Sebastian Peitz

Data Science for Engineering
sebastian.peitz@uni-paderborn.de
ID
47427

90 Publications

Mark all

[90]
2026 | Journal Article | LibreCat-ID: 51160 | OA
Philipp, F. M., Schaller, M., Boshoff, S., Peitz, S., Nüske, F., & Worthmann, K. (2026). Variance representations and convergence rates for data-driven approximations of Koopman operators. Physica D: Nonlinear Phenomena, 492, Article 135223. https://doi.org/10.1016/j.physd.2026.135223
LibreCat | Files available | DOI | Download (ext.) | arXiv
 
[89]
2026 | Journal Article | LibreCat-ID: 53858 | OA
Akhter, J., Fährmann, P. D., Sonntag, K., & Peitz, S. (2026). Common pitfalls to avoid while using multiobjective optimization in machine learning. SIAM Review, 68(3), 656–683. https://doi.org/10.1137/24M1658875
LibreCat | DOI | Download (ext.)
 
[88]
2026 | Conference Paper | LibreCat-ID: 67308
Amakor, A. C., Berkemeier, M. B., Wohlleben, M. C., Sextro, W., & Peitz, S. (2026). Surrogate-Assisted Multi-objective Design of Complex Multibody Systems. In W. Senn, M. Sanguineti, A. Saudargiene, I. V. Tetko, A. E. P. Villa, V. Jirsa, & Y. Bengio (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2025 (pp. 251–262). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-04555-3_21
LibreCat | DOI
 
[87]
2026 | Conference Paper | LibreCat-ID: 67307 | OA
Hotegni, S. S., & Peitz, S. (2026). SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion. 14th International Conference on Learning Representations (ICLR). https://doi.org/10.48550/arXiv.2509.21058
LibreCat | DOI | Download (ext.)
 
[86]
2026 | Preprint | LibreCat-ID: 67306 | OA
Stenner, J., Harder, H., & Peitz, S. (2026). Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection. In arXiv:2606.30238.
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[85]
2026 | Preprint | LibreCat-ID: 67309
Stenner, J., Kilian, A., Peitz, S., & de Meer, H. (2026). Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning. In arXiv:2606.30316.
LibreCat
 
[84]
2026 | Conference Paper | LibreCat-ID: 67300
Hotegni, S. S., & Peitz, S. (2026). Enhancing Adversarial Robustness Through Multi-objective Representation Learning. In W. Senn, M. Sanguineti, A. Saudargiene, I. V. Tetko, A. E. P. Villa, V. Jirsa, & Y. Bengio (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2025 (pp. 442–454). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-04558-4_35
LibreCat | DOI
 
[83]
2026 | Journal Article | LibreCat-ID: 67302
Markmann, T., Straat, M., Peitz, S., & Hammer, B. (2026). Fourier neural operators as data-driven surrogates for two- and three-dimensional Rayleigh–Bénard convection. Neurocomputing, 679, 133201. https://doi.org/10.1016/j.neucom.2026.133201
LibreCat | DOI
 
[82]
2026 | Conference Paper | LibreCat-ID: 67299 | OA
Harder, H., Vishwasrao, A., Guastoni, L., Vinuesa, R., & Peitz, S. (2026). Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems. In G. Sukhatme, L. Lindemann, S. Tu, A. Wierman, & N. Atanasov (Eds.), Proceedings of The 8th Annual Learning for Dynamics and Control Conference (Vol. 331, pp. 1601–1619). PMLR. https://doi.org/10.48550/arXiv.2511.04641
LibreCat | DOI | Download (ext.)
 
[81]
2026 | Conference Paper | LibreCat-ID: 67303 | OA
Amakor, A. C., Sonntag, K., & Peitz, S. (2026). Interactive Pareto navigation for deep multi-task learning. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 652–668. https://doi.org/10.1007/978-3-032-37667-1_37
LibreCat | DOI | Download (ext.)
 
[80]
2026 | Conference Paper | LibreCat-ID: 67305 | OA
Becktepe, J., Franz, A., Thuerey, N., & Peitz, S. (2026). Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control. International Conference on Machine Learning (ICML). https://doi.org/10.48550/arXiv.2601.15015
LibreCat | DOI | Download (ext.)
 
[79]
2026 | Journal Article | LibreCat-ID: 67301
Wohlleben, M. C., Schütte, J., Berkemeier, M., Sextro, W., & Peitz, S. (2026). Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings. Multibody System Dynamics. https://doi.org/10.1007/s11044-026-10146-9
LibreCat | DOI
 
[78]
2026 | Preprint | LibreCat-ID: 67298 | OA
Mugisho Zagabe, C., & Peitz, S. (2026). Automatic feature identification in least-squares policy iteration using the Koopman operator framework. In arXiv:2603.26464.
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[77]
2026 | Preprint | LibreCat-ID: 67304 | OA
Plotzki, T., & Peitz, S. (2026). Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection. In arXiv:2603.28074.
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[76]
2025 | Journal Article | LibreCat-ID: 51159
Amakor, A. C., Sonntag, K., & Peitz, S. (2025). A multiobjective continuation method to compute the regularization path of deep neural networks. Machine Learning with Applications, 19, Article 100625. https://doi.org/10.1016/j.mlwa.2025.100625
LibreCat | Files available | DOI
 
[75]
2025 | Journal Article | LibreCat-ID: 46579 | OA
Peitz, S., Harder, H., Nüske, F., Philipp, F., Schaller, M., & Worthmann, K. (2025). Equivariance and partial observations in Koopman operator theory for partial differential equations. Journal of Computational Dynamics, 12, 305–324. https://doi.org/10.3934/jcd.2024035
LibreCat | DOI | Download (ext.) | arXiv
 
[74]
2025 | Conference Paper | LibreCat-ID: 53793 | OA
Harder, H., & Peitz, S. (2025). Solving partial differential equations with equivariant extreme learning machines. In J. Berberich, A. Ianelli, & F. Allgöwer (Eds.), Systems theory in data and optimization (pp. 153–168). Springer. https://doi.org/10.1007/978-3-031-83191-1_10
LibreCat | DOI | Download (ext.)
 
