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

M. Schaller, K. Worthmann, F. Philipp, S. Peitz, and F. Nüske, “Towards reliable data-based optimal and predictive control using extended DMD,”10.1016/j.ifacol.2023.02.029__in IFAC-PapersOnLine, 2023, vol. 56, no. 1, pp. 169–174, doi:10.1016/j.ifacol.2023.02.029 .
LibreCat: /record/30125
DOI: 10.1016/j.ifacol.2023.02.029
arXiv: 2202.09084

S. S. Hotegni, S. Mahabadi, and A. Vakilian, “Approximation Algorithms for Fair Range Clustering,” presented at the International Conference on Machine Learning, Honolulu, Hawaii, USA.
LibreCat: /record/45695

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

F. Nüske, S. Peitz, F. Philipp, M. Schaller, and K. Worthmann, “Finite-data error bounds for Koopman-based prediction and control,”10.1007/s00332-022-09862-1__Journal of Nonlinear Science, vol. 33, Art. no. 14, 2023, doi:10.1007/s00332-022-09862-1 .
LibreCat: /record/23428
DOI: 10.1007/s00332-022-09862-1

S. S. Hotegni, S. Peitz, and M. B. Berkemeier, “Multi-Objective Optimization for Sparse Deep Neural Network Training,” arXiv:2308.12243. 2023.
LibreCat: /record/46649
arXiv: 2308.12243

M. Dellnitz et al., “Efficient time stepping for numerical integration using reinforcement  learning,”10.1137/21M1412682__SIAM Journal on Scientific Computing, vol. 45, no. 2, pp. 9–5, 2023, doi:10.1137/21M1412682 .
LibreCat: /record/21600
DOI: 10.1137/21M1412682
arXiv: arXiv:2104.03562

O. Wallscheid 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, Art. no. 5616, 2023, doi:10.21105/joss.05616 .
LibreCat: /record/46784
DOI: 10.21105/joss.05616

M. C. Wohlleben, L. Muth, S. Peitz, and W. Sextro, “Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits,”10.1002/pamm.202300039__2023, doi:10.1002/pamm.202300039 .
LibreCat: /record/46813
DOI: 10.1002/pamm.202300039

S. Peitz, A. Hunstig, H. Rose, and T. Meier, “Accelerating the analysis of optical quantum systems using the Koopman operator.” 2023.
LibreCat: /record/48502

A. C. Amakor, K. Sonntag, and S. Peitz, “A multiobjective continuation method to compute the regularization path of deep neural networks,” arXiv. 2023.
LibreCat: /record/51159