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
F. M. Philipp, M. Schaller, S. Boshoff, S. Peitz, F. Nüske, and K. Worthmann, “Variance representations and convergence rates for data-driven approximations of Koopman operators,” Physica D: Nonlinear Phenomena, vol. 492, Art. no. 135223, 2026, doi: 10.1016/j.physd.2026.135223.
LibreCat | Files available | DOI | Download (ext.) | arXiv
 
[89]
2026 | Journal Article | LibreCat-ID: 53858 | OA
J. Akhter, P. D. Fährmann, K. Sonntag, and S. Peitz, “Common pitfalls to avoid while using multiobjective optimization in machine learning,” SIAM Review, vol. 68, no. 3, pp. 656–683, 2026, doi: 10.1137/24M1658875.
LibreCat | DOI | Download (ext.)
 
[88]
2026 | Conference Paper | LibreCat-ID: 67308
A. C. Amakor, M. B. Berkemeier, M. C. Wohlleben, W. Sextro, and S. Peitz, “Surrogate-Assisted Multi-objective Design of Complex Multibody Systems,” in Artificial Neural Networks and Machine Learning – ICANN 2025, 2026, pp. 251–262, doi: 10.1007/978-3-032-04555-3_21.
LibreCat | DOI
 
[87]
2026 | Conference Paper | LibreCat-ID: 67307 | OA
S. S. Hotegni and S. Peitz, “SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion,” 2026, doi: 10.48550/arXiv.2509.21058.
LibreCat | DOI | Download (ext.)
 
[86]
2026 | Preprint | LibreCat-ID: 67306 | OA
J. Stenner, H. Harder, and S. Peitz, “Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection,” arXiv:2606.30238. 2026.
LibreCat | Download (ext.)
 
[85]
2026 | Preprint | LibreCat-ID: 67309
J. Stenner, A. Kilian, S. Peitz, and H. de Meer, “Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning,” arXiv:2606.30316. 2026.
LibreCat
 
[84]
2026 | Conference Paper | LibreCat-ID: 67300
S. S. Hotegni and S. Peitz, “Enhancing Adversarial Robustness Through Multi-objective Representation Learning,” in Artificial Neural Networks and Machine Learning – ICANN 2025, 2026, pp. 442–454, doi: 10.1007/978-3-032-04558-4_35.
LibreCat | DOI
 
[83]
2026 | Journal Article | LibreCat-ID: 67302
T. Markmann, M. Straat, S. Peitz, and B. Hammer, “Fourier neural operators as data-driven surrogates for two- and three-dimensional Rayleigh–Bénard convection,” Neurocomputing, vol. 679, p. 133201, 2026, doi: 10.1016/j.neucom.2026.133201.
LibreCat | DOI
 
[82]
2026 | Conference Paper | LibreCat-ID: 67299 | OA
H. Harder, A. Vishwasrao, L. Guastoni, R. Vinuesa, and S. Peitz, “Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems,” in Proceedings of The 8th Annual Learning for Dynamics and Control Conference, 2026, vol. 331, pp. 1601–1619, doi: 10.48550/arXiv.2511.04641.
LibreCat | DOI | Download (ext.)
 
[81]
2026 | Conference Paper | LibreCat-ID: 67303 | OA
A. C. Amakor, K. Sonntag, and S. Peitz, “Interactive Pareto navigation for deep multi-task learning,” in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2026, pp. 652–668, doi: 10.1007/978-3-032-37667-1_37.
LibreCat | DOI | Download (ext.)
 
[80]
2026 | Conference Paper | LibreCat-ID: 67305 | OA
J. Becktepe, A. Franz, N. Thuerey, and S. Peitz, “Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control,” 2026, doi: 10.48550/arXiv.2601.15015.
LibreCat | DOI | Download (ext.)
 
[79]
2026 | Journal Article | LibreCat-ID: 67301
M. C. Wohlleben, J. Schütte, M. Berkemeier, W. Sextro, and S. Peitz, “Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings,” Multibody System Dynamics, 2026, doi: 10.1007/s11044-026-10146-9.
LibreCat | DOI
 
[78]
2026 | Preprint | LibreCat-ID: 67298 | OA
C. Mugisho Zagabe and S. Peitz, “Automatic feature identification in least-squares policy iteration using the Koopman operator framework,” arXiv:2603.26464. 2026.
LibreCat | Download (ext.)
 