[73]
2025 | Conference Paper | LibreCat-ID: 67313
Voth, D., Dane, L., Grebe, J., Peitz, S., & Terhörst, P. (2025). Effective Backdoor Learning on Open-Set Face Recognition Systems. 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 1027–1039. https://doi.org/10.1109/WACV61041.2025.00109
LibreCat | DOI
 
[72]
2025 | Preprint | LibreCat-ID: 67312
Markmann, T., Straat, M., Peitz, S., & Hammer, B. (2025). Control of Rayleigh-Bénard Convection: Effectiveness of Reinforcement Learning in the Turbulent Regime. In arXiv:2504.12000.
LibreCat
 
[71]
2025 | Preprint | LibreCat-ID: 67310
Boshoff, S., Peitz, S., & Klus, S. (2025). Bayesian Transfer Operators in Reproducing Kernel Hilbert Spaces. In arXiv:2509.22482.
LibreCat
 
[70]
2025 | Journal Article | LibreCat-ID: 57829
de Payrebrune, K. M., Flaßkamp, K., Ströhla, T., Sattel, T., Bestle, D., Röder, B., Eberhard, P., Peitz, S., Stoffel, M., Rutwik, G., Aditya, B., Wohlleben, M. C., Sextro, W., Raff, M., Remy, C. D., Yadav, M., Stender, M., van Delden, J., Lüddecke, T., … Blech, C. (2025). The impact of AI on engineering design procedures for dynamical systems. Technische Mechanik - European Journal of Engineering Mechanics, 45(1), 1–23. https://doi.org/10.24352/UB.OVGU-2025-037
LibreCat | DOI
 
[69]
2025 | Journal Article | LibreCat-ID: 51158 | OA
Philipp, F., Schaller, M., Worthmann, K., Peitz, S., & Nüske, F. (2025). Error analysis of kernel EDMD for prediction and control in the Koopman  framework. Ournal of Nonlinear Science, 35, Article 92. https://doi.org/10.1007/s00332-025-10182-3
LibreCat | DOI | Download (ext.) | arXiv
 
[68]
2025 | Journal Article | LibreCat-ID: 67319
de Payrebrune, K. M., Flaßkamp, K., Ströhla, T., Sattel, T., Bestle, D., Röder, B., Eberhard, P., Peitz, S., Stoffel, M., Rutwik, G., Aditya, B., Wohlleben, M., Sextro, W., Raff, M., Remy, C. D., Yadav, M., Stender, M., van Delden, J., Lüddecke, T., … Blech, C. (2025). The impact of AI on engineering design procedures for dynamical systems. Technische Mechanik - European Journal of Engineering Mechanics, 45(1), 1–23. https://doi.org/10.24352/UB.OVGU-2025-037
LibreCat | DOI
 
[67]
2025 | Journal Article | LibreCat-ID: 67317
Berkemeier, M., & Peitz, S. (2025). Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients. Open Journal of Mathematical Optimization, 6, 1–36. https://doi.org/10.5802/ojmo.47
LibreCat | DOI
 
[66]
2025 | Journal Article | LibreCat-ID: 67314
Harder, H., Peitz, S., Nüske, F., Philipp, F., Schaller, M., & Worthmann, K. (2025). Group Convolutional Extended Dynamic Mode Decomposition. Physica D: Nonlinear Phenomena, 480, 134725. https://doi.org/10.1016/j.physd.2025.134725
LibreCat | DOI
 
[65]
2025 | Journal Article | LibreCat-ID: 67316
Peitz, S., & Hotegni, S. S. (2025). Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art. Machine Learning with Applications, 100700. https://doi.org/10.1016/j.mlwa.2025.100700
LibreCat | DOI
 
[64]
2025 | Preprint | LibreCat-ID: 67315
Straat, M., Markmann, T., Peitz, S., & Hammer, B. (2025). Improving the Robustness of Control of Chaotic Convective Flows with Domain-Informed Reinforcement Learning. In arXiv:2511.00272.
LibreCat
 
[63]
2025 | Preprint | LibreCat-ID: 67318
Fromme, F., Allen-Blanchette, C., Harder, H., & Peitz, S. (2025). Surrogate Modeling of 3D Rayleigh-Bénard Convection with Equivariant Autoencoders. In arXiv:2505.13569.
LibreCat
 
[62]
2024 | Journal Article | LibreCat-ID: 46019 | OA
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
LibreCat | DOI | Download (ext.)
 
[61]
2024 | Journal Article | LibreCat-ID: 40171 | OA
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
LibreCat | DOI | Download (ext.)
 
[60]
2024 | Journal Article | LibreCat-ID: 33461 | OA
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
LibreCat | DOI | Download (ext.) | arXiv
 
[59]
2024 | Journal Article | LibreCat-ID: 38031 | OA
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
LibreCat | DOI | Download (ext.) | arXiv
 
[58]
2024 | Journal Article | LibreCat-ID: 32447
Sonntag, K., & Peitz, S. (2024). Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping. SIAM Journal on Optimization, 34(3), 2259–2286. https://doi.org/10.1137/23M1588512
LibreCat | DOI
 
[57]
2024 | Conference Paper | LibreCat-ID: 46649 | OA
Hotegni, S. S., Berkemeier, M. B., & Peitz, S. (2024). Multi-Objective Optimization for Sparse Deep Multi-Task Learning. 2024 International Joint Conference on Neural Networks (IJCNN), 9. https://doi.org/10.1109/IJCNN60899.2024.10650994
LibreCat | DOI | Download (ext.)
 
[56]
2024 | Journal Article | LibreCat-ID: 51334 | OA
Sonntag, K., Gebken, B., Müller, G., Peitz, S., & Volkwein, S. (2024). A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces. Journal of Optimization Theory and Applications, 203, 455–487. https://doi.org/10.1007/s10957-024-02520-4
LibreCat | DOI | Download (ext.) | arXiv
 
[55]
2024 | Conference Paper | LibreCat-ID: 62913
Hunstig, A., Peitz, S., Rose, H., & Meier, T. (2024). Accelerating the analysis of optical quantum systems using the Koopman operator. IEEE 63rd Conference on Decision and Control (CDC). https://doi.org/10.1109/cdc56724.2024.10886589
LibreCat | DOI
 
[54]
2024 | Conference Paper | LibreCat-ID: 52758
Harder, H., & Peitz, S. (2024). On the continuity and smoothness of the value function in reinforcement learning and optimal control. IEEE 63rd Conference on Decision and Control (CDC), 1935–1940. https://doi.org/10.1109/CDC56724.2024.10886529
LibreCat | DOI
 
[53]
2024 | Book Chapter | LibreCat-ID: 46578 | OA
Bernreuther, M., Dellnitz, M., Gebken, B., Müller, G., Peitz, S., Sonntag, K., & Volkwein, S. (2024). Multiobjective Optimization of Non-Smooth PDE-Constrained Problems. In DFG Priority Programme 1962. Birkhäuser.
LibreCat | Download (ext.) | arXiv
 
[52]
2024 | Conference Paper | LibreCat-ID: 42160 | OA
Werner, S., & Peitz, S. (2024). Numerical evidence for sample efficiency of model-based over model-free reinforcement learning control of partial differential equations. European Control Conference (ECC), 2958–2964. https://doi.org/10.23919/ECC64448.2024.10590945
LibreCat | DOI | Download (ext.) | arXiv
 
[51]
2024 | Conference Paper | LibreCat-ID: 48502 | OA
Peitz, S., Hunstig, A., Rose, H., & Meier, T. (2024). Accelerating the analysis of optical quantum systems using the Koopman operator. IEEE 63rd Conference on Decision and Control (CDC), 7964–7969. https://doi.org/10.1109/CDC56724.2024.10886589
LibreCat | DOI | Download (ext.)
 
