Philipp, Friedrich M., et al. “Extended Dynamic Mode Decomposition: Sharp Bounds on the Sample  Efficiency.” ArXiv:2402.02494, 2024.
LibreCat: /record/51160
arXiv: 2402.02494

Sonntag, Konstantin, and Sebastian 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, Springer, 2024, doi:10.1007/s10957-024-02389-3.
LibreCat: /record/46019
DOI: 10.1007/s10957-024-02389-3

Sonntag, Konstantin, et al. “A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces.” ArXiv:2402.06376, 2024.
LibreCat: /record/51334
arXiv: 	2402.06376

Peitz, Sebastian, et al. “Distributed Control of Partial Differential Equations Using  Convolutional Reinforcement Learning.”10.1016/j.physd.2024.134096__Physica D: Nonlinear Phenomena, vol. 461, Elsevier, 2024, p. 134096, doi:10.1016/j.physd.2024.134096.
LibreCat: /record/40171
DOI: 10.1016/j.physd.2024.134096

Otto, Samuel E., et al. “Learning Bilinear Models of Actuated Koopman Generators from  Partially-Observed Trajectories.”10.1137/22M1523601__SIAM Journal on Applied Dynamical Systems, vol. 23, no. 1, SIAM, 2024, pp. 885–923, doi:10.1137/22M1523601.
LibreCat: /record/33461
DOI: 10.1137/22M1523601
arXiv: 2209.09977

Philipp, Friedrich, et al. “Error Bounds for Kernel-Based Approximations of the Koopman Operator.”10.1016/j.acha.2024.101657__Applied and Computational Harmonic Analysis , vol. 71, 101657, Springer , 2024, doi:10.1016/j.acha.2024.101657.
LibreCat: /record/38031
DOI: 10.1016/j.acha.2024.101657
arXiv: 2301.08637

Harder, Hans, and Sebastian Peitz. Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines.
LibreCat: /record/53793

Harder, Hans, and Sebastian Peitz. On the Continuity and Smoothness of the Value Function in Reinforcement Learning and Optimal Control. 2024.
LibreCat: /record/52758

Akhter, Junaid, et al. “Common Pitfalls to Avoid While Using Multiobjective Optimization in Machine Learning.” ArXiv, 2024.
LibreCat: /record/53858

Peitz, Sebastian, and Katharina Bieker. “On the Universal Transformation of Data-Driven Models to Control Systems.”10.1016/j.automatica.2022.110840__Automatica, vol. 149, 110840, Elsevier, 2023, doi:10.1016/j.automatica.2022.110840.
LibreCat: /record/21199
DOI: 10.1016/j.automatica.2022.110840

Werner, Stefan, and Sebastian Peitz. “Learning a Model Is Paramount for Sample Efficiency in Reinforcement  Learning Control of PDEs.” ArXiv:2302.07160, 2023.
LibreCat: /record/42160
arXiv: 2302.07160

Gebken, Bennet, et al. “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, 2023, pp. 709–41, doi:10.1007/s10898-022-01223-2.
LibreCat: /record/27426
DOI: 10.1007/s10898-022-01223-2

Schaller, Manuel, et al. “Towards Reliable Data-Based Optimal and Predictive Control Using Extended DMD.”10.1016/j.ifacol.2023.02.029__IFAC-PapersOnLine, vol. 56, no. 1, 2023, pp. 169–74, doi:10.1016/j.ifacol.2023.02.029.
LibreCat: /record/30125
DOI: 10.1016/j.ifacol.2023.02.029
arXiv: 2202.09084

Peitz, Sebastian, et al. “Partial Observations, Coarse Graining and Equivariance in Koopman  Operator Theory for Large-Scale Dynamical Systems.” ArXiv:2307.15325, 2023.
LibreCat: /record/46579
arXiv: 2307.15325

Nüske, Feliks, et al. “Finite-Data Error Bounds for Koopman-Based Prediction and Control.”10.1007/s00332-022-09862-1__Journal of Nonlinear Science, vol. 33, 14, 2023, doi:10.1007/s00332-022-09862-1.
LibreCat: /record/23428
DOI: 10.1007/s00332-022-09862-1

Hotegni, Sedjro Salomon, et al. “Multi-Objective Optimization for Sparse Deep Neural Network Training.” ArXiv:2308.12243, 2023.
LibreCat: /record/46649
arXiv: 2308.12243

Dellnitz, Michael, et al. “Efficient Time Stepping for Numerical Integration Using Reinforcement  Learning.”10.1137/21M1412682__SIAM Journal on Scientific Computing, vol. 45, no. 2, 2023, pp. 9–95, doi:10.1137/21M1412682.
LibreCat: /record/21600
DOI: 10.1137/21M1412682
arXiv: arXiv:2104.03562