[77]
2026 | Preprint | LibreCat-ID: 67304 | OA
T. Plotzki and S. Peitz, “Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection,” arXiv:2603.28074. 2026.
LibreCat | Download (ext.)
 
[76]
2025 | Journal Article | LibreCat-ID: 51159
A. C. Amakor, K. Sonntag, and S. Peitz, “A multiobjective continuation method to compute the regularization path of deep neural networks,” Machine Learning with Applications, vol. 19, Art. no. 100625, 2025, doi: 10.1016/j.mlwa.2025.100625.
LibreCat | Files available | DOI
 
[75]
2025 | Journal Article | LibreCat-ID: 46579 | OA
S. Peitz, H. Harder, F. Nüske, F. Philipp, M. Schaller, and K. Worthmann, “Equivariance and partial observations in Koopman operator theory for partial differential equations,” Journal of Computational Dynamics, vol. 12, pp. 305–324, 2025, doi: 10.3934/jcd.2024035.
LibreCat | DOI | Download (ext.) | arXiv
 
[74]
2025 | Conference Paper | LibreCat-ID: 53793 | OA
H. Harder and S. Peitz, “Solving partial differential equations with equivariant extreme learning machines,” in Systems theory in data and optimization, 2025, pp. 153–168, doi: 10.1007/978-3-031-83191-1_10.
LibreCat | DOI | Download (ext.)
 
[73]
2025 | Conference Paper | LibreCat-ID: 67313
D. Voth, L. Dane, J. Grebe, S. Peitz, and P. Terhörst, “Effective Backdoor Learning on Open-Set Face Recognition Systems,” in 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 1027–1039, doi: 10.1109/WACV61041.2025.00109.
LibreCat | DOI
 
[72]
2025 | Preprint | LibreCat-ID: 67312
T. Markmann, M. Straat, S. Peitz, and B. Hammer, “Control of Rayleigh-Bénard Convection: Effectiveness of Reinforcement Learning in the Turbulent Regime,” arXiv:2504.12000. 2025.
LibreCat
 
[71]
2025 | Preprint | LibreCat-ID: 67310
S. Boshoff, S. Peitz, and S. Klus, “Bayesian Transfer Operators in Reproducing Kernel Hilbert Spaces,” arXiv:2509.22482. 2025.
LibreCat
 
[70]
2025 | Journal Article | LibreCat-ID: 57829
K. M. de Payrebrune et al., “The impact of AI on engineering design procedures for dynamical systems,” Technische Mechanik - European Journal of Engineering Mechanics, vol. 45, no. 1, pp. 1–23, 2025, doi: 10.24352/UB.OVGU-2025-037.
LibreCat | DOI
 
[69]
2025 | Journal Article | LibreCat-ID: 51158 | OA
F. Philipp, M. Schaller, K. Worthmann, S. Peitz, and F. Nüske, “Error analysis of kernel EDMD for prediction and control in the Koopman  framework,” ournal of Nonlinear Science, vol. 35, Art. no. 92, 2025, doi: 10.1007/s00332-025-10182-3.
LibreCat | DOI | Download (ext.) | arXiv
 
[68]
2025 | Journal Article | LibreCat-ID: 67319
K. M. de Payrebrune et al., “The impact of AI on engineering design procedures for dynamical systems,” Technische Mechanik - European Journal of Engineering Mechanics, vol. 45, no. 1, pp. 1–23, 2025, doi: 10.24352/UB.OVGU-2025-037.
LibreCat | DOI
 
[67]
2025 | Journal Article | LibreCat-ID: 67317
M. Berkemeier and S. Peitz, “Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients,” Open Journal of Mathematical Optimization, vol. 6, pp. 1–36, 2025, doi: 10.5802/ojmo.47.
LibreCat | DOI
 
[66]
2025 | Journal Article | LibreCat-ID: 67314
H. Harder, S. Peitz, F. Nüske, F. Philipp, M. Schaller, and K. Worthmann, “Group Convolutional Extended Dynamic Mode Decomposition,” Physica D: Nonlinear Phenomena, vol. 480, p. 134725, 2025, doi: 10.1016/j.physd.2025.134725.
LibreCat | DOI
 