[50]
2024 | Preprint | LibreCat-ID: 67320
Berkemeier, M., Sonntag, K., & Peitz, S. (2024). New Nonlinear Conjugate Gradient Methods with Guaranteed Descent for Multi-Objective Optimization. In Optimizaton Online.
LibreCat
 
[49]
2023 | Journal Article | LibreCat-ID: 21199 | OA
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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[48]
2023 | Conference Paper | LibreCat-ID: 54838
Boshoff, S., Stenner, J., Weber, D., Meyer, M., Chidananda, V., Peitz, S., & Wallscheid, O. (2023). Hybrid control of interconnected power converters using both expert-driven droop and data-driven reinforcement learning approaches. IEEE Power and Energy Student Summit (PESS), 124–129.
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[47]
2023 | Conference Paper | LibreCat-ID: 54839
Meyer, M., Weber, D., Chidananda, V., Schweins, O., Stenner, J., Boshoff, S., Peitz, S., & Wallscheid, O. (2023). ElectricGrid.jl – Automated modeling of decentralized electrical energy grids. IEEE Power and Energy Student Summit (PESS), 112–117.
LibreCat | Download (ext.)
 
[46]
2023 | Journal Article | LibreCat-ID: 27426 | OA
Gebken, B., Bieker, K., & Peitz, S. (2023). On the structure of regularization paths for piecewise differentiable regularization terms. Journal of Global Optimization, 85(3), 709–741. https://doi.org/10.1007/s10898-022-01223-2
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[45]
2023 | Conference Paper | LibreCat-ID: 30125 | OA
Schaller, M., Worthmann, K., Philipp, F., Peitz, S., & Nüske, F. (2023). Towards reliable data-based optimal and predictive control using extended DMD. IFAC-PapersOnLine, 56(1), 169–174. https://doi.org/10.1016/j.ifacol.2023.02.029
LibreCat | DOI | Download (ext.) | arXiv
 
[44]
2023 | Journal Article | LibreCat-ID: 23428 | OA
Nüske, F., Peitz, S., Philipp, F., Schaller, M., & Worthmann, K. (2023). Finite-data error bounds for Koopman-based prediction and control. Journal of Nonlinear Science, 33, Article 14. https://doi.org/10.1007/s00332-022-09862-1
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[43]
2023 | Journal Article | LibreCat-ID: 21600
Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz, S., & Pfannschmidt, K. (2023). Efficient time stepping for numerical integration using reinforcement  learning. SIAM Journal on Scientific Computing, 45(2), A579–A595. https://doi.org/10.1137/21M1412682
LibreCat | Files available | DOI | Download (ext.) | arXiv
 
[42]
2023 | Journal Article | LibreCat-ID: 46784 | OA
Wallscheid, O., Peitz, S., Stenner, J., Weber, D., Boshoff, S., Meyer, M., Chidananda, V., & Schweins, O. (2023). ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids. Journal of Open Source Software, 8(89), Article 5616. https://doi.org/10.21105/joss.05616
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[41]
2023 | Conference Paper | LibreCat-ID: 46813 | OA
Wohlleben, M. C., Muth, L., Peitz, S., & Sextro, W. (2023). Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits. Proceedings in Applied Mathematics and Mechanics. https://doi.org/10.1002/pamm.202300039
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[40]
2022 | Book Chapter | LibreCat-ID: 16296 | OA
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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[39]
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
LibreCat | DOI
 
[38]
2022 | Journal Article | LibreCat-ID: 29673 | OA
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
LibreCat | DOI | Download (ext.) | arXiv
 
[37]
2022 | Preprint | LibreCat-ID: 33150 | OA
Berkemeier, M. B., & Peitz, S. (2022). Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients. In arXiv:2208.12094.
LibreCat | Download (ext.) | arXiv
 
[36]
2022 | Journal Article | LibreCat-ID: 20731 | OA
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
LibreCat | Files available | DOI | Download (ext.)
 
[35]
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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[34]
2021 | Journal Article | LibreCat-ID: 21337 | OA
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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[33]
2021 | Journal Article | LibreCat-ID: 16867 | OA
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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[32]
2021 | Journal Article | LibreCat-ID: 16295 | OA
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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[31]
2021 | Journal Article | LibreCat-ID: 16294 | OA
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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[30]
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
LibreCat | DOI
 
[29]
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
LibreCat | DOI
 
[28]
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
LibreCat | DOI
 
[27]
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
LibreCat | DOI
 
[26]
2020 | Journal Article | LibreCat-ID: 16290 | OA
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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[25]
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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[24]
2020 | Journal Article | LibreCat-ID: 16297
Hernández Castellanos, C. I., Ober-Blöbaum, S., & Peitz, S. (2020). Explicit Multi-objective Model Predictive Control for Nonlinear Systems  Under Uncertainty. International Journal of Robust and Nonlinear Control, 30(17), 7593–7618. https://doi.org/10.1002/rnc.5197
LibreCat | DOI
 
[23]
2019 | Journal Article | LibreCat-ID: 10593
Peitz, S., & Klus, S. (2019). Koopman operator-based model reduction for switched-system control of PDEs. Automatica, 106, 184–191. https://doi.org/10.1016/j.automatica.2019.05.016
LibreCat | DOI
 
[22]
2019 | Journal Article | LibreCat-ID: 10595
Gebken, B., Peitz, S., & Dellnitz, M. (2019). On the hierarchical structure of Pareto critical sets. Journal of Global Optimization, 73(4), 891–913. https://doi.org/10.1007/s10898-019-00737-6
LibreCat | DOI
 
[21]
2019 | Conference Paper | LibreCat-ID: 10597
Hanke, S., Peitz, S., Wallscheid, O., Böcker, J., & Dellnitz, M. (2019). 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). https://doi.org/10.1109/precede.2019.8753313
LibreCat | DOI
 
[20]
2019 | Conference Paper | LibreCat-ID: 29636
Hanke, S., Peitz, S., Wallscheid, O., Böcker, J., & Dellnitz, M. (2019). Finite-control-set model predictive control for a permanent magnet synchronous motor application with online least squares system identification. 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 1–6.
LibreCat
 
