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