B. Gebken, “A note on the convergence of deterministic gradient sampling in nonsmooth optimization,”10.1007/s10589-024-00552-0__Computational Optimization and Applications, 2024, doi:10.1007/s10589-024-00552-0 .

K. Sonntag and S. Peitz, “Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems,”10.1007/s10957-024-02389-3__Journal of Optimization Theory and Applications, 2024, doi:10.1007/s10957-024-02389-3 .

K. Sonntag, B. Gebken, G. Müller, S. Peitz, and S. Volkwein, “A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces,” arXiv:2402.06376. 2024.

C. Bick and S. von der Gracht, “Heteroclinic dynamics in network dynamical systems with higher-order interactions,”10.1093/comnet/cnae009__Journal of Complex Networks, vol. 12, no. 2, 2024, doi:10.1093/comnet/cnae009 .

S. Peitz and K. Bieker, “On the Universal Transformation of Data-Driven Models to Control Systems,”10.1016/j.automatica.2022.110840__Automatica, vol. 149, Art. no. 110840, 2023, doi:10.1016/j.automatica.2022.110840 .

B. Gebken, K. Bieker, and S. Peitz, “On the structure of regularization paths for piecewise differentiable regularization terms,”10.1007/s10898-022-01223-2__Journal of Global Optimization, vol. 85, no. 3, pp. 709–741, 2023, doi:10.1007/s10898-022-01223-2 .

S. von der Gracht, E. Nijholt, and B. Rink, “A parametrisation method for high-order phase reduction in coupled  oscillator networks,” arXiv:2306.03320. .

F. Nüske, S. Peitz, F. Philipp, M. Schaller, and K. Worthmann, “Finite-data error bounds for Koopman-based prediction and control,”10.1007/s00332-022-09862-1__Journal of Nonlinear Science, vol. 33, Art. no. 14, 2023, doi:10.1007/s00332-022-09862-1 .

M. Dellnitz et al., “Efficient time stepping for numerical integration using reinforcement  learning,”10.1137/21M1412682__SIAM Journal on Scientific Computing, vol. 45, no. 2, pp. 9–5, 2023, doi:10.1137/21M1412682 .

S. von der Gracht, E. Nijholt, and B. Rink, “Hypernetworks: Cluster Synchronization Is a Higher-Order Effect,”10.1137/23m1561075__SIAM Journal on Applied Mathematics, vol. 83, no. 6, pp. 2329–2353, 2023, doi:10.1137/23m1561075 .

S. von der Gracht, E. Nijholt, and B. Rink, “Higher order interactions lead to ‘reluctant’ synchrony breaking,” arXiv:2311.17186. 2023.

R. Gerlach, S. von der Gracht, and M. Dellnitz, “On the Dynamical Hierarchy in Gathering Protocols with Circulant  Topologies,” arXiv:2305.06632. .

K. Sonntag and S. Peitz, “Fast Convergence of Inertial Multiobjective Gradient-like Systems with Asymptotic Vanishing Damping,” arXiv:2307.00975. 2023.

M. Bernreuther et al., “Multiobjective Optimization of Non-Smooth PDE-Constrained Problems,” arXiv:2308.01113. 2023.

S. Banholzer, B. Gebken, M. Dellnitz, S. Peitz, and S. Volkwein, “ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical Continuation,” in Non-Smooth and Complementarity-Based Distributed Parameter Systems, H. Michael, H. Roland, K. Christian, U. Michael, and U. Stefan, Eds. Cham: Springer, 2022, pp. 43–76.

S. Peitz, M. Dellnitz, and S. Bannenberg, “Efficient Virtual Design and Testing of Autonomous Vehicles,” in German Success Stories in Industrial Mathematics, vol. 35, H. G. Bock, K.-H. Küfer, P. Maas, A. Milde, and V. Schulz, Eds. Cham: Springer International Publishing, 2022.

S. Klus, F. Nüske, and S. Peitz, “Koopman analysis of quantum systems,”10.1088/1751-8121/ac7d22__Journal of Physics A: Mathematical and Theoretical, vol. 55, no. 31, p. 314002, 2022, doi:10.1088/1751-8121/ac7d22 .

B. Gebken, “Using second-order information in gradient sampling methods for  nonsmooth optimization,” arXiv:2210.04579. 2022.

B. Gebken, Computation and analysis of Pareto critical sets in smooth and nonsmooth multiobjective optimization. 2022.

M. B. Berkemeier and S. Peitz, “Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients,” arXiv:2208.12094. 2022.

K. Bieker, B. Gebken, and S. Peitz, “On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation,”10.1109/TPAMI.2021.3114962__IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 11, pp. 7797–7808, 2022, doi:10.1109/TPAMI.2021.3114962 .