[19]
2018 | Preprint | LibreCat-ID: 21634 | OA
Hanke, S., Peitz, S., Wallscheid, O., Klus, S., Böcker, J., & Dellnitz, M. (2018). Koopman Operator-Based Finite-Control-Set Model Predictive Control for  Electrical Drives. ArXiv:1804.00854.
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[18]
2018 | Book Chapter | LibreCat-ID: 22796
Kummert, F., Albers, A., Bremer, C., Büttner, S., Dellnitz, M., Dumitrescu, R., … Wittrowski, J. (2018). Eingesetzte wissenschaftliche Methoden. In A. Trächtler (Ed.), Ressourceneffiziente Selbstoptimierende Wäscherei – Ergebnisse des ReSerW-Projekts. Paderborn: Springer.
LibreCat
 
[17]
2018 | Conference Paper | LibreCat-ID: 8750
Gebken, B., Peitz, S., & Dellnitz, M. (2018). A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems. In Numerical and Evolutionary Optimization – NEO 2017. Cham. https://doi.org/10.1007/978-3-319-96104-0_2
LibreCat | DOI
 
[16]
2018 | Journal Article | LibreCat-ID: 8751 | OA
Peitz, S., & Dellnitz, M. (2018). A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction. Mathematical and Computational Applications, 23(2). https://doi.org/10.3390/mca23020030
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[15]
2018 | Book Chapter | LibreCat-ID: 8754
Beermann, D., Dellnitz, M., Peitz, S., & Volkwein, S. (2018). Set-Oriented Multiobjective Optimal Control of PDEs Using Proper Orthogonal Decomposition. In Reduced-Order Modeling (ROM) for Simulation and Optimization (pp. 47–72). Cham. https://doi.org/10.1007/978-3-319-75319-5_3
LibreCat | DOI
 
[14]
2018 | Journal Article | LibreCat-ID: 8755
Klus, S., Gelß, P., Peitz, S., & Schütte, C. (2018). Tensor-based dynamic mode decomposition. Nonlinearity, 31(7), 3359–3380. https://doi.org/10.1088/1361-6544/aabc8f
LibreCat | DOI
 
[13]
2018 | Conference Paper | LibreCat-ID: 8757
Beermann, D., Dellnitz, M., Peitz, S., & Volkwein, S. (2018). POD-based multiobjective optimal control of PDEs with non-smooth objectives. In PAMM (pp. 51–54). https://doi.org/10.1002/pamm.201710015
LibreCat | DOI
 
[12]
2018 | Preprint | LibreCat-ID: 16292 | OA
Peitz, S. (2018). Controlling nonlinear PDEs using low-dimensional bilinear approximations  obtained from data. ArXiv:1801.06419.
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[11]
2018 | Preprint | LibreCat-ID: 16293 | OA
Klus, S., Peitz, S., & Schuster, I. (2018). Analyzing high-dimensional time-series data using kernel transfer  operator eigenfunctions. ArXiv:1805.10118.
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[10]
2018 | Journal Article | LibreCat-ID: 29624
Hanke, S., Peitz, S., Wallscheid, O., Klus, S., Böcker, J., & Dellnitz, M. (2018). Koopman Operator-Based Finite-Control-Set Model Predictive Control for Electrical Drives. ArXiv Preprint ArXiv:1804.00854.
LibreCat
 
[9]
2018 | Journal Article | LibreCat-ID: 8753
Peitz, S., Ober-Blöbaum, S., & Dellnitz, M. (2018). Multiobjective Optimal Control Methods for the Navier-Stokes Equations Using Reduced Order Modeling. Acta Applicandae Mathematicae, 161(1), 171–199. https://doi.org/10.1007/s10440-018-0209-7
LibreCat | DOI
 
[8]
2017 | Conference Paper | LibreCat-ID: 5914
Dellnitz, M., Eckstein, J., Flaßkamp, K., Friedel, P., Horenkamp, C., Köhler, U., … Tiemeyer, S. (2017). Multiobjective Optimal Control Methods for the Development of an Intelligent Cruise Control. In Progress in Industrial Mathematics at ECMI 2014 (pp. 633–641). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-23413-7_87
LibreCat | DOI
 
[7]
2017 | Conference Paper | LibreCat-ID: 8752
Peitz, S., & Dellnitz, M. (2017). Gradient-Based Multiobjective Optimization with Uncertainties. In NEO 2016 (pp. 159–182). Cham. https://doi.org/10.1007/978-3-319-64063-1_7
LibreCat | DOI
 
[6]
2017 | Dissertation | LibreCat-ID: 10594 | OA
Peitz, S. (2017).   Exploiting structure in multiobjective optimization and optimal control. https://doi.org/10.17619/UNIPB/1-176
LibreCat | Files available | DOI | Download (ext.)
 
[5]
2017 | Journal Article | LibreCat-ID: 8756
Peitz, S., Schäfer, K., Ober-Blöbaum, S., Eckstein, J., Köhler, U., & Dellnitz, M. (2017). A multiobjective MPC approach for autonomously driven electric vehicles. Proceedings of the 20th World Congress of the International Federation of Automatic Control (IFAC), 50(1), 8674–8679. https://doi.org/10.1016/j.ifacol.2017.08.1526
LibreCat | DOI
 
[4]
2016 | Conference Paper | LibreCat-ID: 8759
Peitz, S., Gräler, M., Henke, C., Molo, M. H., Dellnitz, M., & Trächtler, A. (2016). Multiobjective Model Predictive Control of an Industrial Laundry. In Procedia Technology (pp. 483–490). https://doi.org/10.1016/j.protcy.2016.08.061
LibreCat | DOI
 
[3]
2016 | Conference Paper | LibreCat-ID: 8758
Eckstein, J., Peitz, S., Schäfer, K., Friedel, P., Köhler, U., Hessel von Molo, M., Ober-Blöbaum, S., & Dellnitz, M. (2016). A comparison of two predictive approaches to control the longitudinal dynamics of electric vehicles. Procedia Technology, 3rd International Conference on System-Integrated Intelligence: New Challenges for Product and Production Engineering, 26, 465–472. https://doi.org/10.1016/j.protcy.2016.08.059
LibreCat | DOI
 
[2]
2016 | Conference Paper | LibreCat-ID: 29433
Peitz, S., Ober-Blöbaum, S., & Dellnitz, M. (2016). Reduced order model based multiobjective optimal control of fluids. Proceedings of International Congress of Theoretical and Applied Mechanics.
LibreCat
 
[1]
2015 | Conference Paper | LibreCat-ID: 8760
Peitz, S., & Dellnitz, M. (2015). Multiobjective Optimization of the Flow Around a Cylinder Using Model Order Reduction. In PAMM (pp. 613–614). https://doi.org/10.1002/pamm.201510296
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[90]
2026 | Journal Article | LibreCat-ID: 51160 | OA
Philipp, F. M., Schaller, M., Boshoff, S., Peitz, S., Nüske, F., & Worthmann, K. (2026). Variance representations and convergence rates for data-driven approximations of Koopman operators. Physica D: Nonlinear Phenomena, 492, Article 135223. https://doi.org/10.1016/j.physd.2026.135223
LibreCat | Files available | DOI | Download (ext.) | arXiv
 
[89]
2026 | Journal Article | LibreCat-ID: 53858 | OA
Akhter, J., Fährmann, P. D., Sonntag, K., & Peitz, S. (2026). Common pitfalls to avoid while using multiobjective optimization in machine learning. SIAM Review, 68(3), 656–683. https://doi.org/10.1137/24M1658875
LibreCat | DOI | Download (ext.)
 
