F. M. Philipp, M. Schaller, S. Boshoff, S. Peitz, F. Nüske, and K. Worthmann, “Extended Dynamic Mode Decomposition: Sharp bounds on the sample  efficiency,” arXiv:2402.02494. 2024.
LibreCat: /record/51160
arXiv: 2402.02494

K. Sonntag and S. 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, 2024, doi:10.1007/s10957-024-02389-3 .
LibreCat: /record/46019
DOI: 10.1007/s10957-024-02389-3

K. Sonntag, B. Gebken, G. Müller, S. Peitz, and S. Volkwein, “A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces,” arXiv:2402.06376. 2024.
LibreCat: /record/51334
arXiv: 	2402.06376

S. Peitz, J. Stenner, V. Chidananda, O. Wallscheid, S. L. Brunton, and K. Taira, “Distributed Control of Partial Differential Equations Using  Convolutional Reinforcement Learning,”10.1016/j.physd.2024.134096__Physica D: Nonlinear Phenomena, vol. 461, p. 134096, 2024, doi:10.1016/j.physd.2024.134096 .
LibreCat: /record/40171
DOI: 10.1016/j.physd.2024.134096

S. E. Otto, S. Peitz, and C. W. Rowley, “Learning Bilinear Models of Actuated Koopman Generators from  Partially-Observed Trajectories,”10.1137/22M1523601__SIAM Journal on Applied Dynamical Systems, vol. 23, no. 1, pp. 885–923, 2024, doi:10.1137/22M1523601 .
LibreCat: /record/33461
DOI: 10.1137/22M1523601
arXiv: 2209.09977

F. Philipp, M. Schaller, K. Worthmann, S. Peitz, and F. Nüske, “Error bounds for kernel-based approximations of the Koopman operator,”10.1016/j.acha.2024.101657__Applied and Computational Harmonic Analysis , vol. 71, Art. no. 101657, 2024, doi:10.1016/j.acha.2024.101657 .
LibreCat: /record/38031
DOI: 10.1016/j.acha.2024.101657
arXiv: 2301.08637

J. Akhter, P. D. Fährmann, K. Sonntag, and S. Peitz, “Common pitfalls to avoid while using multiobjective optimization in machine learning,” arXiv. 2024.
LibreCat: /record/53858

S. Peitz and K. Bieker, “On the Universal Transformation of Data-Driven Models to Control Systems,”10.1016/j.automatica.2022.110840__Automatica, vol. 149, Art. no. 110840, 2023, doi:10.1016/j.automatica.2022.110840 .
LibreCat: /record/21199
DOI: 10.1016/j.automatica.2022.110840

S. Werner and S. Peitz, “Learning a model is paramount for sample efficiency in reinforcement  learning control of PDEs,” arXiv:2302.07160. 2023.
LibreCat: /record/42160
arXiv: 2302.07160

B. Gebken, K. Bieker, and S. Peitz, “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, pp. 709–741, 2023, doi:10.1007/s10898-022-01223-2 .
LibreCat: /record/27426
DOI: 10.1007/s10898-022-01223-2