Gebken, B. (2024). A note on the convergence of deterministic gradient sampling in nonsmooth optimization. Computational Optimization and Applications.https://doi.org/10.1007/s10589-024-00552-0 
LibreCat: /record/51208
DOI: 10.1007/s10589-024-00552-0

Sonntag, K., &; Peitz, S. (2024). Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems. Journal of Optimization Theory and Applications.https://doi.org/10.1007/s10957-024-02389-3 
LibreCat: /record/46019
DOI: 10.1007/s10957-024-02389-3

Sonntag, K., Gebken, B., Müller, G., Peitz, S., &; Volkwein, S. (2024). A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces. In arXiv:2402.06376.
LibreCat: /record/51334
arXiv: 	2402.06376

Bick, C., &; von der Gracht, S. (2024). Heteroclinic dynamics in network dynamical systems with higher-order interactions. Journal of Complex Networks, 12(2).https://doi.org/10.1093/comnet/cnae009 
LibreCat: /record/52726
DOI: 10.1093/comnet/cnae009
arXiv: 2309.02006

Peitz, S., &; Bieker, K. (2023). On the Universal Transformation of Data-Driven Models to Control Systems. Automatica, 149, Article 110840.https://doi.org/10.1016/j.automatica.2022.110840 
LibreCat: /record/21199
DOI: 10.1016/j.automatica.2022.110840

Gebken, B., Bieker, K., &; Peitz, S. (2023). On the structure of regularization paths for piecewise differentiable regularization terms. Journal of Global Optimization, 85(3), 709–741.https://doi.org/10.1007/s10898-022-01223-2 
LibreCat: /record/27426
DOI: 10.1007/s10898-022-01223-2

von der Gracht, S., Nijholt, E., &; Rink, B. (n.d.). A parametrisation method for high-order phase reduction in coupled  oscillator networks. In arXiv:2306.03320.
LibreCat: /record/45498
arXiv: 2306.03320

Nüske, F., Peitz, S., Philipp, F., Schaller, M., &; Worthmann, K. (2023). Finite-data error bounds for Koopman-based prediction and control. Journal of Nonlinear Science, 33, Article 14.https://doi.org/10.1007/s00332-022-09862-1 
LibreCat: /record/23428
DOI: 10.1007/s00332-022-09862-1

Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz, S., &; Pfannschmidt, K. (2023). Efficient time stepping for numerical integration using reinforcement  learning. SIAM Journal on Scientific Computing, 45(2), 9–5.https://doi.org/10.1137/21M1412682 
LibreCat: /record/21600
DOI: 10.1137/21M1412682
arXiv: arXiv:2104.03562

von der Gracht, S., Nijholt, E., &; Rink, B. (2023). Hypernetworks: Cluster Synchronization Is a Higher-Order Effect. SIAM Journal on Applied Mathematics, 83(6), 2329–2353.https://doi.org/10.1137/23m1561075 
LibreCat: /record/49326
DOI: 10.1137/23m1561075
arXiv: 2302.08974

von der Gracht, S., Nijholt, E., &; Rink, B. (2023). Higher order interactions lead to ctant” synchrony breaking. In arXiv:2311.17186.
LibreCat: /record/49371
arXiv: 2311.17186

Gerlach, R., von der Gracht, S., &; Dellnitz, M. (n.d.). On the Dynamical Hierarchy in Gathering Protocols with Circulant  Topologies. In arXiv:2305.06632.
LibreCat: /record/44840
arXiv: 2305.06632

Sonntag, K., &; Peitz, S. (2023). Fast Convergence of Inertial Multiobjective Gradient-like Systems with Asymptotic Vanishing Damping. In arXiv:2307.00975.
LibreCat: /record/32447
arXiv: 2307.00975

Bernreuther, M., Dellnitz, M., Gebken, B., Müller, G., Peitz, S., Sonntag, K., &; Volkwein, S. (2023). Multiobjective Optimization of Non-Smooth PDE-Constrained Problems. In arXiv:2308.01113.
LibreCat: /record/46578
arXiv: 2308.01113

