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64 Publications
2023 | Preprint | LibreCat-ID: 51159 | 
Amakor AC, Sonntag K, Peitz S. A multiobjective continuation method to compute the regularization path of deep neural networks. arXiv. Published online 2023.
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2023 | Preprint | LibreCat-ID: 51158 |
Philipp F, Schaller M, Worthmann K, Peitz S, Nüske F. Error analysis of kernel EDMD for prediction and control in the Koopman framework. arXiv:231210460. Published online 2023.
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| arXiv
2023 | Preprint | LibreCat-ID: 32447 |
Sonntag K, Peitz S. Fast Convergence of Inertial Multiobjective Gradient-like Systems with Asymptotic Vanishing Damping. arXiv:230700975. Published online 2023.
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| arXiv
2023 | Preprint | LibreCat-ID: 46578 |
Bernreuther M, Dellnitz M, Gebken B, et al. Multiobjective Optimization of Non-Smooth PDE-Constrained Problems. arXiv:230801113. Published online 2023.
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| arXiv
2022 | Book Chapter | LibreCat-ID: 16296 |
Banholzer S, Gebken B, Dellnitz M, Peitz S, Volkwein S. ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical Continuation. In: Michael H, Roland H, Christian K, Michael U, Stefan U, eds. Non-Smooth and Complementarity-Based Distributed Parameter Systems. Springer; 2022:43-76. doi:10.1007/978-3-030-79393-7_3
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2022 | Book Chapter | LibreCat-ID: 30294
Peitz S, Dellnitz M, Bannenberg S. Efficient Virtual Design and Testing of Autonomous Vehicles. In: Bock HG, Küfer K-H, Maas P, Milde A, Schulz V, eds. German Success Stories in Industrial Mathematics. Vol 35. Mathematics in Industry. Springer International Publishing; 2022. doi:10.1007/978-3-030-81455-7_23
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2022 | Journal Article | LibreCat-ID: 29673 |
Klus S, Nüske F, Peitz S. Koopman analysis of quantum systems. Journal of Physics A: Mathematical and Theoretical. 2022;55(31):314002. doi:10.1088/1751-8121/ac7d22
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| arXiv
2022 | Preprint | LibreCat-ID: 33150 |
Berkemeier MB, Peitz S. Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients. arXiv:220812094. Published online 2022.
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| arXiv
2022 | Journal Article | LibreCat-ID: 20731 |
Bieker K, Gebken B, Peitz S. On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2022;44(11):7797-7808. doi:10.1109/TPAMI.2021.3114962
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2022 | Book Chapter | LibreCat-ID: 29727
Wohlleben MC, Bender A, Peitz S, Sextro W. Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction. In: Machine Learning, Optimization, and Data Science. Springer International Publishing; 2022. doi:10.1007/978-3-030-95470-3_8
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2021 | Journal Article | LibreCat-ID: 21337 |
Berkemeier MB, Peitz S. Derivative-Free Multiobjective Trust Region Descent Method Using Radial Basis Function Surrogate Models. Mathematical and Computational Applications. 2021;26(2). doi:10.3390/mca26020031
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2021 | Journal Article | LibreCat-ID: 16867 |
Gebken B, Peitz S. An efficient descent method for locally Lipschitz multiobjective optimization problems. Journal of Optimization Theory and Applications. 2021;188:696-723. doi:10.1007/s10957-020-01803-w
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2021 | Journal Article | LibreCat-ID: 16295 |
Gebken B, Peitz S. Inverse multiobjective optimization: Inferring decision criteria from data. Journal of Global Optimization. 2021;80:3-29. doi:10.1007/s10898-020-00983-z
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2021 | Journal Article | LibreCat-ID: 16294 |
Ober-Blöbaum S, Peitz S. Explicit multiobjective model predictive control for nonlinear systems with symmetries. International Journal of Robust and Nonlinear Control. 2021;31(2):380-403. doi:10.1002/rnc.5281
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2020 | Book Chapter | LibreCat-ID: 17411
Flaßkamp K, Ober-Blöbaum S, Peitz S. Symmetry in Optimal Control: A Multiobjective Model Predictive Control Approach. In: Junge O, Schütze O, Froyland G, Ober-Blöbaum S, Padberg-Gehle K, eds. Advances in Dynamics, Optimization and Computation. Cham: Springer; 2020. doi:10.1007/978-3-030-51264-4_9
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2020 | Journal Article | LibreCat-ID: 10596
Schütze O, Cuate O, Martín A, Peitz S, Dellnitz M. Pareto Explorer: a global/local exploration tool for many-objective optimization problems. Engineering Optimization. 2020;52(5):832-855. doi:10.1080/0305215x.2019.1617286
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2020 | Journal Article | LibreCat-ID: 16288
Klus S, Nüske F, Peitz S, Niemann J-H, Clementi C, Schütte C. Data-driven approximation of the Koopman generator: Model reduction, system identification, and control. Physica D: Nonlinear Phenomena. 2020;406. doi:10.1016/j.physd.2020.132416
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2020 | Book Chapter | LibreCat-ID: 16289
Peitz S, Klus S. Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator. In: Lecture Notes in Control and Information Sciences. Vol 484. Lecture Notes in Control and Information Sciences. Cham: Springer; 2020:257-282. doi:10.1007/978-3-030-35713-9_10
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2020 | Journal Article | LibreCat-ID: 16290 |
Bieker K, Peitz S, Brunton SL, Kutz JN, Dellnitz M. Deep model predictive flow control with limited sensor data and online learning. Theoretical and Computational Fluid Dynamics. 2020;34:577–591. doi:10.1007/s00162-020-00520-4
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2020 | Journal Article | LibreCat-ID: 16309
Peitz S, Otto SE, Rowley CW. Data-Driven Model Predictive Control using Interpolated Koopman Generators. SIAM Journal on Applied Dynamical Systems. 2020;19(3):2162-2193. doi:10.1137/20M1325678
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