[88]
2026 | Conference Paper | LibreCat-ID: 67308
Amakor, A. C., Berkemeier, M. B., Wohlleben, M. C., Sextro, W., & Peitz, S. (2026). Surrogate-Assisted Multi-objective Design of Complex Multibody Systems. In W. Senn, M. Sanguineti, A. Saudargiene, I. V. Tetko, A. E. P. Villa, V. Jirsa, & Y. Bengio (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2025 (pp. 251–262). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-04555-3_21
LibreCat | DOI
 
[87]
2026 | Conference Paper | LibreCat-ID: 67307 | OA
Hotegni, S. S., & Peitz, S. (2026). SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion. 14th International Conference on Learning Representations (ICLR). https://doi.org/10.48550/arXiv.2509.21058
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[86]
2026 | Preprint | LibreCat-ID: 67306 | OA
Stenner, J., Harder, H., & Peitz, S. (2026). Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection. In arXiv:2606.30238.
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[85]
2026 | Preprint | LibreCat-ID: 67309
Stenner, J., Kilian, A., Peitz, S., & de Meer, H. (2026). Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning. In arXiv:2606.30316.
LibreCat
 
[84]
2026 | Conference Paper | LibreCat-ID: 67300
Hotegni, S. S., & Peitz, S. (2026). Enhancing Adversarial Robustness Through Multi-objective Representation Learning. In W. Senn, M. Sanguineti, A. Saudargiene, I. V. Tetko, A. E. P. Villa, V. Jirsa, & Y. Bengio (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2025 (pp. 442–454). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-04558-4_35
LibreCat | DOI
 
[83]
2026 | Journal Article | LibreCat-ID: 67302
Markmann, T., Straat, M., Peitz, S., & Hammer, B. (2026). Fourier neural operators as data-driven surrogates for two- and three-dimensional Rayleigh–Bénard convection. Neurocomputing, 679, 133201. https://doi.org/10.1016/j.neucom.2026.133201
LibreCat | DOI
 
[82]
2026 | Conference Paper | LibreCat-ID: 67299 | OA
Harder, H., Vishwasrao, A., Guastoni, L., Vinuesa, R., & Peitz, S. (2026). Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems. In G. Sukhatme, L. Lindemann, S. Tu, A. Wierman, & N. Atanasov (Eds.), Proceedings of The 8th Annual Learning for Dynamics and Control Conference (Vol. 331, pp. 1601–1619). PMLR. https://doi.org/10.48550/arXiv.2511.04641
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[81]
2026 | Conference Paper | LibreCat-ID: 67303 | OA
Amakor, A. C., Sonntag, K., & Peitz, S. (2026). Interactive Pareto navigation for deep multi-task learning. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 652–668. https://doi.org/10.1007/978-3-032-37667-1_37
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[80]
2026 | Conference Paper | LibreCat-ID: 67305 | OA
Becktepe, J., Franz, A., Thuerey, N., & Peitz, S. (2026). Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control. International Conference on Machine Learning (ICML). https://doi.org/10.48550/arXiv.2601.15015
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[79]
2026 | Journal Article | LibreCat-ID: 67301
Wohlleben, M. C., Schütte, J., Berkemeier, M., Sextro, W., & Peitz, S. (2026). Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings. Multibody System Dynamics. https://doi.org/10.1007/s11044-026-10146-9
LibreCat | DOI
 
[78]
2026 | Preprint | LibreCat-ID: 67298 | OA
Mugisho Zagabe, C., & Peitz, S. (2026). Automatic feature identification in least-squares policy iteration using the Koopman operator framework. In arXiv:2603.26464.
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[77]
2026 | Preprint | LibreCat-ID: 67304 | OA
Plotzki, T., & Peitz, S. (2026). Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection. In arXiv:2603.28074.
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[76]
2025 | Journal Article | LibreCat-ID: 51159
Amakor, A. C., Sonntag, K., & Peitz, S. (2025). A multiobjective continuation method to compute the regularization path of deep neural networks. Machine Learning with Applications, 19, Article 100625. https://doi.org/10.1016/j.mlwa.2025.100625
LibreCat | Files available | DOI
 
[75]
2025 | Journal Article | LibreCat-ID: 46579 | OA
Peitz, S., Harder, H., Nüske, F., Philipp, F., Schaller, M., & Worthmann, K. (2025). Equivariance and partial observations in Koopman operator theory for partial differential equations. Journal of Computational Dynamics, 12, 305–324. https://doi.org/10.3934/jcd.2024035
LibreCat | DOI | Download (ext.) | arXiv
 
[74]
2025 | Conference Paper | LibreCat-ID: 53793 | OA
Harder, H., & Peitz, S. (2025). Solving partial differential equations with equivariant extreme learning machines. In J. Berberich, A. Ianelli, & F. Allgöwer (Eds.), Systems theory in data and optimization (pp. 153–168). Springer. https://doi.org/10.1007/978-3-031-83191-1_10
LibreCat | DOI | Download (ext.)
 