Wallscheid, Oliver, et al. “ElectricGrid.Jl - A Julia-Based Modeling and Simulationtool for Power Electronics-Driven Electric Energy Grids.”10.21105/joss.05616__Journal of Open Source Software, vol. 8, no. 89, 5616, The Open Journal, 2023, doi:10.21105/joss.05616.
LibreCat: /record/46784
DOI: 10.21105/joss.05616

Wohlleben, Meike Claudia, et al. “Transferability of a Discrepancy Model for the Dynamics of Electromagnetic Oscillating Circuits.”10.1002/pamm.202300039__Proceedings in Applied Mathematics and Mechanics, Wiley, 2023, doi:10.1002/pamm.202300039.
LibreCat: /record/46813
DOI: 10.1002/pamm.202300039

Peitz, Sebastian, et al. Accelerating the Analysis of Optical Quantum Systems Using the Koopman Operator. 2023.
LibreCat: /record/48502

Amakor, Augustina Chidinma, et al. “A Multiobjective Continuation Method to Compute the Regularization Path of Deep Neural Networks.” ArXiv, 2023.
LibreCat: /record/51159

Philipp, Friedrich, et al. “Error Analysis of Kernel EDMD for Prediction and Control in the Koopman  Framework.” ArXiv:2312.10460, 2023.
LibreCat: /record/51158
arXiv: 2312.10460

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

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

Banholzer, Stefan, et al. “ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical Continuation.”10.1007/978-3-030-79393-7_3__Non-Smooth and Complementarity-Based Distributed Parameter Systems, edited by Hintermüller Michael et al., Springer, 2022, pp. 43–76, doi:10.1007/978-3-030-79393-7_3.
LibreCat: /record/16296
DOI: 10.1007/978-3-030-79393-7_3

Peitz, Sebastian, et al. “Efficient Virtual Design and Testing of Autonomous Vehicles.”10.1007/978-3-030-81455-7_23__German Success Stories in Industrial Mathematics, edited by H. G. Bock et al., vol. 35, Springer International Publishing, 2022, doi:10.1007/978-3-030-81455-7_23.
LibreCat: /record/30294
DOI: 10.1007/978-3-030-81455-7_23

Klus, Stefan, et al. “Koopman Analysis of Quantum Systems.”10.1088/1751-8121/ac7d22__Journal of Physics A: Mathematical and Theoretical, vol. 55, no. 31, IOP Publishing Ltd., 2022, p. 314002, doi:10.1088/1751-8121/ac7d22.
LibreCat: /record/29673
DOI: 10.1088/1751-8121/ac7d22
arXiv: 2201.12062

Berkemeier, Manuel Bastian, and Sebastian Peitz. “Multi-Objective Trust-Region Filter Method for Nonlinear Constraints Using Inexact Gradients.” ArXiv:2208.12094, 2022.
LibreCat: /record/33150
arXiv: 2208.12094

Bieker, Katharina, et al. “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, IEEE, 2022, pp. 7797–808, doi:10.1109/TPAMI.2021.3114962.
LibreCat: /record/20731
DOI: 10.1109/TPAMI.2021.3114962

Wohlleben, Meike Claudia, et al. “Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction.”10.1007/978-3-030-95470-3_8__Machine Learning, Optimization, and Data Science, Springer International Publishing, 2022, doi:10.1007/978-3-030-95470-3_8.
LibreCat: /record/29727
DOI: 10.1007/978-3-030-95470-3_8

Berkemeier, Manuel Bastian, and Sebastian Peitz. “Derivative-Free Multiobjective Trust Region Descent Method Using Radial  Basis Function Surrogate Models.”10.3390/mca26020031__Mathematical and Computational Applications, vol. 26, no. 2, 31, 2021, doi:10.3390/mca26020031.
LibreCat: /record/21337
DOI: 10.3390/mca26020031

Gebken, Bennet, and Sebastian Peitz. “An Efficient Descent Method for Locally Lipschitz Multiobjective Optimization Problems.”10.1007/s10957-020-01803-w__Journal of Optimization Theory and Applications, vol. 188, 2021, pp. 696–723, doi:10.1007/s10957-020-01803-w.
LibreCat: /record/16867
DOI: 10.1007/s10957-020-01803-w

Gebken, Bennet, and Sebastian Peitz. “Inverse Multiobjective Optimization: Inferring Decision Criteria from Data.”10.1007/s10898-020-00983-z__Journal of Global Optimization, vol. 80, Springer, 2021, pp. 3–29, doi:10.1007/s10898-020-00983-z.
LibreCat: /record/16295
DOI: 10.1007/s10898-020-00983-z

Ober-Blöbaum, Sina, and Sebastian 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), 2021, pp. 380–403, doi:10.1002/rnc.5281.
LibreCat: /record/16294
DOI: 10.1002/rnc.5281

Flaßkamp, Kathrin, et al. “Symmetry in Optimal Control: A Multiobjective Model Predictive Control Approach.”10.1007/978-3-030-51264-4_9__Advances in Dynamics, Optimization and Computation, edited by Oliver Junge et al., Springer, 2020, doi:10.1007/978-3-030-51264-4_9.
LibreCat: /record/17411
DOI: 10.1007/978-3-030-51264-4_9

