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66 Publications
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: 32447 |
K. Sonntag and S. Peitz, “Fast Convergence of Inertial Multiobjective Gradient-like Systems with Asymptotic Vanishing Damping,” arXiv:2307.00975. 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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2022 | Book Chapter | LibreCat-ID: 16296 |
S. Banholzer, B. Gebken, M. Dellnitz, S. Peitz, and S. Volkwein, “ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical Continuation,” in Non-Smooth and Complementarity-Based Distributed Parameter Systems, H. Michael, H. Roland, K. Christian, U. Michael, and U. Stefan, Eds. Cham: Springer, 2022, pp. 43–76.
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2022 | Book Chapter | LibreCat-ID: 30294
S. Peitz, M. Dellnitz, and S. Bannenberg, “Efficient Virtual Design and Testing of Autonomous Vehicles,” in German Success Stories in Industrial Mathematics, vol. 35, H. G. Bock, K.-H. Küfer, P. Maas, A. Milde, and V. Schulz, Eds. Cham: Springer International Publishing, 2022.
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2022 | Journal Article | LibreCat-ID: 29673 |
S. Klus, F. Nüske, and S. Peitz, “Koopman analysis of quantum systems,” Journal of Physics A: Mathematical and Theoretical, vol. 55, no. 31, p. 314002, 2022, doi: 10.1088/1751-8121/ac7d22.
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| arXiv
2022 | Preprint | LibreCat-ID: 33150 |
M. B. Berkemeier and S. Peitz, “Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients,” arXiv:2208.12094. 2022.
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| arXiv
2022 | Journal Article | LibreCat-ID: 20731 |
K. Bieker, B. Gebken, and S. Peitz, “On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 11, pp. 7797–7808, 2022, doi: 10.1109/TPAMI.2021.3114962.
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2022 | Book Chapter | LibreCat-ID: 29727
M. C. Wohlleben, A. Bender, S. Peitz, and W. Sextro, “Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction,” in Machine Learning, Optimization, and Data Science, Cham: Springer International Publishing, 2022.
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2021 | Journal Article | LibreCat-ID: 21337 |
M. B. Berkemeier and S. Peitz, “Derivative-Free Multiobjective Trust Region Descent Method Using Radial Basis Function Surrogate Models,” Mathematical and Computational Applications, vol. 26, no. 2, 2021.
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2021 | Journal Article | LibreCat-ID: 16867 |
B. Gebken and S. Peitz, “An efficient descent method for locally Lipschitz multiobjective optimization problems,” Journal of Optimization Theory and Applications, vol. 188, pp. 696–723, 2021.
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2021 | Journal Article | LibreCat-ID: 16295 |
B. Gebken and S. Peitz, “Inverse multiobjective optimization: Inferring decision criteria from data,” Journal of Global Optimization, vol. 80, pp. 3–29, 2021.
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2021 | Journal Article | LibreCat-ID: 16294 |
S. Ober-Blöbaum and S. Peitz, “Explicit multiobjective model predictive control for nonlinear systems with symmetries,” International Journal of Robust and Nonlinear Control, vol. 31(2), pp. 380–403, 2021, doi: 10.1002/rnc.5281.
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2020 | Book Chapter | LibreCat-ID: 17411
K. Flaßkamp, S. Ober-Blöbaum, and S. Peitz, “Symmetry in Optimal Control: A Multiobjective Model Predictive Control Approach,” in Advances in Dynamics, Optimization and Computation, O. Junge, O. Schütze, G. Froyland, S. Ober-Blöbaum, and K. Padberg-Gehle, Eds. Cham: Springer, 2020.
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