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63 Publications
2024 | Preprint | LibreCat-ID: 51160 | 
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.
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| arXiv
2024 | Journal Article | LibreCat-ID: 46019 |
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
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2024 | Preprint | LibreCat-ID: 51334 |
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.
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| arXiv
2024 | Journal Article | LibreCat-ID: 40171 |
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
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2024 | Journal Article | LibreCat-ID: 33461 |
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
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| arXiv
2024 | Journal Article | LibreCat-ID: 38031 |
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
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| arXiv
2024 | Preprint | LibreCat-ID: 53793 |
Harder, H., & Peitz, S. (n.d.). Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines.
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2024 | Preprint | LibreCat-ID: 52758
Harder, H., & Peitz, S. (2024). On the continuity and smoothness of the value function in reinforcement learning and optimal control.
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2023 | Journal Article | LibreCat-ID: 21199 |
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
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2023 | Preprint | LibreCat-ID: 42160 |
Werner, S., & Peitz, S. (2023). Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs. In arXiv:2302.07160.
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| arXiv