[65]
2025 | Journal Article | LibreCat-ID: 67316
S. Peitz and S. S. Hotegni, “Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art,” Machine Learning with Applications, no. 100700, 2025, doi: 10.1016/j.mlwa.2025.100700.
LibreCat | DOI
 
[64]
2025 | Preprint | LibreCat-ID: 67315
M. Straat, T. Markmann, S. Peitz, and B. Hammer, “Improving the Robustness of Control of Chaotic Convective Flows with Domain-Informed Reinforcement Learning,” arXiv:2511.00272. 2025.
LibreCat
 
[63]
2025 | Preprint | LibreCat-ID: 67318
F. Fromme, C. Allen-Blanchette, H. Harder, and S. Peitz, “Surrogate Modeling of 3D Rayleigh-Bénard Convection with Equivariant Autoencoders,” arXiv:2505.13569. 2025.
LibreCat
 
[62]
2024 | Journal Article | LibreCat-ID: 46019 | OA
K. Sonntag and S. Peitz, “Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems,” Journal of Optimization Theory and Applications, 2024, doi: 10.1007/s10957-024-02389-3.
LibreCat | DOI | Download (ext.)
 
[61]
2024 | Journal Article | LibreCat-ID: 40171 | OA
S. Peitz, J. Stenner, V. Chidananda, O. Wallscheid, S. L. Brunton, and K. Taira, “Distributed Control of Partial Differential Equations Using  Convolutional Reinforcement Learning,” Physica D: Nonlinear Phenomena, vol. 461, p. 134096, 2024, doi: 10.1016/j.physd.2024.134096.
LibreCat | DOI | Download (ext.)
 
[60]
2024 | Journal Article | LibreCat-ID: 33461 | OA
S. E. Otto, S. Peitz, and C. W. Rowley, “Learning Bilinear Models of Actuated Koopman Generators from  Partially-Observed Trajectories,” SIAM Journal on Applied Dynamical Systems, vol. 23, no. 1, pp. 885–923, 2024, doi: 10.1137/22M1523601.
LibreCat | DOI | Download (ext.) | arXiv
 
[59]
2024 | Journal Article | LibreCat-ID: 38031 | OA
F. Philipp, M. Schaller, K. Worthmann, S. Peitz, and F. Nüske, “Error bounds for kernel-based approximations of the Koopman operator,” Applied and Computational Harmonic Analysis , vol. 71, Art. no. 101657, 2024, doi: 10.1016/j.acha.2024.101657.
LibreCat | DOI | Download (ext.) | arXiv
 
[58]
2024 | Journal Article | LibreCat-ID: 32447
K. Sonntag and S. Peitz, “Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping,” SIAM Journal on Optimization, vol. 34, no. 3, pp. 2259–2286, 2024, doi: 10.1137/23M1588512.
LibreCat | DOI
 
[57]
2024 | Conference Paper | LibreCat-ID: 46649 | OA
S. S. Hotegni, M. B. Berkemeier, and S. Peitz, “Multi-Objective Optimization for Sparse Deep Multi-Task Learning,” in 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 2024, p. 9, doi: 10.1109/IJCNN60899.2024.10650994.
LibreCat | DOI | Download (ext.)
 
[56]
2024 | Journal Article | LibreCat-ID: 51334 | OA
K. Sonntag, B. Gebken, G. Müller, S. Peitz, and S. Volkwein, “A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces,” Journal of Optimization Theory and Applications, vol. 203, pp. 455–487, 2024, doi: 10.1007/s10957-024-02520-4.
LibreCat | DOI | Download (ext.) | arXiv
 
[55]
2024 | Conference Paper | LibreCat-ID: 62913
A. Hunstig, S. Peitz, H. Rose, and T. Meier, “Accelerating the analysis of optical quantum systems using the Koopman operator,” 2024, doi: 10.1109/cdc56724.2024.10886589.
LibreCat | DOI
 
[54]
2024 | Conference Paper | LibreCat-ID: 52758
H. Harder and S. Peitz, “On the continuity and smoothness of the value function in reinforcement learning and optimal control,” in IEEE 63rd conference on decision and control (CDC), 2024, pp. 1935–1940, doi: 10.1109/CDC56724.2024.10886529.
LibreCat | DOI
 
[53]
2024 | Book Chapter | LibreCat-ID: 46578 | OA
M. Bernreuther et al., “Multiobjective Optimization of Non-Smooth PDE-Constrained Problems,” in DFG Priority Programme 1962, Birkhäuser, 2024.
LibreCat | Download (ext.) | arXiv
 
