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 .
LibreCat: /record/51208
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 .
LibreCat: /record/46019
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.
LibreCat: /record/51334
arXiv: 	2402.06376

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 .
LibreCat: /record/52726
DOI: 10.1093/comnet/cnae009
arXiv: 2309.02006

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 .
LibreCat: /record/21199
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 .
LibreCat: /record/27426
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. .
LibreCat: /record/45498
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 .
LibreCat: /record/23428
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 .
LibreCat: /record/21600
DOI: 10.1137/21M1412682
arXiv: arXiv:2104.03562

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 .
LibreCat: /record/49326
DOI: 10.1137/23m1561075
arXiv: 2302.08974

S. von der Gracht, E. Nijholt, and B. Rink, “Higher order interactions lead to ‘reluctant’ synchrony breaking,” arXiv:2311.17186. 2023.
LibreCat: /record/49371
arXiv: 2311.17186

R. Gerlach, S. von der Gracht, and M. Dellnitz, “On the Dynamical Hierarchy in Gathering Protocols with Circulant  Topologies,” arXiv:2305.06632. .
LibreCat: /record/44840
arXiv: 2305.06632

K. Sonntag and S. Peitz, “Fast Convergence of Inertial Multiobjective Gradient-like Systems with Asymptotic Vanishing Damping,” arXiv:2307.00975. 2023.
LibreCat: /record/32447
arXiv: 2307.00975

M. Bernreuther et al., “Multiobjective Optimization of Non-Smooth PDE-Constrained Problems,” arXiv:2308.01113. 2023.
LibreCat: /record/46578
arXiv: 2308.01113

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.
LibreCat: /record/16296
DOI: 10.1007/978-3-030-79393-7_3

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.
LibreCat: /record/30294
DOI: 10.1007/978-3-030-81455-7_23

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 .
LibreCat: /record/29673
DOI: 10.1088/1751-8121/ac7d22
arXiv: 2201.12062

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

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

M. B. Berkemeier and S. Peitz, “Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients,” arXiv:2208.12094. 2022.
LibreCat: /record/33150
arXiv: 2208.12094

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 .
LibreCat: /record/20731
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 .
LibreCat: /record/24169
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 .
LibreCat: /record/24170
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.
LibreCat: /record/21195
DOI: 10.1007/s11571-020-09656-9

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.
LibreCat: /record/21337
DOI: 10.3390/mca26020031

F. Nüske, P. Koltai, L. Boninsegna, and C. Clementi, “Spectral Properties of Effective Dynamics from Conditional Expectations,” Entropy, 2021.
LibreCat: /record/21820
DOI: 10.3390/e23020134

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.
LibreCat: /record/16867
DOI: 10.1007/s10957-020-01803-w

B. Gebken and S. Peitz, “Inverse multiobjective optimization: Inferring decision criteria from data,” Journal of Global Optimization, vol. 80, pp. 3–29, 2021.
LibreCat: /record/16295
DOI: 10.1007/s10898-020-00983-z

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

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 .
LibreCat: /record/16294
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.
LibreCat: /record/17411
DOI: 10.1007/978-3-030-51264-4_9

S. Klus, F. Nüske, and B. Hamzi, “Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator,” Entropy, 2020.
LibreCat: /record/21819
DOI: 10.3390/e22070722

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.
LibreCat: /record/10596
DOI: 10.1080/0305215x.2019.1617286

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.
LibreCat: /record/16288
DOI: 10.1016/j.physd.2020.132416

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.
LibreCat: /record/16289
DOI: 10.1007/978-3-030-35713-9_10

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.
LibreCat: /record/16290
DOI: 10.1007/s00162-020-00520-4

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.
LibreCat: /record/16309
DOI: 10.1137/20M1325678

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 .
LibreCat: /record/16710
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 .
LibreCat: /record/16297
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.
LibreCat: /record/17994
DOI: 10.1007/978-3-030-51264-4_3

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 .
LibreCat: /record/16712
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.
LibreCat: /record/21944
DOI: 10.1063/1.5100131

T. Sahai, A. Ziessler, S. Klus, and M. Dellnitz, “Continuous relaxations for the traveling salesman problem,” Nonlinear Dynamics, 2019.
LibreCat: /record/16709
DOI: 10.1007/s11071-019-05092-5

S. Peitz and S. Klus, “Koopman operator-based model reduction for switched-system control of PDEs,” Automatica, vol. 106, pp. 184–191, 2019.
LibreCat: /record/10593
DOI: 10.1016/j.automatica.2019.05.016

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.
LibreCat: /record/10595
DOI: 10.1007/s10898-019-00737-6

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.
LibreCat: /record/10597
DOI: 10.1109/precede.2019.8753313

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.
LibreCat: /record/16711

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 .
LibreCat: /record/16708
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.
LibreCat: /record/21634

F. Litzinger et al., “Rapid Calculation of Molecular Kinetics Using Compressed Sensing,” Journal of Chemical Theory and Computation, pp. 2771–2783, 2018.
LibreCat: /record/21940
DOI: 10.1021/acs.jctc.8b00089