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64 Publications
2023 | Preprint | LibreCat-ID: 51159 | 
Amakor, A. C., Sonntag, K., & Peitz, S. (2023). A multiobjective continuation method to compute the regularization path of deep neural networks. In arXiv.
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2023 | Preprint | LibreCat-ID: 51158 |
Philipp, F., Schaller, M., Worthmann, K., Peitz, S., & Nüske, F. (2023). Error analysis of kernel EDMD for prediction and control in the Koopman framework. In arXiv:2312.10460.
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| arXiv
2023 | Preprint | LibreCat-ID: 32447 |
Sonntag, K., & Peitz, S. (2023). Fast Convergence of Inertial Multiobjective Gradient-like Systems with Asymptotic Vanishing Damping. In arXiv:2307.00975.
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| arXiv
2023 | Preprint | LibreCat-ID: 46578 |
Bernreuther, M., Dellnitz, M., Gebken, B., Müller, G., Peitz, S., Sonntag, K., & Volkwein, S. (2023). Multiobjective Optimization of Non-Smooth PDE-Constrained Problems. In arXiv:2308.01113.
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| arXiv
2022 | Book Chapter | LibreCat-ID: 16296 |
Banholzer, S., Gebken, B., Dellnitz, M., Peitz, S., & Volkwein, S. (2022). ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical Continuation. In H. Michael, H. Roland, K. Christian, U. Michael, & U. Stefan (Eds.), Non-Smooth and Complementarity-Based Distributed Parameter Systems (pp. 43–76). Springer. https://doi.org/10.1007/978-3-030-79393-7_3
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2022 | Book Chapter | LibreCat-ID: 30294
Peitz, S., Dellnitz, M., & Bannenberg, S. (2022). Efficient Virtual Design and Testing of Autonomous Vehicles. In H. G. Bock, K.-H. Küfer, P. Maas, A. Milde, & V. Schulz (Eds.), German Success Stories in Industrial Mathematics (Vol. 35). Springer International Publishing. https://doi.org/10.1007/978-3-030-81455-7_23
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2022 | Journal Article | LibreCat-ID: 29673 |
Klus, S., Nüske, F., & Peitz, S. (2022). Koopman analysis of quantum systems. Journal of Physics A: Mathematical and Theoretical, 55(31), 314002. https://doi.org/10.1088/1751-8121/ac7d22
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| arXiv
2022 | Preprint | LibreCat-ID: 33150 |
Berkemeier, M. B., & Peitz, S. (2022). Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients. In arXiv:2208.12094.
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| arXiv
2022 | Journal Article | LibreCat-ID: 20731 |
Bieker, K., Gebken, B., & Peitz, S. (2022). On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 7797–7808. https://doi.org/10.1109/TPAMI.2021.3114962
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2022 | Book Chapter | LibreCat-ID: 29727
Wohlleben, M. C., Bender, A., Peitz, S., & Sextro, W. (2022). Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction. In Machine Learning, Optimization, and Data Science. Springer International Publishing. https://doi.org/10.1007/978-3-030-95470-3_8
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2021 | Journal Article | LibreCat-ID: 21337 |
Berkemeier, M. B., & Peitz, S. (2021). Derivative-Free Multiobjective Trust Region Descent Method Using Radial Basis Function Surrogate Models. Mathematical and Computational Applications, 26(2). https://doi.org/10.3390/mca26020031
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2021 | Journal Article | LibreCat-ID: 16867 |
Gebken, B., & Peitz, S. (2021). An efficient descent method for locally Lipschitz multiobjective optimization problems. Journal of Optimization Theory and Applications, 188, 696–723. https://doi.org/10.1007/s10957-020-01803-w
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2021 | Journal Article | LibreCat-ID: 16295 |
Gebken, B., & Peitz, S. (2021). Inverse multiobjective optimization: Inferring decision criteria from data. Journal of Global Optimization, 80, 3–29. https://doi.org/10.1007/s10898-020-00983-z
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2021 | Journal Article | LibreCat-ID: 16294 |
Ober-Blöbaum, S., & Peitz, S. (2021). Explicit multiobjective model predictive control for nonlinear systems with symmetries. International Journal of Robust and Nonlinear Control, 31(2), 380–403. https://doi.org/10.1002/rnc.5281
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2020 | Book Chapter | LibreCat-ID: 17411
Flaßkamp, K., Ober-Blöbaum, S., & Peitz, S. (2020). Symmetry in Optimal Control: A Multiobjective Model Predictive Control Approach. In O. Junge, O. Schütze, G. Froyland, S. Ober-Blöbaum, & K. Padberg-Gehle (Eds.), Advances in Dynamics, Optimization and Computation. Cham: Springer. https://doi.org/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. (2020). Pareto Explorer: a global/local exploration tool for many-objective optimization problems. Engineering Optimization, 52(5), 832–855. https://doi.org/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. (2020). Data-driven approximation of the Koopman generator: Model reduction, system identification, and control. Physica D: Nonlinear Phenomena, 406. https://doi.org/10.1016/j.physd.2020.132416
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2020 | Book Chapter | LibreCat-ID: 16289
Peitz, S., & Klus, S. (2020). 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, pp. 257–282). Cham: Springer. https://doi.org/10.1007/978-3-030-35713-9_10
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2020 | Journal Article | LibreCat-ID: 16290 |
Bieker, K., Peitz, S., Brunton, S. L., Kutz, J. N., & Dellnitz, M. (2020). Deep model predictive flow control with limited sensor data and online learning. Theoretical and Computational Fluid Dynamics, 34, 577–591. https://doi.org/10.1007/s00162-020-00520-4
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2020 | Journal Article | LibreCat-ID: 16309
Peitz, S., Otto, S. E., & Rowley, C. W. (2020). Data-Driven Model Predictive Control using Interpolated Koopman Generators. SIAM Journal on Applied Dynamical Systems, 19(3), 2162–2193. https://doi.org/10.1137/20M1325678
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