nonlinear systems  with symmetries
S. Ober-Blöbaum, S. Peitz, International Journal of Robust and Nonlinear Control 31(2) (2021) 380–403.

Symmetry in Optimal Control: A Multiobjective Model Predictive Control Approach
K. Flaßkamp, S. Ober-Blöbaum, S. Peitz, in: O. Junge, O. Schütze, G. Froyland, S. Ober-Blöbaum, K. Padberg-Gehle (Eds.), Advances in Dynamics, Optimization and Computation, Springer, Cham, 2020.

Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator
S. Klus, F. Nüske, B. Hamzi, Entropy (2020).

Pareto Explorer: a global/local exploration tool for many-objective optimization problems
O. Schütze, O. Cuate, A. Martín, S. Peitz, M. Dellnitz, Engineering Optimization 52 (2020) 832–855.

Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
S. Klus, F. Nüske, S. Peitz, J.-H. Niemann, C. Clementi, C. Schütte, Physica D: Nonlinear Phenomena 406 (2020).

Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator
S. Peitz, S. Klus, in: Lecture Notes in Control and Information Sciences, Springer, Cham, 2020, pp. 257–282.

Deep model predictive flow control with limited sensor data and online learning
K. Bieker, S. Peitz, S.L. Brunton, J.N. Kutz, M. Dellnitz, Theoretical and Computational Fluid Dynamics 34 (2020) 577–591.

Data-Driven Model Predictive Control using Interpolated Koopman  Generators
S. Peitz, S.E. Otto, C.W. Rowley, SIAM Journal on Applied Dynamical Systems 19 (2020) 2162–2193.

A Set-Oriented Path Following Method for the Approximation of Parameter Dependent Attractors
R. Gerlach, A. Ziessler, B. Eckhardt, M. Dellnitz, SIAM Journal on Applied Dynamical Systems (2020) 705–723.

Explicit Multi-objective Model Predictive Control for Nonlinear Systems  Under Uncertainty
C.I. Hernández Castellanos, S. Ober-Blöbaum, S. Peitz, International Journal of Robust and Nonlinear Control 30(17) (2020) 7593–7618.

The Approximation of Invariant Sets in Infinite Dimensional Dynamical Systems
R. Gerlach, A. Ziessler, in: O. Junge, O. Schütze, S. Ober-Blöbaum, K. Padberg-Gehle (Eds.), Advances in Dynamics, Optimization and Computation, Springer International Publishing, Cham, 2020, pp. 66–85.

On the equivariance properties of self-adjoint matrices
M. Dellnitz, B. Gebken, R. Gerlach, S. Klus, Dynamical Systems 35 (2020) 197–215.

Coarse-graining molecular systems by spectral matching
F. Nüske, L. Boninsegna, C. Clementi, The Journal of Chemical Physics (2019).

Continuous relaxations for the traveling salesman problem
T. Sahai, A. Ziessler, S. Klus, M. Dellnitz, Nonlinear Dynamics (2019).

Koopman operator-based model reduction for switched-system control of PDEs
S. Peitz, S. Klus, Automatica 106 (2019) 184–191.

On the hierarchical structure of Pareto critical sets
B. Gebken, S. Peitz, M. Dellnitz, Journal of Global Optimization 73 (2019) 891–913.

Finite-Control-Set Model Predictive Control for a Permanent Magnet Synchronous Motor Application with Online Least Squares System Identification
S. Hanke, S. Peitz, O. Wallscheid, J. Böcker, M. Dellnitz, in: 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 2019.

Revealing the intrinsic geometry of finite dimensional invariant sets of  infinite dimensional dynamical systems
R. Gerlach, P. Koltai, M. Dellnitz, ArXiv:1902.08824 (2019).

The Numerical Computation of Unstable Manifolds for Infinite Dimensional Dynamical Systems by Embedding Techniques
A. Ziessler, M. Dellnitz, R. Gerlach, SIAM Journal on Applied Dynamical Systems 18 (2019) 1265–1292.