[52]
2024 | Conference Paper | LibreCat-ID: 42160 | OA
S. Werner and S. Peitz, “Numerical evidence for sample efficiency of model-based over model-free reinforcement learning control of partial differential equations,” in European Control Conference (ECC), 2024, pp. 2958–2964, doi: 10.23919/ECC64448.2024.10590945.
LibreCat | DOI | Download (ext.) | arXiv
 
[51]
2024 | Conference Paper | LibreCat-ID: 48502 | OA
S. Peitz, A. Hunstig, H. Rose, and T. Meier, “Accelerating the analysis of optical quantum systems using the Koopman operator,” in IEEE 63rd Conference on Decision and Control (CDC), 2024, pp. 7964–7969, doi: 10.1109/CDC56724.2024.10886589.
LibreCat | DOI | Download (ext.)
 
[50]
2024 | Preprint | LibreCat-ID: 67320
M. Berkemeier, K. Sonntag, and S. Peitz, “New Nonlinear Conjugate Gradient Methods with Guaranteed Descent for Multi-Objective Optimization,” Optimizaton Online. 2024.
LibreCat
 
[49]
2023 | Journal Article | LibreCat-ID: 21199 | OA
S. Peitz and K. Bieker, “On the Universal Transformation of Data-Driven Models to Control Systems,” Automatica, vol. 149, Art. no. 110840, 2023, doi: 10.1016/j.automatica.2022.110840.
LibreCat | DOI | Download (ext.)
 
[48]
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.
LibreCat | Download (ext.)
 
[47]
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.
LibreCat | Download (ext.)
 
[46]
2023 | Journal Article | LibreCat-ID: 27426 | OA
B. Gebken, K. Bieker, and S. Peitz, “On the structure of regularization paths for piecewise differentiable regularization terms,” Journal of Global Optimization, vol. 85, no. 3, pp. 709–741, 2023, doi: 10.1007/s10898-022-01223-2.
LibreCat | DOI | Download (ext.)
 
[45]
2023 | Conference Paper | LibreCat-ID: 30125 | OA
M. Schaller, K. Worthmann, F. Philipp, S. Peitz, and F. Nüske, “Towards reliable data-based optimal and predictive control using extended DMD,” in IFAC-PapersOnLine, 2023, vol. 56, no. 1, pp. 169–174, doi: 10.1016/j.ifacol.2023.02.029.
LibreCat | DOI | Download (ext.) | arXiv
 
[44]
2023 | Journal Article | LibreCat-ID: 23428 | OA
F. Nüske, S. Peitz, F. Philipp, M. Schaller, and K. Worthmann, “Finite-data error bounds for Koopman-based prediction and control,” Journal of Nonlinear Science, vol. 33, Art. no. 14, 2023, doi: 10.1007/s00332-022-09862-1.
LibreCat | DOI | Download (ext.)
 
[43]
2023 | Journal Article | LibreCat-ID: 21600
M. Dellnitz et al., “Efficient time stepping for numerical integration using reinforcement  learning,” SIAM Journal on Scientific Computing, vol. 45, no. 2, pp. A579–A595, 2023, doi: 10.1137/21M1412682.
LibreCat | Files available | DOI | Download (ext.) | arXiv
 
[42]
2023 | Journal Article | LibreCat-ID: 46784 | OA
O. Wallscheid et al., “ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids,” Journal of Open Source Software, vol. 8, no. 89, Art. no. 5616, 2023, doi: 10.21105/joss.05616.
LibreCat | DOI | Download (ext.)
 
[41]
2023 | Conference Paper | LibreCat-ID: 46813 | OA
M. C. Wohlleben, L. Muth, S. Peitz, and W. Sextro, “Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits,” 2023, doi: 10.1002/pamm.202300039.
LibreCat | DOI | Download (ext.)
 
[40]
2022 | Book Chapter | LibreCat-ID: 16296 | OA
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.
LibreCat | DOI | Download (ext.)
 
