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.
LibreCat: /record/51160
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 
LibreCat: /record/46019
DOI: 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.
LibreCat: /record/51334
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 
LibreCat: /record/40171
DOI: 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 
LibreCat: /record/33461
DOI: 10.1137/22M1523601
arXiv: 2209.09977

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 
LibreCat: /record/38031
DOI: 10.1016/j.acha.2024.101657
arXiv: 2301.08637

Akhter, J., Fährmann, P. D., Sonntag, K., &; Peitz, S. (2024). Common pitfalls to avoid while using multiobjective optimization in machine learning. In arXiv.
LibreCat: /record/53858

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 
LibreCat: /record/21199
DOI: 10.1016/j.automatica.2022.110840

Werner, S., &; Peitz, S. (2023). Learning a model is paramount for sample efficiency in reinforcement  learning control of PDEs. In arXiv:2302.07160.
LibreCat: /record/42160
arXiv: 2302.07160

Gebken, B., Bieker, K., &; Peitz, S. (2023). On the structure of regularization paths for piecewise differentiable regularization terms. Journal of Global Optimization, 85(3), 709–741.https://doi.org/10.1007/s10898-022-01223-2 
LibreCat: /record/27426
DOI: 10.1007/s10898-022-01223-2

Schaller, M., Worthmann, K., Philipp, F., Peitz, S., &; Nüske, F. (2023). Towards reliable data-based optimal and predictive control using extended DMD. IFAC-PapersOnLine, 56(1), 169–174.https://doi.org/10.1016/j.ifacol.2023.02.029 
LibreCat: /record/30125
DOI: 10.1016/j.ifacol.2023.02.029
arXiv: 2202.09084

Hotegni, S. S., Mahabadi, S., &; Vakilian, A. (n.d.). Approximation Algorithms for Fair Range Clustering. Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023. International Conference on Machine Learning, Honolulu, Hawaii, USA.
LibreCat: /record/45695

Peitz, S., Harder, H., Nüske, F., Philipp, F., Schaller, M., &; Worthmann, K. (2023). Partial observations, coarse graining and equivariance in Koopman  operator theory for large-scale dynamical systems. In arXiv:2307.15325.
LibreCat: /record/46579
arXiv: 2307.15325

Nüske, F., Peitz, S., Philipp, F., Schaller, M., &; Worthmann, K. (2023). Finite-data error bounds for Koopman-based prediction and control. Journal of Nonlinear Science, 33, Article 14.https://doi.org/10.1007/s00332-022-09862-1 
LibreCat: /record/23428
DOI: 10.1007/s00332-022-09862-1

Hotegni, S. S., Peitz, S., &; Berkemeier, M. B. (2023). Multi-Objective Optimization for Sparse Deep Neural Network Training. In arXiv:2308.12243.
LibreCat: /record/46649
arXiv: 2308.12243

Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz, S., &; Pfannschmidt, K. (2023). Efficient time stepping for numerical integration using reinforcement  learning. SIAM Journal on Scientific Computing, 45(2), 9–5.https://doi.org/10.1137/21M1412682 
LibreCat: /record/21600
DOI: 10.1137/21M1412682
arXiv: arXiv:2104.03562

Wallscheid, O., Peitz, S., Stenner, J., Weber, D., Boshoff, S., Meyer, M., Chidananda, V., &; Schweins, O. (2023). ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids. Journal of Open Source Software, 8(89), Article 5616.https://doi.org/10.21105/joss.05616 
LibreCat: /record/46784
DOI: 10.21105/joss.05616

Wohlleben, M. C., Muth, L., Peitz, S., &; Sextro, W. (2023). Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits. Proceedings in Applied Mathematics and Mechanics.https://doi.org/10.1002/pamm.202300039 
LibreCat: /record/46813
DOI: 10.1002/pamm.202300039

Peitz, S., Hunstig, A., Rose, H., &; Meier, T. (2023). Accelerating the analysis of optical quantum systems using the Koopman operator.
LibreCat: /record/48502

Amakor, A. C., Sonntag, K., &; Peitz, S. (2023). A multiobjective continuation method to compute the regularization path of deep neural networks. In arXiv.
LibreCat: /record/51159