[73]
2025 | Conference Paper | LibreCat-ID: 67313
Voth, D., Dane, L., Grebe, J., Peitz, S., & Terhörst, P. (2025). Effective Backdoor Learning on Open-Set Face Recognition Systems. 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 1027–1039. https://doi.org/10.1109/WACV61041.2025.00109
LibreCat | DOI
 
[72]
2025 | Preprint | LibreCat-ID: 67312
Markmann, T., Straat, M., Peitz, S., & Hammer, B. (2025). Control of Rayleigh-Bénard Convection: Effectiveness of Reinforcement Learning in the Turbulent Regime. In arXiv:2504.12000.
LibreCat
 
[71]
2025 | Preprint | LibreCat-ID: 67310
Boshoff, S., Peitz, S., & Klus, S. (2025). Bayesian Transfer Operators in Reproducing Kernel Hilbert Spaces. In arXiv:2509.22482.
LibreCat
 
[70]
2025 | Journal Article | LibreCat-ID: 57829
de Payrebrune, K. M., Flaßkamp, K., Ströhla, T., Sattel, T., Bestle, D., Röder, B., Eberhard, P., Peitz, S., Stoffel, M., Rutwik, G., Aditya, B., Wohlleben, M. C., Sextro, W., Raff, M., Remy, C. D., Yadav, M., Stender, M., van Delden, J., Lüddecke, T., … Blech, C. (2025). The impact of AI on engineering design procedures for dynamical systems. Technische Mechanik - European Journal of Engineering Mechanics, 45(1), 1–23. https://doi.org/10.24352/UB.OVGU-2025-037
LibreCat | DOI
 
[69]
2025 | Journal Article | LibreCat-ID: 51158 | OA
Philipp, F., Schaller, M., Worthmann, K., Peitz, S., & Nüske, F. (2025). Error analysis of kernel EDMD for prediction and control in the Koopman  framework. Ournal of Nonlinear Science, 35, Article 92. https://doi.org/10.1007/s00332-025-10182-3
LibreCat | DOI | Download (ext.) | arXiv
 
[68]
2025 | Journal Article | LibreCat-ID: 67319
de Payrebrune, K. M., Flaßkamp, K., Ströhla, T., Sattel, T., Bestle, D., Röder, B., Eberhard, P., Peitz, S., Stoffel, M., Rutwik, G., Aditya, B., Wohlleben, M., Sextro, W., Raff, M., Remy, C. D., Yadav, M., Stender, M., van Delden, J., Lüddecke, T., … Blech, C. (2025). The impact of AI on engineering design procedures for dynamical systems. Technische Mechanik - European Journal of Engineering Mechanics, 45(1), 1–23. https://doi.org/10.24352/UB.OVGU-2025-037
LibreCat | DOI
 
[67]
2025 | Journal Article | LibreCat-ID: 67317
Berkemeier, M., & Peitz, S. (2025). Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients. Open Journal of Mathematical Optimization, 6, 1–36. https://doi.org/10.5802/ojmo.47
LibreCat | DOI
 
[66]
2025 | Journal Article | LibreCat-ID: 67314
Harder, H., Peitz, S., Nüske, F., Philipp, F., Schaller, M., & Worthmann, K. (2025). Group Convolutional Extended Dynamic Mode Decomposition. Physica D: Nonlinear Phenomena, 480, 134725. https://doi.org/10.1016/j.physd.2025.134725
LibreCat | DOI
 
[65]
2025 | Journal Article | LibreCat-ID: 67316
Peitz, S., & Hotegni, S. S. (2025). Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art. Machine Learning with Applications, 100700. https://doi.org/10.1016/j.mlwa.2025.100700
LibreCat | DOI
 
[64]
2025 | Preprint | LibreCat-ID: 67315
Straat, M., Markmann, T., Peitz, S., & Hammer, B. (2025). Improving the Robustness of Control of Chaotic Convective Flows with Domain-Informed Reinforcement Learning. In arXiv:2511.00272.
LibreCat
 
[63]
2025 | Preprint | LibreCat-ID: 67318
Fromme, F., Allen-Blanchette, C., Harder, H., & Peitz, S. (2025). Surrogate Modeling of 3D Rayleigh-Bénard Convection with Equivariant Autoencoders. In arXiv:2505.13569.
LibreCat
 
[62]
2024 | Journal Article | LibreCat-ID: 46019 | OA
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
LibreCat | DOI | Download (ext.)
 
[61]
2024 | Journal Article | LibreCat-ID: 40171 | OA
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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[60]
2024 | Journal Article | LibreCat-ID: 33461 | OA
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
LibreCat | DOI | Download (ext.) | arXiv
 
[59]
2024 | Journal Article | LibreCat-ID: 38031 | OA
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
LibreCat | DOI | Download (ext.) | arXiv
 
[58]
2024 | Journal Article | LibreCat-ID: 32447
Sonntag, K., & Peitz, S. (2024). Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping. SIAM Journal on Optimization, 34(3), 2259–2286. https://doi.org/10.1137/23M1588512
LibreCat | DOI
 
[57]
2024 | Conference Paper | LibreCat-ID: 46649 | OA
Hotegni, S. S., Berkemeier, M. B., & Peitz, S. (2024). Multi-Objective Optimization for Sparse Deep Multi-Task Learning. 2024 International Joint Conference on Neural Networks (IJCNN), 9. https://doi.org/10.1109/IJCNN60899.2024.10650994
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[56]
2024 | Journal Article | LibreCat-ID: 51334 | OA
Sonntag, K., Gebken, B., Müller, G., Peitz, S., & Volkwein, S. (2024). A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces. Journal of Optimization Theory and Applications, 203, 455–487. https://doi.org/10.1007/s10957-024-02520-4
LibreCat | DOI | Download (ext.) | arXiv
 
[55]
2024 | Conference Paper | LibreCat-ID: 62913
Hunstig, A., Peitz, S., Rose, H., & Meier, T. (2024). Accelerating the analysis of optical quantum systems using the Koopman operator. IEEE 63rd Conference on Decision and Control (CDC). https://doi.org/10.1109/cdc56724.2024.10886589
LibreCat | DOI
 
[54]
2024 | Conference Paper | LibreCat-ID: 52758
Harder, H., & Peitz, S. (2024). On the continuity and smoothness of the value function in reinforcement learning and optimal control. IEEE 63rd Conference on Decision and Control (CDC), 1935–1940. https://doi.org/10.1109/CDC56724.2024.10886529
LibreCat | DOI
 
[53]
2024 | Book Chapter | LibreCat-ID: 46578 | OA
Bernreuther, M., Dellnitz, M., Gebken, B., Müller, G., Peitz, S., Sonntag, K., & Volkwein, S. (2024). Multiobjective Optimization of Non-Smooth PDE-Constrained Problems. In DFG Priority Programme 1962. Birkhäuser.
LibreCat | Download (ext.) | arXiv
 
[52]
2024 | Conference Paper | LibreCat-ID: 42160 | OA
Werner, S., & Peitz, S. (2024). Numerical evidence for sample efficiency of model-based over model-free reinforcement learning control of partial differential equations. European Control Conference (ECC), 2958–2964. https://doi.org/10.23919/ECC64448.2024.10590945
LibreCat | DOI | Download (ext.) | arXiv
 
[51]
2024 | Conference Paper | LibreCat-ID: 48502 | OA
Peitz, S., Hunstig, A., Rose, H., & Meier, T. (2024). Accelerating the analysis of optical quantum systems using the Koopman operator. IEEE 63rd Conference on Decision and Control (CDC), 7964–7969. https://doi.org/10.1109/CDC56724.2024.10886589
LibreCat | DOI | Download (ext.)
 
