Philipp, Friedrich M., et al. “Extended Dynamic Mode Decomposition: Sharp Bounds on the Sample  Efficiency.” ArXiv:2402.02494, 2024.

Sonntag, Konstantin, and Sebastian Peitz. “Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems.”10.1007/s10957-024-02389-3__Journal of Optimization Theory and Applications, Springer, 2024, doi:10.1007/s10957-024-02389-3.

Sonntag, Konstantin, et al. “A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces.” ArXiv:2402.06376, 2024.

Peitz, Sebastian, et al. “Distributed Control of Partial Differential Equations Using  Convolutional Reinforcement Learning.”10.1016/j.physd.2024.134096__Physica D: Nonlinear Phenomena, vol. 461, Elsevier, 2024, p. 134096, doi:10.1016/j.physd.2024.134096.

Otto, Samuel E., et al. “Learning Bilinear Models of Actuated Koopman Generators from  Partially-Observed Trajectories.”10.1137/22M1523601__SIAM Journal on Applied Dynamical Systems, vol. 23, no. 1, SIAM, 2024, pp. 885–923, doi:10.1137/22M1523601.

Philipp, Friedrich, et al. “Error Bounds for Kernel-Based Approximations of the Koopman Operator.”10.1016/j.acha.2024.101657__Applied and Computational Harmonic Analysis , vol. 71, 101657, Springer , 2024, doi:10.1016/j.acha.2024.101657.

Harder, Hans, and Sebastian Peitz. Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines.

Harder, Hans, and Sebastian Peitz. On the Continuity and Smoothness of the Value Function in Reinforcement Learning and Optimal Control. 2024.

Akhter, Junaid, et al. “Common Pitfalls to Avoid While Using Multiobjective Optimization in Machine Learning.” ArXiv, 2024.

Peitz, Sebastian, and Katharina Bieker. “On the Universal Transformation of Data-Driven Models to Control Systems.”10.1016/j.automatica.2022.110840__Automatica, vol. 149, 110840, Elsevier, 2023, doi:10.1016/j.automatica.2022.110840.