Philipp FM, Schaller M, Boshoff S, Peitz S, Nüske F, Worthmann K. Extended Dynamic Mode Decomposition: Sharp bounds on the sample  efficiency. arXiv:240202494. Published online 2024.
LibreCat: /record/51160
arXiv: 2402.02494

Sonntag K, Peitz S. Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems. Journal of Optimization Theory and Applications. Published online 2024. doi: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. A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces. arXiv:240206376. Published online 2024.
LibreCat: /record/51334
arXiv: 	2402.06376

Peitz S, Stenner J, Chidananda V, Wallscheid O, Brunton SL, Taira K. Distributed Control of Partial Differential Equations Using  Convolutional Reinforcement Learning. Physica D: Nonlinear Phenomena. 2024;461:134096. doi:10.1016/j.physd.2024.134096
LibreCat: /record/40171
DOI: 10.1016/j.physd.2024.134096

Otto SE, Peitz S, Rowley CW. Learning Bilinear Models of Actuated Koopman Generators from  Partially-Observed Trajectories. SIAM Journal on Applied Dynamical Systems. 2024;23(1):885-923. doi:10.1137/22M1523601
LibreCat: /record/33461
DOI: 10.1137/22M1523601
arXiv: 2209.09977

Philipp F, Schaller M, Worthmann K, Peitz S, Nüske F. Error bounds for kernel-based approximations of the Koopman operator. Applied and Computational Harmonic Analysis . 2024;71. doi:10.1016/j.acha.2024.101657
LibreCat: /record/38031
DOI: 10.1016/j.acha.2024.101657
arXiv: 2301.08637

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

Harder H, Peitz S. On the continuity and smoothness of the value function in reinforcement learning and optimal control. Published online 2024.
LibreCat: /record/52758

Akhter J, Fährmann PD, Sonntag K, Peitz S. Common pitfalls to avoid while using multiobjective optimization in machine learning. arXiv. Published online 2024.
LibreCat: /record/53858

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

Werner S, Peitz S. Learning a model is paramount for sample efficiency in reinforcement  learning control of PDEs. arXiv:230207160. Published online 2023.
LibreCat: /record/42160
arXiv: 2302.07160

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

Schaller M, Worthmann K, Philipp F, Peitz S, Nüske F. Towards reliable data-based optimal and predictive control using extended DMD. In: IFAC-PapersOnLine. Vol 56. ; 2023:169-174. doi:10.1016/j.ifacol.2023.02.029
LibreCat: /record/30125
DOI: 10.1016/j.ifacol.2023.02.029
arXiv: 2202.09084

Peitz S, Harder H, Nüske F, Philipp F, Schaller M, Worthmann K. Partial observations, coarse graining and equivariance in Koopman  operator theory for large-scale dynamical systems. arXiv:230715325. Published online 2023.
LibreCat: /record/46579
arXiv: 2307.15325

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

Hotegni SS, Peitz S, Berkemeier MB. Multi-Objective Optimization for Sparse Deep Neural Network Training. arXiv:230812243. Published online 2023.
LibreCat: /record/46649
arXiv: 2308.12243

Dellnitz M, Hüllermeier E, Lücke M, et al. Efficient time stepping for numerical integration using reinforcement  learning. SIAM Journal on Scientific Computing. 2023;45(2):9-5. doi:10.1137/21M1412682
LibreCat: /record/21600
DOI: 10.1137/21M1412682
arXiv: arXiv:2104.03562

Wallscheid O, Peitz S, Stenner J, et al. ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids. Journal of Open Source Software. 2023;8(89). doi:10.21105/joss.05616
LibreCat: /record/46784
DOI: 10.21105/joss.05616

Wohlleben MC, Muth L, Peitz S, Sextro W. Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits. In: Proceedings in Applied Mathematics and Mechanics. Wiley; 2023. doi:10.1002/pamm.202300039
LibreCat: /record/46813
DOI: 10.1002/pamm.202300039

Peitz S, Hunstig A, Rose H, Meier T. Accelerating the analysis of optical quantum systems using the Koopman operator. Published online 2023.
LibreCat: /record/48502

Amakor AC, Sonntag K, Peitz S. A multiobjective continuation method to compute the regularization path of deep neural networks. arXiv. Published online 2023.
LibreCat: /record/51159

Philipp F, Schaller M, Worthmann K, Peitz S, Nüske F. Error analysis of kernel EDMD for prediction and control in the Koopman  framework. arXiv:231210460. Published online 2023.
LibreCat: /record/51158
arXiv: 2312.10460