Schütze, Oliver, et al. “Pareto Explorer: A Global/Local Exploration Tool for Many-Objective Optimization Problems.”10.1080/0305215x.2019.1617286__Engineering Optimization, vol. 52, no. 5, 2020, pp. 832–55, doi:10.1080/0305215x.2019.1617286.
LibreCat: /record/10596
DOI: 10.1080/0305215x.2019.1617286

Klus, Stefan, et al. “Data-Driven Approximation of the Koopman Generator: Model Reduction, System Identification, and Control.”10.1016/j.physd.2020.132416__Physica D: Nonlinear Phenomena, vol. 406, 132416, 2020, doi:10.1016/j.physd.2020.132416.
LibreCat: /record/16288
DOI: 10.1016/j.physd.2020.132416

Peitz, Sebastian, and Stefan Klus. “Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator.”10.1007/978-3-030-35713-9_10__Lecture Notes in Control and Information Sciences, vol. 484, Springer, 2020, pp. 257–82, doi:10.1007/978-3-030-35713-9_10.
LibreCat: /record/16289
DOI: 10.1007/978-3-030-35713-9_10

Bieker, Katharina, et al. “Deep Model Predictive Flow Control with Limited Sensor Data and Online Learning.”10.1007/s00162-020-00520-4__Theoretical and Computational Fluid Dynamics, vol. 34, 2020, pp. 577–591, doi:10.1007/s00162-020-00520-4.
LibreCat: /record/16290
DOI: 10.1007/s00162-020-00520-4

Peitz, Sebastian, et al. “Data-Driven Model Predictive Control Using Interpolated Koopman  Generators.”10.1137/20M1325678__SIAM Journal on Applied Dynamical Systems, vol. 19, no. 3, 2020, pp. 2162–93, doi:10.1137/20M1325678.
LibreCat: /record/16309
DOI: 10.1137/20M1325678

Hernández Castellanos, Carlos Ignacio, et al. “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), 2020, pp. 7593–618, doi:10.1002/rnc.5197.
LibreCat: /record/16297
DOI: 10.1002/rnc.5197

Peitz, Sebastian, and Stefan Klus. “Koopman Operator-Based Model Reduction for Switched-System Control of PDEs.”10.1016/j.automatica.2019.05.016__Automatica, vol. 106, 2019, pp. 184–91, doi:10.1016/j.automatica.2019.05.016.
LibreCat: /record/10593
DOI: 10.1016/j.automatica.2019.05.016

Gebken, Bennet, et al. “On the Hierarchical Structure of Pareto Critical Sets.”10.1007/s10898-019-00737-6__Journal of Global Optimization, vol. 73, no. 4, 2019, pp. 891–913, doi:10.1007/s10898-019-00737-6.
LibreCat: /record/10595
DOI: 10.1007/s10898-019-00737-6

Hanke, Soren, et al. “Finite-Control-Set Model Predictive Control for a Permanent Magnet Synchronous Motor Application with Online Least Squares System Identification.”10.1109/precede.2019.8753313__2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 2019, doi:10.1109/precede.2019.8753313.
LibreCat: /record/10597
DOI: 10.1109/precede.2019.8753313

Hanke, Sören, et al. “Finite-Control-Set Model Predictive Control for a Permanent Magnet Synchronous Motor Application with Online Least Squares System Identification.” 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 2019, pp. 1–6.
LibreCat: /record/29636

Hanke, Sören, et al. “Koopman Operator-Based Finite-Control-Set Model Predictive Control for  Electrical Drives.” ArXiv:1804.00854, 2018.
LibreCat: /record/21634

Kummert, Franz, et al. “Eingesetzte wissenschaftliche Methoden.” Ressourceneffiziente Selbstoptimierende Wäscherei – Ergebnisse des ReSerW-Projekts, edited by Ansgar Trächtler, Springer, 2018.
LibreCat: /record/22796

Gebken, Bennet, et al. “A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems.”10.1007/978-3-319-96104-0_2__Numerical and Evolutionary Optimization – NEO 2017, 2018, doi:10.1007/978-3-319-96104-0_2.
LibreCat: /record/8750
DOI: 10.1007/978-3-319-96104-0_2

Peitz, Sebastian, and Michael Dellnitz. “A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction.”10.3390/mca23020030__Mathematical and Computational Applications, vol. 23, no. 2, 2018, doi:10.3390/mca23020030.
LibreCat: /record/8751
DOI: 10.3390/mca23020030

Beermann, Dennis, et al. “Set-Oriented Multiobjective Optimal Control of PDEs Using Proper Orthogonal Decomposition.”10.1007/978-3-319-75319-5_3__Reduced-Order Modeling (ROM) for Simulation and Optimization, 2018, pp. 47–72, doi:10.1007/978-3-319-75319-5_3.
LibreCat: /record/8754
DOI: 10.1007/978-3-319-75319-5_3