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.

Werner, Stefan, and Sebastian Peitz. “Learning a Model Is Paramount for Sample Efficiency in Reinforcement  Learning Control of PDEs.” ArXiv:2302.07160, 2023.

Gebken, Bennet, et al. “On the Structure of Regularization Paths for Piecewise Differentiable Regularization Terms.”10.1007/s10898-022-01223-2__Journal of Global Optimization, vol. 85, no. 3, 2023, pp. 709–41, doi:10.1007/s10898-022-01223-2.

Schaller, Manuel, et al. “Towards Reliable Data-Based Optimal and Predictive Control Using Extended DMD.”10.1016/j.ifacol.2023.02.029__IFAC-PapersOnLine, vol. 56, no. 1, 2023, pp. 169–74, doi:10.1016/j.ifacol.2023.02.029.

Peitz, Sebastian, et al. “Partial Observations, Coarse Graining and Equivariance in Koopman  Operator Theory for Large-Scale Dynamical Systems.” ArXiv:2307.15325, 2023.

Nüske, Feliks, et al. “Finite-Data Error Bounds for Koopman-Based Prediction and Control.”10.1007/s00332-022-09862-1__Journal of Nonlinear Science, vol. 33, 14, 2023, doi:10.1007/s00332-022-09862-1.

Hotegni, Sedjro Salomon, et al. “Multi-Objective Optimization for Sparse Deep Neural Network Training.” ArXiv:2308.12243, 2023.

Dellnitz, Michael, et al. “Efficient Time Stepping for Numerical Integration Using Reinforcement  Learning.”10.1137/21M1412682__SIAM Journal on Scientific Computing, vol. 45, no. 2, 2023, pp. 9–95, doi:10.1137/21M1412682.

Wallscheid, Oliver, et al. “ElectricGrid.Jl - A Julia-Based Modeling and Simulationtool for Power Electronics-Driven Electric Energy Grids.”10.21105/joss.05616__Journal of Open Source Software, vol. 8, no. 89, 5616, The Open Journal, 2023, doi:10.21105/joss.05616.

Wohlleben, Meike Claudia, et al. “Transferability of a Discrepancy Model for the Dynamics of Electromagnetic Oscillating Circuits.”10.1002/pamm.202300039__Proceedings in Applied Mathematics and Mechanics, Wiley, 2023, doi:10.1002/pamm.202300039.

Peitz, Sebastian, et al. Accelerating the Analysis of Optical Quantum Systems Using the Koopman Operator. 2023.