Sonntag K, Peitz S. Fast Convergence of Inertial Multiobjective Gradient-like Systems with Asymptotic Vanishing Damping. arXiv:230700975. Published online 2023.
LibreCat: /record/32447
arXiv: 2307.00975

Bernreuther M, Dellnitz M, Gebken B, et al. Multiobjective Optimization of Non-Smooth PDE-Constrained Problems. arXiv:230801113. Published online 2023.
LibreCat: /record/46578
arXiv: 2308.01113

Banholzer S, Gebken B, Dellnitz M, Peitz S, Volkwein S. ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical Continuation. In: Michael H, Roland H, Christian K, Michael U, Stefan U, eds. Non-Smooth and Complementarity-Based Distributed Parameter Systems. Springer; 2022:43-76. doi: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. Efficient Virtual Design and Testing of Autonomous Vehicles. In: Bock HG, Küfer K-H, Maas P, Milde A, Schulz V, eds. German Success Stories in Industrial Mathematics. Vol 35. Mathematics in Industry. 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 S, Nüske F, Peitz S. Koopman analysis of quantum systems. Journal of Physics A: Mathematical and Theoretical. 2022;55(31):314002. doi:10.1088/1751-8121/ac7d22
LibreCat: /record/29673
DOI: 10.1088/1751-8121/ac7d22
arXiv: 2201.12062

Berkemeier MB, Peitz S. Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients. arXiv:220812094. Published online 2022.
LibreCat: /record/33150
arXiv: 2208.12094

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

Wohlleben MC, Bender A, Peitz S, Sextro W. Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction. In: 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 MB, Peitz S. Derivative-Free Multiobjective Trust Region Descent Method Using Radial  Basis Function Surrogate Models. Mathematical and Computational Applications. 2021;26(2). doi:10.3390/mca26020031
LibreCat: /record/21337
DOI: 10.3390/mca26020031

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

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

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

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

Schütze O, Cuate O, Martín A, Peitz S, Dellnitz M. Pareto Explorer: a global/local exploration tool for many-objective optimization problems. Engineering Optimization. 2020;52(5):832-855. doi: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. Data-driven approximation of the Koopman generator: Model reduction, system identification, and control. Physica D: Nonlinear Phenomena. 2020;406. doi:10.1016/j.physd.2020.132416
LibreCat: /record/16288
DOI: 10.1016/j.physd.2020.132416

Peitz S, Klus S. 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. Lecture Notes in Control and Information Sciences. Cham: Springer; 2020:257-282. doi: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 SL, Kutz JN, Dellnitz M. Deep model predictive flow control with limited sensor data and online learning. Theoretical and Computational Fluid Dynamics. 2020;34:577–591. doi:10.1007/s00162-020-00520-4
LibreCat: /record/16290
DOI: 10.1007/s00162-020-00520-4

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

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

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

Gebken B, Peitz S, Dellnitz M. On the hierarchical structure of Pareto critical sets. Journal of Global Optimization. 2019;73(4):891-913. doi: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. 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. doi:10.1109/precede.2019.8753313
LibreCat: /record/10597
DOI: 10.1109/precede.2019.8753313

Hanke S, Peitz S, Wallscheid O, Böcker J, Dellnitz M. 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). ; 2019:1–6.
LibreCat: /record/29636

Hanke S, Peitz S, Wallscheid O, Klus S, Böcker J, Dellnitz M. Koopman Operator-Based Finite-Control-Set Model Predictive Control for  Electrical Drives. arXiv:180400854. 2018.
LibreCat: /record/21634

Kummert F, Albers A, Bremer C, et al. Eingesetzte wissenschaftliche Methoden. In: Trächtler A, ed. Ressourceneffiziente Selbstoptimierende Wäscherei – Ergebnisse des ReSerW-Projekts. Paderborn: Springer; 2018.
LibreCat: /record/22796

Gebken B, Peitz S, Dellnitz M. A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems. In: Numerical and Evolutionary Optimization – NEO 2017. Cham; 2018. doi:10.1007/978-3-319-96104-0_2
LibreCat: /record/8750
DOI: 10.1007/978-3-319-96104-0_2

Peitz S, Dellnitz M. A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction. Mathematical and Computational Applications. 2018;23(2). doi:10.3390/mca23020030
LibreCat: /record/8751
DOI: 10.3390/mca23020030