F. Nüske, P. Gelß, S. Klus, and C. Clementi, “Tensor-based computation of metastable and coherent sets,”10.1016/j.physd.2021.133018__Physica D: Nonlinear Phenomena, Art. no. 133018, 2021, doi:10.1016/j.physd.2021.133018 .

S. Klus, P. Gelß, F. Nüske, and F. Noé, “Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry,”10.1088/2632-2153/ac14ad__Machine Learning: Science and Technology, Art. no. 045016, 2021, doi:10.1088/2632-2153/ac14ad .

C. Goelz et al., “Electrophysiological signatures of dedifferentiation differ between fit and less fit older adults,” Cognitive Neurodynamics, 2021.

M. B. Berkemeier and S. Peitz, “Derivative-Free Multiobjective Trust Region Descent Method Using Radial  Basis Function Surrogate Models,” Mathematical and Computational Applications, vol. 26, no. 2, 2021.

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

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

B. Gebken and S. Peitz, “Inverse multiobjective optimization: Inferring decision criteria from data,” Journal of Global Optimization, vol. 80, pp. 3–29, 2021.

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

S. Ober-Blöbaum and S. Peitz, “Explicit multiobjective model predictive control for nonlinear systems  with symmetries,”10.1002/rnc.5281__International Journal of Robust and Nonlinear Control, vol. 31(2), pp. 380–403, 2021, doi:10.1002/rnc.5281 .

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

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

O. Schütze, O. Cuate, A. Martín, S. Peitz, and M. Dellnitz, “Pareto Explorer: a global/local exploration tool for many-objective optimization problems,” Engineering Optimization, vol. 52, no. 5, pp. 832–855, 2020.

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

S. Peitz and S. Klus, “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, Cham: Springer, 2020, pp. 257–282.

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

S. Peitz, S. E. Otto, and C. W. Rowley, “Data-Driven Model Predictive Control using Interpolated Koopman  Generators,” SIAM Journal on Applied Dynamical Systems, vol. 19, no. 3, pp. 2162–2193, 2020.

R. Gerlach, A. Ziessler, B. Eckhardt, and M. Dellnitz, “A Set-Oriented Path Following Method for the Approximation of Parameter Dependent Attractors,”10.1137/19m1247139__SIAM Journal on Applied Dynamical Systems, pp. 705–723, 2020, doi:10.1137/19m1247139 .

C. I. Hernández Castellanos, S. Ober-Blöbaum, and S. Peitz, “Explicit Multi-objective Model Predictive Control for Nonlinear Systems  Under Uncertainty,”10.1002/rnc.5197__International Journal of Robust and Nonlinear Control, vol. 30(17), pp. 7593–7618, 2020, doi:10.1002/rnc.5197 .

R. Gerlach and A. Ziessler, “The Approximation of Invariant Sets in Infinite Dimensional Dynamical Systems,” in Advances in Dynamics, Optimization and Computation, vol. 304, O. Junge, O. Schütze, S. Ober-Blöbaum, and K. Padberg-Gehle, Eds. Cham: Springer International Publishing, 2020, pp. 66–85.

M. Dellnitz, B. Gebken, R. Gerlach, and S. Klus, “On the equivariance properties of self-adjoint matrices,”10.1080/14689367.2019.1661355__Dynamical Systems, vol. 35, no. 2, pp. 197–215, 2020, doi:10.1080/14689367.2019.1661355 .

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

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

S. Peitz and S. Klus, “Koopman operator-based model reduction for switched-system control of PDEs,” Automatica, vol. 106, pp. 184–191, 2019.

B. Gebken, S. Peitz, and M. Dellnitz, “On the hierarchical structure of Pareto critical sets,” Journal of Global Optimization, vol. 73, no. 4, pp. 891–913, 2019.

S. Hanke, S. Peitz, O. Wallscheid, J. Böcker, and M. Dellnitz, “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), 2019.

R. Gerlach, P. Koltai, and M. Dellnitz, “Revealing the intrinsic geometry of finite dimensional invariant sets of  infinite dimensional dynamical systems,” arXiv:1902.08824. 2019.

A. Ziessler, M. Dellnitz, and R. Gerlach, “The Numerical Computation of Unstable Manifolds for Infinite Dimensional Dynamical Systems by Embedding Techniques,”10.1137/18m1204395__SIAM Journal on Applied Dynamical Systems, vol. 18, no. 3, pp. 1265–1292, 2019, doi:10.1137/18m1204395 .

S. Hanke, S. Peitz, O. Wallscheid, S. Klus, J. Böcker, and M. Dellnitz, “Koopman Operator-Based Finite-Control-Set Model Predictive Control for  Electrical Drives,” arXiv:1804.00854. 2018.

F. Litzinger et al., “Rapid Calculation of Molecular Kinetics Using Compressed Sensing,” Journal of Chemical Theory and Computation, pp. 2771–2783, 2018.