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.

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

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.

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

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

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

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

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

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

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

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

Hotegni SS, Mahabadi S, Vakilian A. Approximation Algorithms for Fair Range Clustering. In: Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023.

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.

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

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

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

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

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

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

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