Beermann D, Dellnitz M, Peitz S, Volkwein S. Set-Oriented Multiobjective Optimal Control of PDEs Using Proper Orthogonal Decomposition. In: Reduced-Order Modeling (ROM) for Simulation and Optimization. Cham; 2018:47-72. doi:10.1007/978-3-319-75319-5_3
LibreCat: /record/8754
DOI: 10.1007/978-3-319-75319-5_3

Klus S, Gelß P, Peitz S, Schütte C. Tensor-based dynamic mode decomposition. Nonlinearity. 2018;31(7):3359-3380. doi:10.1088/1361-6544/aabc8f
LibreCat: /record/8755
DOI: 10.1088/1361-6544/aabc8f

Beermann D, Dellnitz M, Peitz S, Volkwein S. POD-based multiobjective optimal control of PDEs with non-smooth objectives. In: PAMM. ; 2018:51-54. doi:10.1002/pamm.201710015
LibreCat: /record/8757
DOI: 10.1002/pamm.201710015

Peitz S. Controlling nonlinear PDEs using low-dimensional bilinear approximations  obtained from data. arXiv:180106419. 2018.
LibreCat: /record/16292

Klus S, Peitz S, Schuster I. Analyzing high-dimensional time-series data using kernel transfer  operator eigenfunctions. arXiv:180510118. 2018.
LibreCat: /record/16293

Hanke S, Peitz S, Wallscheid O, Klus S, Böcker J, Dellnitz M. Koopman Operator-Based Finite-Control-Set Model Predictive Control for Electrical Drives. arXiv preprint arXiv:180400854. Published online 2018.
LibreCat: /record/29624

Peitz S, Ober-Blöbaum S, Dellnitz M. Multiobjective Optimal Control Methods for the Navier-Stokes Equations Using Reduced Order Modeling. Acta Applicandae Mathematicae. 2018;161(1):171–199. doi:10.1007/s10440-018-0209-7
LibreCat: /record/8753
DOI: 10.1007/s10440-018-0209-7

Dellnitz M, Eckstein J, Flaßkamp K, et al. Multiobjective Optimal Control Methods for the Development of an Intelligent Cruise Control. In: Progress in Industrial Mathematics at ECMI 2014 . Cham: Springer International Publishing; 2017:633-641. doi:10.1007/978-3-319-23413-7_87
LibreCat: /record/5914
DOI: 10.1007/978-3-319-23413-7_87

Peitz S, Dellnitz M. Gradient-Based Multiobjective Optimization with Uncertainties. In: NEO 2016. Cham; 2017:159-182. doi:10.1007/978-3-319-64063-1_7
LibreCat: /record/8752
DOI: 10.1007/978-3-319-64063-1_7

Peitz S.   Exploiting Structure in Multiobjective Optimization and Optimal Control.; 2017. doi:10.17619/UNIPB/1-176
LibreCat: /record/10594
DOI: 10.17619/UNIPB/1-176

Peitz S, Schäfer K, Ober-Blöbaum S, Eckstein J, Köhler U, Dellnitz M. A multiobjective MPC approach for autonomously driven electric vehicles. Proceedings of the 20th World Congress of the International Federation of Automatic Control (IFAC). 2017;50(1):8674-8679. doi:10.1016/j.ifacol.2017.08.1526
LibreCat: /record/8756
DOI: 10.1016/j.ifacol.2017.08.1526

Peitz S, Gräler M, Henke C, Molo MH, Dellnitz M, Trächtler A. Multiobjective Model Predictive Control of an Industrial Laundry. In: Procedia Technology. ; 2016:483-490. doi:10.1016/j.protcy.2016.08.061
LibreCat: /record/8759
DOI: 10.1016/j.protcy.2016.08.061

Eckstein J, Peitz S, Schäfer K, et al. A comparison of two predictive approaches to control the longitudinal dynamics of electric vehicles. In: Procedia Technology, 3rd International Conference on System-Integrated Intelligence: New Challenges for Product and Production Engineering. Vol 26. ; 2016:465-472. doi:10.1016/j.protcy.2016.08.059
LibreCat: /record/8758
DOI: 10.1016/j.protcy.2016.08.059

Peitz S, Ober-Blöbaum S, Dellnitz M. Reduced order model based multiobjective optimal control of fluids. In: Proceedings of International Congress of Theoretical and Applied Mechanics. ; 2016.
LibreCat: /record/29433

Peitz S, Dellnitz M. Multiobjective Optimization of the Flow Around a Cylinder Using Model Order Reduction. In: PAMM. ; 2015:613-614. doi:10.1002/pamm.201510296
LibreCat: /record/8760
DOI: 10.1002/pamm.201510296