Philipp, F. M., Schaller, M., Boshoff, S., Peitz, S., Nüske, F., &; Worthmann, K. (2024). Extended Dynamic Mode Decomposition: Sharp bounds on the sample  efficiency. In arXiv:2402.02494.

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 

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.

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

Otto, S. E., Peitz, S., &; Rowley, C. W. (2024). Learning Bilinear Models of Actuated Koopman Generators from  Partially-Observed Trajectories. SIAM Journal on Applied Dynamical Systems, 23(1), 885–923.https://doi.org/10.1137/22M1523601 

Philipp, F., Schaller, M., Worthmann, K., Peitz, S., &; Nüske, F. (2024). Error bounds for kernel-based approximations of the Koopman operator. Applied and Computational Harmonic Analysis , 71, Article 101657.https://doi.org/10.1016/j.acha.2024.101657 

Harder, H., &; Peitz, S. (n.d.). Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines.

Harder, H., &; Peitz, S. (2024). On the continuity and smoothness of the value function in reinforcement learning and optimal control.

Akhter, J., Fährmann, P. D., Sonntag, K., &; Peitz, S. (2024). Common pitfalls to avoid while using multiobjective optimization in machine learning. In arXiv.

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 

Werner, S., &; Peitz, S. (2023). Learning a model is paramount for sample efficiency in reinforcement  learning control of PDEs. In arXiv:2302.07160.

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 

Schaller, M., Worthmann, K., Philipp, F., Peitz, S., &; Nüske, F. (2023). Towards reliable data-based optimal and predictive control using extended DMD. IFAC-PapersOnLine, 56(1), 169–174.https://doi.org/10.1016/j.ifacol.2023.02.029 

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

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 

Hotegni, S. S., Peitz, S., &; Berkemeier, M. B. (2023). Multi-Objective Optimization for Sparse Deep Neural Network Training. In arXiv:2308.12243.

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 

Wallscheid, O., Peitz, S., Stenner, J., Weber, D., Boshoff, S., Meyer, M., Chidananda, V., &; Schweins, O. (2023). ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids. Journal of Open Source Software, 8(89), Article 5616.https://doi.org/10.21105/joss.05616 

Wohlleben, M. C., Muth, L., Peitz, S., &; Sextro, W. (2023). Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits. Proceedings in Applied Mathematics and Mechanics.https://doi.org/10.1002/pamm.202300039 

Peitz, S., Hunstig, A., Rose, H., &; Meier, T. (2023). Accelerating the analysis of optical quantum systems using the Koopman operator.

Amakor, A. C., Sonntag, K., &; Peitz, S. (2023). A multiobjective continuation method to compute the regularization path of deep neural networks. In arXiv.

Philipp, F., Schaller, M., Worthmann, K., Peitz, S., &; Nüske, F. (2023). Error analysis of kernel EDMD for prediction and control in the Koopman  framework. In arXiv:2312.10460.

Sonntag, K., &; Peitz, S. (2023). Fast Convergence of Inertial Multiobjective Gradient-like Systems with Asymptotic Vanishing Damping. In 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.

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 

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 

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 

Berkemeier, M. B., &; Peitz, S. (2022). Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients. In 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 

Wohlleben, M. C., Bender, A., Peitz, S., &; Sextro, W. (2022). Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction. In Machine Learning, Optimization, and Data Science. Springer International Publishing.https://doi.org/10.1007/978-3-030-95470-3_8 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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. 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 1–6.

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.

Kummert, F., Albers, A., Bremer, C., Büttner, S., Dellnitz, M., Dumitrescu, R.,  Wittrowski, J. (2018). Eingesetzte wissenschaftliche Methoden. In A. Trächtler (Ed.), Ressourceneffiziente Selbstoptimierende Wäscherei – Ergebnisse des ReSerW-Projekts. Paderborn: Springer.

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

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

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

Klus, S., Gelß, P., Peitz, S., &; Schütte, C. (2018). Tensor-based dynamic mode decomposition. Nonlinearity, 31(7), 3359–3380.https://doi.org/10.1088/1361-6544/aabc8f 

Beermann, D., Dellnitz, M., Peitz, S., &; Volkwein, S. (2018). POD-based multiobjective optimal control of PDEs with non-smooth objectives. In PAMM (pp. 51–54).https://doi.org/10.1002/pamm.201710015 

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

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

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 Preprint ArXiv:1804.00854.

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

Dellnitz, M., Eckstein, J., Flaßkamp, K., Friedel, P., Horenkamp, C., Köhler, U.,https://doi.org/10.1007/978-3-319-23413-7_87__Tiemeyer, S. (2017). Multiobjective Optimal Control Methods for the Development of an Intelligent Cruise Control. In Progress in Industrial Mathematics at ECMI 2014  (pp. 633–641). Cham: Springer International Publishing.https://doi.org/10.1007/978-3-319-23413-7_87 

Peitz, S., &; Dellnitz, M. (2017). Gradient-Based Multiobjective Optimization with Uncertainties. In NEO 2016 (pp. 159–182). Cham.https://doi.org/10.1007/978-3-319-64063-1_7 

Peitz, S. (2017).   Exploiting structure in multiobjective optimization and optimal control.https://doi.org/10.17619/UNIPB/1-176 

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

Peitz, S., Gräler, M., Henke, C., Molo, M. H., Dellnitz, M., &; Trächtler, A. (2016). Multiobjective Model Predictive Control of an Industrial Laundry. In Procedia Technology (pp. 483–490).https://doi.org/10.1016/j.protcy.2016.08.061 

Eckstein, J., Peitz, S., Schäfer, K., Friedel, P., Köhler, U., Hessel von Molo, M., Ober-Blöbaum, S., &; Dellnitz, M. (2016). A comparison of two predictive approaches to control the longitudinal dynamics of electric vehicles. Procedia Technology, 3rd International Conference on System-Integrated Intelligence: New Challenges for Product and Production Engineering, 26, 465–472.https://doi.org/10.1016/j.protcy.2016.08.059 

Peitz, S., Ober-Blöbaum, S., &; Dellnitz, M. (2016). Reduced order model based multiobjective optimal control of fluids. Proceedings of International Congress of Theoretical and Applied Mechanics.

Peitz, S., &; Dellnitz, M. (2015). Multiobjective Optimization of the Flow Around a Cylinder Using Model Order Reduction. In PAMM (pp. 613–614).https://doi.org/10.1002/pamm.201510296