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67 Publications
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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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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2020 | Journal Article | LibreCat-ID: 16297
Hernández Castellanos CI, Ober-Blöbaum S, Peitz S. Explicit Multi-objective Model Predictive Control for Nonlinear Systems Under Uncertainty. International Journal of Robust and Nonlinear Control. 2020;30(17):7593-7618. doi:10.1002/rnc.5197
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2019 | Journal Article | LibreCat-ID: 10593
Peitz S, Klus S. Koopman operator-based model reduction for switched-system control of PDEs. Automatica. 2019;106:184-191. doi:10.1016/j.automatica.2019.05.016
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2019 | Journal Article | LibreCat-ID: 10595
Gebken B, Peitz S, Dellnitz M. On the hierarchical structure of Pareto critical sets. Journal of Global Optimization. 2019;73(4):891-913. doi:10.1007/s10898-019-00737-6
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2019 | Conference Paper | LibreCat-ID: 10597
Hanke S, Peitz S, Wallscheid O, Böcker J, Dellnitz M. Finite-Control-Set Model Predictive Control for a Permanent Magnet Synchronous Motor Application with Online Least Squares System Identification. In: 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE). ; 2019. doi:10.1109/precede.2019.8753313
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2019 | Conference Paper | LibreCat-ID: 29636
Hanke S, Peitz S, Wallscheid O, Böcker J, Dellnitz M. Finite-control-set model predictive control for a permanent magnet synchronous motor application with online least squares system identification. In: 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE). ; 2019:1–6.
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2018 | Preprint | LibreCat-ID: 21634 |
Hanke S, Peitz S, Wallscheid O, Klus S, Böcker J, Dellnitz M. Koopman Operator-Based Finite-Control-Set Model Predictive Control for Electrical Drives. arXiv:180400854. 2018.
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2018 | Book Chapter | LibreCat-ID: 22796
Kummert F, Albers A, Bremer C, et al. Eingesetzte wissenschaftliche Methoden. In: Trächtler A, ed. Ressourceneffiziente Selbstoptimierende Wäscherei – Ergebnisse des ReSerW-Projekts. Paderborn: Springer; 2018.
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