Banholzer, S., Gebken, B., Dellnitz, M., Peitz, S., &; Volkwein, S. (2022). ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical Continuation. In H. Michael, H. Roland, K. Christian, U. Michael, &; U. Stefan (Eds.), Non-Smooth and Complementarity-Based Distributed Parameter Systems (pp. 43–76). Springer.https://doi.org/10.1007/978-3-030-79393-7_3 
LibreCat: /record/16296
DOI: 10.1007/978-3-030-79393-7_3

Peitz, S., Dellnitz, M., &; Bannenberg, S. (2022). Efficient Virtual Design and Testing of Autonomous Vehicles. In H. G. Bock, K.-H. Küfer, P. Maas, A. Milde, &; V. Schulz (Eds.), German Success Stories in Industrial Mathematics (Vol. 35). Springer International Publishing.https://doi.org/10.1007/978-3-030-81455-7_23 
LibreCat: /record/30294
DOI: 10.1007/978-3-030-81455-7_23

Klus, S., Nüske, F., &; Peitz, S. (2022). Koopman analysis of quantum systems. Journal of Physics A: Mathematical and Theoretical, 55(31), 314002.https://doi.org/10.1088/1751-8121/ac7d22 
LibreCat: /record/29673
DOI: 10.1088/1751-8121/ac7d22
arXiv: 2201.12062

Gebken, B. (2022). Using second-order information in gradient sampling methods for  nonsmooth optimization. In arXiv:2210.04579.
LibreCat: /record/34618
arXiv: 2210.04579

Gebken, B. (2022). Computation and analysis of Pareto critical sets in smooth and nonsmooth multiobjective optimization.https://doi.org/10.17619/UNIPB/1-1327 
LibreCat: /record/31556
DOI: 10.17619/UNIPB/1-1327

Berkemeier, M. B., &; Peitz, S. (2022). Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients. In arXiv:2208.12094.
LibreCat: /record/33150
arXiv: 2208.12094

Bieker, K., Gebken, B., &; Peitz, S. (2022). On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 7797–7808.https://doi.org/10.1109/TPAMI.2021.3114962 
LibreCat: /record/20731
DOI: 10.1109/TPAMI.2021.3114962

Nüske, F., Gelß, P., Klus, S., &; Clementi, C. (2021). Tensor-based computation of metastable and coherent sets. Physica D: Nonlinear Phenomena, Article 133018.https://doi.org/10.1016/j.physd.2021.133018 
LibreCat: /record/24169
DOI: 10.1016/j.physd.2021.133018

Klus, S., Gelß, P., Nüske, F., &; Noé, F. (2021). Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry. Machine Learning: Science and Technology, Article 045016.https://doi.org/10.1088/2632-2153/ac14ad 
LibreCat: /record/24170
DOI: 10.1088/2632-2153/ac14ad

Goelz, C., Mora, K., Stroehlein, J. K., Haase, F. K., Dellnitz, M., Reinsberger, C., &; Vieluf, S. (2021). Electrophysiological signatures of dedifferentiation differ between fit and less fit older adults. Cognitive Neurodynamics.https://doi.org/10.1007/s11571-020-09656-9 
LibreCat: /record/21195
DOI: 10.1007/s11571-020-09656-9

Berkemeier, M. B., &; Peitz, S. (2021). Derivative-Free Multiobjective Trust Region Descent Method Using Radial  Basis Function Surrogate Models. Mathematical and Computational Applications, 26(2).https://doi.org/10.3390/mca26020031 
LibreCat: /record/21337
DOI: 10.3390/mca26020031

Nüske, F., Koltai, P., Boninsegna, L., &; Clementi, C. (2021). Spectral Properties of Effective Dynamics from Conditional Expectations. Entropy.https://doi.org/10.3390/e23020134 
LibreCat: /record/21820
DOI: 10.3390/e23020134

