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66 Publications
2024 | Preprint | LibreCat-ID: 51160 | 
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.
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| arXiv
2024 | Journal Article | LibreCat-ID: 46019 |
K. Sonntag and S. Peitz, “Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems,” Journal of Optimization Theory and Applications, 2024, doi: 10.1007/s10957-024-02389-3.
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2024 | Preprint | LibreCat-ID: 51334 |
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.
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| arXiv
2024 | Journal Article | LibreCat-ID: 40171 |
S. Peitz, J. Stenner, V. Chidananda, O. Wallscheid, S. L. Brunton, and K. Taira, “Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning,” Physica D: Nonlinear Phenomena, vol. 461, p. 134096, 2024, doi: 10.1016/j.physd.2024.134096.
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2024 | Journal Article | LibreCat-ID: 33461 |
S. E. Otto, S. Peitz, and C. W. Rowley, “Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories,” SIAM Journal on Applied Dynamical Systems, vol. 23, no. 1, pp. 885–923, 2024, doi: 10.1137/22M1523601.
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| arXiv
2024 | Journal Article | LibreCat-ID: 38031 |
F. Philipp, M. Schaller, K. Worthmann, S. Peitz, and F. Nüske, “Error bounds for kernel-based approximations of the Koopman operator,” Applied and Computational Harmonic Analysis , vol. 71, Art. no. 101657, 2024, doi: 10.1016/j.acha.2024.101657.
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| arXiv
2024 | Preprint | LibreCat-ID: 53793 |
H. Harder and S. Peitz, “Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines.” .
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2024 | Preprint | LibreCat-ID: 52758
H. Harder and S. Peitz, “On the continuity and smoothness of the value function in reinforcement learning and optimal control.” 2024.
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2024 | Preprint | LibreCat-ID: 53858 |
J. Akhter, P. D. Fährmann, K. Sonntag, and S. Peitz, “Common pitfalls to avoid while using multiobjective optimization in machine learning,” arXiv. 2024.
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2024 | Journal Article | LibreCat-ID: 32447
K. Sonntag and S. Peitz, “Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping,” SIAM Journal on Optimization, vol. 34, no. 3, pp. 2259–2286, 2024, doi: 10.1137/23M1588512.
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2024 | Conference Paper | LibreCat-ID: 46649 |
S. S. Hotegni, M. B. Berkemeier, and S. Peitz, “Multi-Objective Optimization for Sparse Deep Multi-Task Learning,” in 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 2024, p. 9, doi: 10.1109/IJCNN60899.2024.10650994.
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2023 | Journal Article | LibreCat-ID: 21199 |
S. Peitz and K. Bieker, “On the Universal Transformation of Data-Driven Models to Control Systems,” Automatica, vol. 149, Art. no. 110840, 2023, doi: 10.1016/j.automatica.2022.110840.
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2023 | Preprint | LibreCat-ID: 48502 |
S. Peitz, A. Hunstig, H. Rose, and T. Meier, “Accelerating the analysis of optical quantum systems using the Koopman operator.” 2023.
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2023 | Preprint | LibreCat-ID: 51159 |
A. C. Amakor, K. Sonntag, and S. Peitz, “A multiobjective continuation method to compute the regularization path of deep neural networks,” arXiv. 2023.
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2023 | Preprint | LibreCat-ID: 51158 |
F. Philipp, M. Schaller, K. Worthmann, S. Peitz, and F. Nüske, “Error analysis of kernel EDMD for prediction and control in the Koopman framework,” arXiv:2312.10460. 2023.
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| arXiv
2023 | Preprint | LibreCat-ID: 46578 |
M. Bernreuther et al., “Multiobjective Optimization of Non-Smooth PDE-Constrained Problems,” arXiv:2308.01113. 2023.
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| arXiv
2023 | Conference Paper | LibreCat-ID: 54838
S. Boshoff et al., “Hybrid control of interconnected power converters using both expert-driven droop and data-driven reinforcement learning approaches,” in IEEE Power and Energy Student Summit (PESS), 2023, pp. 124–129.
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2023 | Conference Paper | LibreCat-ID: 54839
M. Meyer et al., “ElectricGrid.jl – Automated modeling of decentralized electrical energy grids,” in IEEE Power and Energy Student Summit (PESS), 2023, pp. 112–117.
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2023 | Preprint | LibreCat-ID: 42160 |
S. Werner and S. Peitz, “Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs,” arXiv:2302.07160. 2023.
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| arXiv
2023 | Journal Article | LibreCat-ID: 27426 |
B. Gebken, K. Bieker, and S. Peitz, “On the structure of regularization paths for piecewise differentiable regularization terms,” Journal of Global Optimization, vol. 85, no. 3, pp. 709–741, 2023, doi: 10.1007/s10898-022-01223-2.
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