[50]
2024 | Preprint | LibreCat-ID: 67320
Berkemeier, M., Sonntag, K., & Peitz, S. (2024). New Nonlinear Conjugate Gradient Methods with Guaranteed Descent for Multi-Objective Optimization. In Optimizaton Online.
LibreCat
 
[49]
2023 | Journal Article | LibreCat-ID: 21199 | OA
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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[48]
2023 | Conference Paper | LibreCat-ID: 54838
Boshoff, S., Stenner, J., Weber, D., Meyer, M., Chidananda, V., Peitz, S., & Wallscheid, O. (2023). Hybrid control of interconnected power converters using both expert-driven droop and data-driven reinforcement learning approaches. IEEE Power and Energy Student Summit (PESS), 124–129.
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[47]
2023 | Conference Paper | LibreCat-ID: 54839
Meyer, M., Weber, D., Chidananda, V., Schweins, O., Stenner, J., Boshoff, S., Peitz, S., & Wallscheid, O. (2023). ElectricGrid.jl – Automated modeling of decentralized electrical energy grids. IEEE Power and Energy Student Summit (PESS), 112–117.
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[46]
2023 | Journal Article | LibreCat-ID: 27426 | OA
Gebken, B., Bieker, K., & Peitz, S. (2023). On the structure of regularization paths for piecewise differentiable regularization terms. Journal of Global Optimization, 85(3), 709–741. https://doi.org/10.1007/s10898-022-01223-2
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[45]
2023 | Conference Paper | LibreCat-ID: 30125 | OA
Schaller, M., Worthmann, K., Philipp, F., Peitz, S., & Nüske, F. (2023). Towards reliable data-based optimal and predictive control using extended DMD. IFAC-PapersOnLine, 56(1), 169–174. https://doi.org/10.1016/j.ifacol.2023.02.029
LibreCat | DOI | Download (ext.) | arXiv
 
[44]
2023 | Journal Article | LibreCat-ID: 23428 | OA
Nüske, F., Peitz, S., Philipp, F., Schaller, M., & Worthmann, K. (2023). Finite-data error bounds for Koopman-based prediction and control. Journal of Nonlinear Science, 33, Article 14. https://doi.org/10.1007/s00332-022-09862-1
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[43]
2023 | Journal Article | LibreCat-ID: 21600
Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz, S., & Pfannschmidt, K. (2023). Efficient time stepping for numerical integration using reinforcement  learning. SIAM Journal on Scientific Computing, 45(2), A579–A595. https://doi.org/10.1137/21M1412682
LibreCat | Files available | DOI | Download (ext.) | arXiv
 
[42]
2023 | Journal Article | LibreCat-ID: 46784 | OA
Wallscheid, O., Peitz, S., Stenner, J., Weber, D., Boshoff, S., Meyer, M., Chidananda, V., & Schweins, O. (2023). ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids. Journal of Open Source Software, 8(89), Article 5616. https://doi.org/10.21105/joss.05616
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[41]
2023 | Conference Paper | LibreCat-ID: 46813 | OA
Wohlleben, M. C., Muth, L., Peitz, S., & Sextro, W. (2023). Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits. Proceedings in Applied Mathematics and Mechanics. https://doi.org/10.1002/pamm.202300039
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[40]
2022 | Book Chapter | LibreCat-ID: 16296 | OA
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
LibreCat | DOI | Download (ext.)
 
[39]
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
LibreCat | DOI
 
[38]
2022 | Journal Article | LibreCat-ID: 29673 | OA
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
LibreCat | DOI | Download (ext.) | arXiv
 
[37]
2022 | Preprint | LibreCat-ID: 33150 | OA
Berkemeier, M. B., & Peitz, S. (2022). Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients. In arXiv:2208.12094.
LibreCat | Download (ext.) | arXiv
 
[36]
2022 | Journal Article | LibreCat-ID: 20731 | OA
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
LibreCat | Files available | DOI | Download (ext.)
 
[35]
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
LibreCat | DOI | Download (ext.)
 
[34]
2021 | Journal Article | LibreCat-ID: 21337 | OA
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
LibreCat | DOI | Download (ext.)
 
[33]
2021 | Journal Article | LibreCat-ID: 16867 | OA
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
LibreCat | DOI | Download (ext.)
 
[32]
2021 | Journal Article | LibreCat-ID: 16295 | OA
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
LibreCat | DOI | Download (ext.)
 
[31]
2021 | Journal Article | LibreCat-ID: 16294 | OA
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
LibreCat | DOI | Download (ext.)
 
[30]
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
LibreCat | DOI
 
[29]
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
LibreCat | DOI
 
[28]
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
LibreCat | DOI
 
[27]
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
LibreCat | DOI
 
[26]
2020 | Journal Article | LibreCat-ID: 16290 | OA
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
LibreCat | DOI | Download (ext.)
 
[25]
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
LibreCat | DOI | Download (ext.)
 
[24]
2020 | Journal Article | LibreCat-ID: 16297
Hernández Castellanos, C. I., Ober-Blöbaum, S., & Peitz, S. (2020). Explicit Multi-objective Model Predictive Control for Nonlinear Systems  Under Uncertainty. International Journal of Robust and Nonlinear Control, 30(17), 7593–7618. https://doi.org/10.1002/rnc.5197
LibreCat | DOI
 
[23]
2019 | Journal Article | LibreCat-ID: 10593
Peitz, S., & Klus, S. (2019). Koopman operator-based model reduction for switched-system control of PDEs. Automatica, 106, 184–191. https://doi.org/10.1016/j.automatica.2019.05.016
LibreCat | DOI
 
[22]
2019 | Journal Article | LibreCat-ID: 10595
Gebken, B., Peitz, S., & Dellnitz, M. (2019). On the hierarchical structure of Pareto critical sets. Journal of Global Optimization, 73(4), 891–913. https://doi.org/10.1007/s10898-019-00737-6
LibreCat | DOI
 
[21]
2019 | Conference Paper | LibreCat-ID: 10597
Hanke, S., Peitz, S., Wallscheid, O., Böcker, J., & Dellnitz, M. (2019). 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). https://doi.org/10.1109/precede.2019.8753313
LibreCat | DOI
 
[20]
2019 | Conference Paper | LibreCat-ID: 29636
Hanke, S., Peitz, S., Wallscheid, O., Böcker, J., & Dellnitz, M. (2019). Finite-control-set model predictive control for a permanent magnet synchronous motor application with online least squares system identification. 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 1–6.
LibreCat
 
[19]
2018 | Preprint | LibreCat-ID: 21634 | OA
Hanke, S., Peitz, S., Wallscheid, O., Klus, S., Böcker, J., & Dellnitz, M. (2018). Koopman Operator-Based Finite-Control-Set Model Predictive Control for  Electrical Drives. ArXiv:1804.00854.
LibreCat | Download (ext.)
 