Gebken, B., &; Peitz, S. (2021). An efficient descent method for locally Lipschitz multiobjective optimization problems. Journal of Optimization Theory and Applications, 188, 696–723.https://doi.org/10.1007/s10957-020-01803-w 
LibreCat: /record/16867
DOI: 10.1007/s10957-020-01803-w

Gebken, B., &; Peitz, S. (2021). Inverse multiobjective optimization: Inferring decision criteria from data. Journal of Global Optimization, 80, 3–29.https://doi.org/10.1007/s10898-020-00983-z 
LibreCat: /record/16295
DOI: 10.1007/s10898-020-00983-z

Gerlach, R. (2021). The Computation and Analysis of Invariant Sets of Infinite-Dimensional Systems.https://doi.org/10.17619/UNIPB/1-1278 
LibreCat: /record/32057
DOI: 10.17619/UNIPB/1-1278

Ober-Blöbaum, S., &; Peitz, S. (2021). Explicit multiobjective model predictive control for nonlinear systems  with symmetries. International Journal of Robust and Nonlinear Control, 31(2), 380–403.https://doi.org/10.1002/rnc.5281 
LibreCat: /record/16294
DOI: 10.1002/rnc.5281

Flaßkamp, K., Ober-Blöbaum, S., &; Peitz, S. (2020). Symmetry in Optimal Control: A Multiobjective Model Predictive Control Approach. In O. Junge, O. Schütze, G. Froyland, S. Ober-Blöbaum, &; K. Padberg-Gehle (Eds.), Advances in Dynamics, Optimization and Computation. Cham: Springer.https://doi.org/10.1007/978-3-030-51264-4_9 
LibreCat: /record/17411
DOI: 10.1007/978-3-030-51264-4_9

Klus, S., Nüske, F., &; Hamzi, B. (2020). Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator. Entropy.https://doi.org/10.3390/e22070722 
LibreCat: /record/21819
DOI: 10.3390/e22070722

Schütze, O., Cuate, O., Martín, A., Peitz, S., &; Dellnitz, M. (2020). Pareto Explorer: a global/local exploration tool for many-objective optimization problems. Engineering Optimization, 52(5), 832–855.https://doi.org/10.1080/0305215x.2019.1617286 
LibreCat: /record/10596
DOI: 10.1080/0305215x.2019.1617286

Klus, S., Nüske, F., Peitz, S., Niemann, J.-H., Clementi, C., &; Schütte, C. (2020). Data-driven approximation of the Koopman generator: Model reduction, system identification, and control. Physica D: Nonlinear Phenomena, 406.https://doi.org/10.1016/j.physd.2020.132416 
LibreCat: /record/16288
DOI: 10.1016/j.physd.2020.132416

Peitz, S., &; Klus, S. (2020). 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, pp. 257–282). Cham: Springer.https://doi.org/10.1007/978-3-030-35713-9_10 
LibreCat: /record/16289
DOI: 10.1007/978-3-030-35713-9_10

Bieker, K., Peitz, S., Brunton, S. L., Kutz, J. N., &; Dellnitz, M. (2020). Deep model predictive flow control with limited sensor data and online learning. Theoretical and Computational Fluid Dynamics, 34, 577–591.https://doi.org/10.1007/s00162-020-00520-4 
LibreCat: /record/16290
DOI: 10.1007/s00162-020-00520-4

Peitz, S., Otto, S. E., &; Rowley, C. W. (2020). Data-Driven Model Predictive Control using Interpolated Koopman  Generators. SIAM Journal on Applied Dynamical Systems, 19(3), 2162–2193.https://doi.org/10.1137/20M1325678 
LibreCat: /record/16309
DOI: 10.1137/20M1325678

Gerlach, R., Ziessler, A., Eckhardt, B., &; Dellnitz, M. (2020). A Set-Oriented Path Following Method for the Approximation of Parameter Dependent Attractors. SIAM Journal on Applied Dynamical Systems, 705–723.https://doi.org/10.1137/19m1247139 
LibreCat: /record/16710
DOI: 10.1137/19m1247139