[39]
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.
LibreCat | DOI
 
[38]
2022 | Journal Article | LibreCat-ID: 29673 | OA
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.
LibreCat | DOI | Download (ext.) | arXiv
 
[37]
2022 | Preprint | LibreCat-ID: 33150 | OA
M. B. Berkemeier and S. Peitz, “Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients,” arXiv:2208.12094. 2022.
LibreCat | Download (ext.) | arXiv
 
[36]
2022 | Journal Article | LibreCat-ID: 20731 | OA
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.
LibreCat | Files available | DOI | Download (ext.)
 
[35]
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.
LibreCat | DOI | Download (ext.)
 
[34]
2021 | Journal Article | LibreCat-ID: 21337 | OA
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.
LibreCat | DOI | Download (ext.)
 
[33]
2021 | Journal Article | LibreCat-ID: 16867 | OA
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.
LibreCat | DOI | Download (ext.)
 
[32]
2021 | Journal Article | LibreCat-ID: 16295 | OA
B. Gebken and S. Peitz, “Inverse multiobjective optimization: Inferring decision criteria from data,” Journal of Global Optimization, vol. 80, pp. 3–29, 2021.
LibreCat | DOI | Download (ext.)
 
[31]
2021 | Journal Article | LibreCat-ID: 16294 | OA
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.
LibreCat | DOI | Download (ext.)
 
[30]
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.
LibreCat | DOI
 
[29]
2020 | Journal Article | LibreCat-ID: 10596
O. Schütze, O. Cuate, A. Martín, S. Peitz, and M. Dellnitz, “Pareto Explorer: a global/local exploration tool for many-objective optimization problems,” Engineering Optimization, vol. 52, no. 5, pp. 832–855, 2020.
LibreCat | DOI
 
[28]
2020 | Journal Article | LibreCat-ID: 16288
S. Klus, F. Nüske, S. Peitz, J.-H. Niemann, C. Clementi, and C. Schütte, “Data-driven approximation of the Koopman generator: Model reduction, system identification, and control,” Physica D: Nonlinear Phenomena, vol. 406, 2020.
LibreCat | DOI
 
[27]
2020 | Book Chapter | LibreCat-ID: 16289
S. Peitz and S. Klus, “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, Cham: Springer, 2020, pp. 257–282.
LibreCat | DOI
 
[26]
2020 | Journal Article | LibreCat-ID: 16290 | OA
K. Bieker, S. Peitz, S. L. Brunton, J. N. Kutz, and M. Dellnitz, “Deep model predictive flow control with limited sensor data and online learning,” Theoretical and Computational Fluid Dynamics, vol. 34, pp. 577–591, 2020.
LibreCat | DOI | Download (ext.)
 
[25]
2020 | Journal Article | LibreCat-ID: 16309
S. Peitz, S. E. Otto, and C. W. Rowley, “Data-Driven Model Predictive Control using Interpolated Koopman  Generators,” SIAM Journal on Applied Dynamical Systems, vol. 19, no. 3, pp. 2162–2193, 2020.
LibreCat | DOI | Download (ext.)
 
[24]
2020 | Journal Article | LibreCat-ID: 16297
C. I. Hernández Castellanos, S. Ober-Blöbaum, and S. Peitz, “Explicit Multi-objective Model Predictive Control for Nonlinear Systems  Under Uncertainty,” International Journal of Robust and Nonlinear Control, vol. 30(17), pp. 7593–7618, 2020, doi: 10.1002/rnc.5197.
LibreCat | DOI
 
[23]
2019 | Journal Article | LibreCat-ID: 10593
S. Peitz and S. Klus, “Koopman operator-based model reduction for switched-system control of PDEs,” Automatica, vol. 106, pp. 184–191, 2019.
LibreCat | DOI
 
[22]
2019 | Journal Article | LibreCat-ID: 10595
B. Gebken, S. Peitz, and M. Dellnitz, “On the hierarchical structure of Pareto critical sets,” Journal of Global Optimization, vol. 73, no. 4, pp. 891–913, 2019.
LibreCat | DOI
 
[21]
2019 | Conference Paper | LibreCat-ID: 10597
S. Hanke, S. Peitz, O. Wallscheid, J. Böcker, and M. Dellnitz, “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.
LibreCat | DOI
 
[20]
2019 | Conference Paper | LibreCat-ID: 29636
S. Hanke, S. Peitz, O. Wallscheid, J. Böcker, and M. Dellnitz, “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, pp. 1–6.
LibreCat
 
[19]
2018 | Preprint | LibreCat-ID: 21634 | OA
S. Hanke, S. Peitz, O. Wallscheid, S. Klus, J. Böcker, and M. Dellnitz, “Koopman Operator-Based Finite-Control-Set Model Predictive Control for  Electrical Drives,” arXiv:1804.00854. 2018.
LibreCat | Download (ext.)
 