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

Rapid Calculation of Molecular Kinetics Using Compressed Sensing
F. Litzinger, L. Boninsegna, H. Wu, F. Nüske, R. Patel, R. Baraniuk, F. Noé, C. Clementi, Journal of Chemical Theory and Computation (2018) 2771–2783.

Data-Driven Model Reduction and Transfer Operator Approximation
S. Klus, F. Nüske, P. Koltai, H. Wu, I. Kevrekidis, C. Schütte, F. Noé, Journal of Nonlinear Science (2018) 985–1010.

Sparse learning of stochastic dynamical equations
L. Boninsegna, F. Nüske, C. Clementi, The Journal of Chemical Physics (2018).

Quantitative comparison of adaptive sampling methods for protein dynamics
E. Hruska, J.R. Abella, F. Nüske, L.E. Kavraki, C. Clementi, The Journal of Chemical Physics (2018).

A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems
B. Gebken, S. Peitz, M. Dellnitz, in: Numerical and Evolutionary Optimization – NEO 2017, Cham, 2018.

A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction
S. Peitz, M. Dellnitz, Mathematical and Computational Applications 23 (2018).

Set-Oriented Multiobjective Optimal Control of PDEs Using Proper Orthogonal Decomposition
D. Beermann, M. Dellnitz, S. Peitz, S. Volkwein, in: Reduced-Order Modeling (ROM) for Simulation and Optimization, Cham, 2018, pp. 47–72.

Tensor-based dynamic mode decomposition
S. Klus, P. Gelß, S. Peitz, C. Schütte, Nonlinearity 31 (2018) 3359–3380.

POD-based multiobjective optimal control of PDEs with non-smooth objectives
D. Beermann, M. Dellnitz, S. Peitz, S. Volkwein, in: PAMM, 2018, pp. 51–54.

Improved Neural Control of Movements Manifests in Expertise-Related Differences in Force Output and Brain Network Dynamics
C. Gölz, C. Voelcker-Rehage, K. Mora, E.-M. Reuter, B. Godde, M. Dellnitz, C. Reinsberger, S. Vieluf, Frontiers in Physiology (2018).

Age- and Expertise-Related Differences of Sensorimotor Network Dynamics during Force Control
S. Vieluf, K. Mora, C. Gölz, E.-M. Reuter, B. Godde, M. Dellnitz, C. Reinsberger, C. Voelcker-Rehage, Neuroscience (2018) 203–213.

Transition Manifolds of Complex Metastable Systems
A. Bittracher, P. Koltai, S. Klus, R. Banisch, M. Dellnitz, C. Schütte, Journal of Nonlinear Science 28 (2018) 471–512.

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

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

Multiobjective Optimal Control Methods for the Navier-Stokes Equations Using Reduced Order Modeling
S. Peitz, S. Ober-Blöbaum, M. Dellnitz, Acta Applicandae Mathematicae 161 (2018) 171–199.

Markov state models from short non-equilibrium simulations—Analysis and correction of estimation bias
F. Nüske, H. Wu, J.-H. Prinz, C. Wehmeyer, C. Clementi, F. Noé, The Journal of Chemical Physics (2017).

Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations
H. Wu, F. Nüske, F. Paul, S. Klus, P. Koltai, F. Noé, The Journal of Chemical Physics (2017).

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

Sensing and control in symmetric networks
M. Dellnitz, S. Klus, Dynamical Systems (2017) 61–79.

A Set-Oriented Numerical Approach for Dynamical Systems with Parameter Uncertainty
M. Dellnitz, S. Klus, A. Ziessler, SIAM Journal on Applied Dynamical Systems (2017) 120–138.

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).
S. Peitz, K. Schäfer, S. Ober-Blöbaum, J. Eckstein, U. Köhler, M. Dellnitz, IFAC-PapersOnLine (2017) 8674–8679.