Hernández Castellanos, C. I., Ober-Blöbaum, S., &; Peitz, S. (2020). Explicit Multi-objective Model Predictive Control for Nonlinear Systems  Under Uncertainty. International Journal of Robust and Nonlinear Control, 30(17), 7593–7618.https://doi.org/10.1002/rnc.5197 
LibreCat: /record/16297
DOI: 10.1002/rnc.5197

Gerlach, R., &; Ziessler, A. (2020). The Approximation of Invariant Sets in Infinite Dimensional Dynamical Systems. In O. Junge, O. Schütze, S. Ober-Blöbaum, &; K. Padberg-Gehle (Eds.), Advances in Dynamics, Optimization and Computation (Vol. 304, pp. 66–85). Springer International Publishing.https://doi.org/10.1007/978-3-030-51264-4_3 
LibreCat: /record/17994
DOI: 10.1007/978-3-030-51264-4_3

Dellnitz, M., Gebken, B., Gerlach, R., &; Klus, S. (2020). On the equivariance properties of self-adjoint matrices. Dynamical Systems, 35(2), 197–215.https://doi.org/10.1080/14689367.2019.1661355 
LibreCat: /record/16712
DOI: 10.1080/14689367.2019.1661355

Nüske, F., Boninsegna, L., &; Clementi, C. (2019). Coarse-graining molecular systems by spectral matching. The Journal of Chemical Physics.https://doi.org/10.1063/1.5100131 
LibreCat: /record/21944
DOI: 10.1063/1.5100131

Sahai, T., Ziessler, A., Klus, S., &; Dellnitz, M. (2019). Continuous relaxations for the traveling salesman problem. Nonlinear Dynamics.https://doi.org/10.1007/s11071-019-05092-5 
LibreCat: /record/16709
DOI: 10.1007/s11071-019-05092-5

Peitz, S., &; Klus, S. (2019). Koopman operator-based model reduction for switched-system control of PDEs. Automatica, 106, 184–191.https://doi.org/10.1016/j.automatica.2019.05.016 
LibreCat: /record/10593
DOI: 10.1016/j.automatica.2019.05.016

Gebken, B., Peitz, S., &; Dellnitz, M. (2019). On the hierarchical structure of Pareto critical sets. Journal of Global Optimization, 73(4), 891–913.https://doi.org/10.1007/s10898-019-00737-6 
LibreCat: /record/10595
DOI: 10.1007/s10898-019-00737-6

Hanke, S., Peitz, S., Wallscheid, O., Böcker, J., &; Dellnitz, M. (2019). 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).https://doi.org/10.1109/precede.2019.8753313 
LibreCat: /record/10597
DOI: 10.1109/precede.2019.8753313

Gerlach, R., Koltai, P., &; Dellnitz, M. (2019). Revealing the intrinsic geometry of finite dimensional invariant sets of  infinite dimensional dynamical systems. In arXiv:1902.08824.
LibreCat: /record/16711

Ziessler, A., Dellnitz, M., &; Gerlach, R. (2019). The Numerical Computation of Unstable Manifolds for Infinite Dimensional Dynamical Systems by Embedding Techniques. SIAM Journal on Applied Dynamical Systems, 18(3), 1265–1292.https://doi.org/10.1137/18m1204395 
LibreCat: /record/16708
DOI: 10.1137/18m1204395

Hanke, S., Peitz, S., Wallscheid, O., Klus, S., Böcker, J., &; Dellnitz, M. (2018). Koopman Operator-Based Finite-Control-Set Model Predictive Control for  Electrical Drives. ArXiv:1804.00854.
LibreCat: /record/21634

Litzinger, F., Boninsegna, L., Wu, H., Nüske, F., Patel, R., Baraniuk, R.,https://doi.org/10.1021/acs.jctc.8b00089__Clementi, C. (2018). Rapid Calculation of Molecular Kinetics Using Compressed Sensing. Journal of Chemical Theory and Computation, 2771–2783.https://doi.org/10.1021/acs.jctc.8b00089 
LibreCat: /record/21940
DOI: 10.1021/acs.jctc.8b00089