[18]
2018 | Book Chapter | LibreCat-ID: 22796
Kummert, F., Albers, A., Bremer, C., Büttner, S., Dellnitz, M., Dumitrescu, R., … Wittrowski, J. (2018). Eingesetzte wissenschaftliche Methoden. In A. Trächtler (Ed.), Ressourceneffiziente Selbstoptimierende Wäscherei – Ergebnisse des ReSerW-Projekts. Paderborn: Springer.
LibreCat
 
[17]
2018 | Conference Paper | LibreCat-ID: 8750
Gebken, B., Peitz, S., & Dellnitz, M. (2018). A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems. In Numerical and Evolutionary Optimization – NEO 2017. Cham. https://doi.org/10.1007/978-3-319-96104-0_2
LibreCat | DOI
 
[16]
2018 | Journal Article | LibreCat-ID: 8751 | OA
Peitz, S., & Dellnitz, M. (2018). A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction. Mathematical and Computational Applications, 23(2). https://doi.org/10.3390/mca23020030
LibreCat | DOI | Download (ext.)
 
[15]
2018 | Book Chapter | LibreCat-ID: 8754
Beermann, D., Dellnitz, M., Peitz, S., & Volkwein, S. (2018). Set-Oriented Multiobjective Optimal Control of PDEs Using Proper Orthogonal Decomposition. In Reduced-Order Modeling (ROM) for Simulation and Optimization (pp. 47–72). Cham. https://doi.org/10.1007/978-3-319-75319-5_3
LibreCat | DOI
 
[14]
2018 | Journal Article | LibreCat-ID: 8755
Klus, S., Gelß, P., Peitz, S., & Schütte, C. (2018). Tensor-based dynamic mode decomposition. Nonlinearity, 31(7), 3359–3380. https://doi.org/10.1088/1361-6544/aabc8f
LibreCat | DOI
 
[13]
2018 | Conference Paper | LibreCat-ID: 8757
Beermann, D., Dellnitz, M., Peitz, S., & Volkwein, S. (2018). POD-based multiobjective optimal control of PDEs with non-smooth objectives. In PAMM (pp. 51–54). https://doi.org/10.1002/pamm.201710015
LibreCat | DOI
 
[12]
2018 | Preprint | LibreCat-ID: 16292 | OA
Peitz, S. (2018). Controlling nonlinear PDEs using low-dimensional bilinear approximations  obtained from data. ArXiv:1801.06419.
LibreCat | Download (ext.)
 
[11]
2018 | Preprint | LibreCat-ID: 16293 | OA
Klus, S., Peitz, S., & Schuster, I. (2018). Analyzing high-dimensional time-series data using kernel transfer  operator eigenfunctions. ArXiv:1805.10118.
LibreCat | Download (ext.)
 
[10]
2018 | Journal Article | LibreCat-ID: 29624
Hanke, S., Peitz, S., Wallscheid, O., Klus, S., Böcker, J., & Dellnitz, M. (2018). Koopman Operator-Based Finite-Control-Set Model Predictive Control for Electrical Drives. ArXiv Preprint ArXiv:1804.00854.
LibreCat
 
[9]
2018 | Journal Article | LibreCat-ID: 8753
Peitz, S., Ober-Blöbaum, S., & Dellnitz, M. (2018). Multiobjective Optimal Control Methods for the Navier-Stokes Equations Using Reduced Order Modeling. Acta Applicandae Mathematicae, 161(1), 171–199. https://doi.org/10.1007/s10440-018-0209-7
LibreCat | DOI
 
[8]
2017 | Conference Paper | LibreCat-ID: 5914
Dellnitz, M., Eckstein, J., Flaßkamp, K., Friedel, P., Horenkamp, C., Köhler, U., … Tiemeyer, S. (2017). Multiobjective Optimal Control Methods for the Development of an Intelligent Cruise Control. In Progress in Industrial Mathematics at ECMI 2014 (pp. 633–641). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-23413-7_87
LibreCat | DOI
 
[7]
2017 | Conference Paper | LibreCat-ID: 8752
Peitz, S., & Dellnitz, M. (2017). Gradient-Based Multiobjective Optimization with Uncertainties. In NEO 2016 (pp. 159–182). Cham. https://doi.org/10.1007/978-3-319-64063-1_7
LibreCat | DOI
 
[6]
2017 | Dissertation | LibreCat-ID: 10594 | OA
Peitz, S. (2017).   Exploiting structure in multiobjective optimization and optimal control. https://doi.org/10.17619/UNIPB/1-176
LibreCat | Files available | DOI | Download (ext.)
 
[5]
2017 | Journal Article | LibreCat-ID: 8756
Peitz, S., Schäfer, K., Ober-Blöbaum, S., Eckstein, J., Köhler, U., & Dellnitz, M. (2017). A multiobjective MPC approach for autonomously driven electric vehicles. Proceedings of the 20th World Congress of the International Federation of Automatic Control (IFAC), 50(1), 8674–8679. https://doi.org/10.1016/j.ifacol.2017.08.1526
LibreCat | DOI
 
[4]
2016 | Conference Paper | LibreCat-ID: 8759
Peitz, S., Gräler, M., Henke, C., Molo, M. H., Dellnitz, M., & Trächtler, A. (2016). Multiobjective Model Predictive Control of an Industrial Laundry. In Procedia Technology (pp. 483–490). https://doi.org/10.1016/j.protcy.2016.08.061
LibreCat | DOI
 
[3]
2016 | Conference Paper | LibreCat-ID: 8758
Eckstein, J., Peitz, S., Schäfer, K., Friedel, P., Köhler, U., Hessel von Molo, M., Ober-Blöbaum, S., & Dellnitz, M. (2016). A comparison of two predictive approaches to control the longitudinal dynamics of electric vehicles. Procedia Technology, 3rd International Conference on System-Integrated Intelligence: New Challenges for Product and Production Engineering, 26, 465–472. https://doi.org/10.1016/j.protcy.2016.08.059
LibreCat | DOI
 
[2]
2016 | Conference Paper | LibreCat-ID: 29433
Peitz, S., Ober-Blöbaum, S., & Dellnitz, M. (2016). Reduced order model based multiobjective optimal control of fluids. Proceedings of International Congress of Theoretical and Applied Mechanics.
LibreCat
 
[1]
2015 | Conference Paper | LibreCat-ID: 8760
Peitz, S., & Dellnitz, M. (2015). Multiobjective Optimization of the Flow Around a Cylinder Using Model Order Reduction. In PAMM (pp. 613–614). https://doi.org/10.1002/pamm.201510296
LibreCat | DOI
 
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