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

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

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

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

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

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

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

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

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

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

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

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

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

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

Multi-Objective Optimization for Sparse Deep Neural Network Training
S.S. Hotegni, S. Peitz, M.B. Berkemeier, ArXiv:2308.12243 (2023).
LibreCat: /record/46649
arXiv: 2308.12243

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

ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids
O. Wallscheid, S. Peitz, J. Stenner, D. Weber, S. Boshoff, M. Meyer, V. Chidananda, O. Schweins, Journal of Open Source Software 8 (2023).
LibreCat: /record/46784
DOI: 10.21105/joss.05616

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

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

A multiobjective continuation method to compute the regularization path of deep neural networks
A.C. Amakor, K. Sonntag, S. Peitz, ArXiv (2023).
LibreCat: /record/51159

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

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

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

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

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

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

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

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

Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction
M.C. Wohlleben, A. Bender, S. Peitz, W. Sextro, in: Machine Learning, Optimization, and Data Science, Springer International Publishing, Cham, 2022.
LibreCat: /record/29727
DOI: 10.1007/978-3-030-95470-3_8

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

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

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

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

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

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

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

Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator
S. Peitz, S. Klus, in: Lecture Notes in Control and Information Sciences, Springer, Cham, 2020, pp. 257–282.
LibreCat: /record/16289
DOI: 10.1007/978-3-030-35713-9_10

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

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

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

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

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

Finite-Control-Set Model Predictive Control for a Permanent Magnet Synchronous Motor Application with Online Least Squares System Identification
S. Hanke, S. Peitz, O. Wallscheid, J. Böcker, M. Dellnitz, 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

Finite-control-set model predictive control for a permanent magnet synchronous motor application with online least squares system identification
S. Hanke, S. Peitz, O. Wallscheid, J. Böcker, M. Dellnitz, in: 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 2019, pp. 1–6.
LibreCat: /record/29636

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

Eingesetzte wissenschaftliche Methoden
F. Kummert, A. Albers, C. Bremer, S. Büttner, M. Dellnitz, R. Dumitrescu, M. Gräler, V. Just, H. Mucha, S. Peitz, C. Röcker, A. Trächtler, C. Tschirner, S. Wang, J. Wittrowski, in: A. Trächtler (Ed.), Ressourceneffiziente Selbstoptimierende Wäscherei – Ergebnisse des ReSerW-Projekts, Springer, Paderborn, 2018.
LibreCat: /record/22796

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

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

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

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

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

Controlling nonlinear PDEs using low-dimensional bilinear approximations  obtained from data
S. Peitz, ArXiv:1801.06419 (2018).
LibreCat: /record/16292

Analyzing high-dimensional time-series data using kernel transfer  operator eigenfunctions
S. Klus, S. Peitz, I. Schuster, ArXiv:1805.10118 (2018).
LibreCat: /record/16293

Koopman Operator-Based Finite-Control-Set Model Predictive Control for Electrical Drives
S. Hanke, S. Peitz, O. Wallscheid, S. Klus, J. Böcker, M. Dellnitz, ArXiv Preprint ArXiv:1804.00854 (2018).
LibreCat: /record/29624

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

Multiobjective Optimal Control Methods for the Development of an Intelligent Cruise Control
M. Dellnitz, J. Eckstein, K. Flaßkamp, P. Friedel, C. Horenkamp, U. Köhler, S. Ober-Blöbaum, S. Peitz, S. Tiemeyer, in: Progress in Industrial Mathematics at ECMI 2014 , Springer International Publishing, Cham, 2017, pp. 633–641.
LibreCat: /record/5914
DOI: 10.1007/978-3-319-23413-7_87

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

 	Exploiting structure in multiobjective optimization and optimal control
S. Peitz,   Exploiting Structure in Multiobjective Optimization and Optimal Control, 2017.
LibreCat: /record/10594
DOI: 10.17619/UNIPB/1-176

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

Multiobjective Model Predictive Control of an Industrial Laundry
S. Peitz, M. Gräler, C. Henke, M.H. Molo, M. Dellnitz, A. Trächtler, in: Procedia Technology, 2016, pp. 483–490.
LibreCat: /record/8759
DOI: 10.1016/j.protcy.2016.08.061

A comparison of two predictive approaches to control the longitudinal dynamics of electric vehicles
J. Eckstein, S. Peitz, K. Schäfer, P. Friedel, U. Köhler, M. Hessel von Molo, S. Ober-Blöbaum, M. Dellnitz, in: Procedia Technology, 3rd International Conference on System-Integrated Intelligence: New Challenges for Product and Production Engineering, 2016, pp. 465–472.
LibreCat: /record/8758
DOI: 10.1016/j.protcy.2016.08.059

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

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