[18]
2018 | Book Chapter | LibreCat-ID: 22796
F. Kummert et al., “Eingesetzte wissenschaftliche Methoden,” in Ressourceneffiziente Selbstoptimierende Wäscherei – Ergebnisse des ReSerW-Projekts, A. Trächtler, Ed. Paderborn: Springer, 2018.
LibreCat
 
[17]
2018 | Conference Paper | LibreCat-ID: 8750
B. Gebken, S. Peitz, and M. Dellnitz, “A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems,” in Numerical and Evolutionary Optimization – NEO 2017, 2018.
LibreCat | DOI
 
[16]
2018 | Journal Article | LibreCat-ID: 8751 | OA
S. Peitz and M. Dellnitz, “A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction,” Mathematical and Computational Applications, vol. 23, no. 2, 2018.
LibreCat | DOI | Download (ext.)
 
[15]
2018 | Book Chapter | LibreCat-ID: 8754
D. Beermann, M. Dellnitz, S. Peitz, and S. Volkwein, “Set-Oriented Multiobjective Optimal Control of PDEs Using Proper Orthogonal Decomposition,” in Reduced-Order Modeling (ROM) for Simulation and Optimization, Cham, 2018, pp. 47–72.
LibreCat | DOI
 
[14]
2018 | Journal Article | LibreCat-ID: 8755
S. Klus, P. Gelß, S. Peitz, and C. Schütte, “Tensor-based dynamic mode decomposition,” Nonlinearity, vol. 31, no. 7, pp. 3359–3380, 2018.
LibreCat | DOI
 
[13]
2018 | Conference Paper | LibreCat-ID: 8757
D. Beermann, M. Dellnitz, S. Peitz, and S. Volkwein, “POD-based multiobjective optimal control of PDEs with non-smooth objectives,” in PAMM, 2018, pp. 51–54.
LibreCat | DOI
 
[12]
2018 | Preprint | LibreCat-ID: 16292 | OA
S. Peitz, “Controlling nonlinear PDEs using low-dimensional bilinear approximations  obtained from data,” arXiv:1801.06419. 2018.
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[11]
2018 | Preprint | LibreCat-ID: 16293 | OA
S. Klus, S. Peitz, and I. Schuster, “Analyzing high-dimensional time-series data using kernel transfer  operator eigenfunctions,” arXiv:1805.10118. 2018.
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[10]
2018 | Journal Article | LibreCat-ID: 29624
S. Hanke, S. Peitz, O. Wallscheid, S. Klus, J. Böcker, and M. Dellnitz, “Koopman Operator-Based Finite-Control-Set Model Predictive Control for Electrical Drives,” arXiv preprint arXiv:1804.00854, 2018.
LibreCat
 
[9]
2018 | Journal Article | LibreCat-ID: 8753
S. Peitz, S. Ober-Blöbaum, and M. Dellnitz, “Multiobjective Optimal Control Methods for the Navier-Stokes Equations Using Reduced Order Modeling,” Acta Applicandae Mathematicae, vol. 161, no. 1, pp. 171–199, 2018, doi: 10.1007/s10440-018-0209-7.
LibreCat | DOI
 
[8]
2017 | Conference Paper | LibreCat-ID: 5914
M. Dellnitz et al., “Multiobjective Optimal Control Methods for the Development of an Intelligent Cruise Control,” in Progress in Industrial Mathematics at ECMI 2014 , 2017, pp. 633–641.
LibreCat | DOI
 
[7]
2017 | Conference Paper | LibreCat-ID: 8752
S. Peitz and M. Dellnitz, “Gradient-Based Multiobjective Optimization with Uncertainties,” in NEO 2016, 2017, pp. 159–182.
LibreCat | DOI
 
[6]
2017 | Dissertation | LibreCat-ID: 10594 | OA
S. Peitz,   Exploiting structure in multiobjective optimization and optimal control. 2017.
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[5]
2017 | Journal Article | LibreCat-ID: 8756
S. Peitz, K. Schäfer, S. Ober-Blöbaum, J. Eckstein, U. Köhler, and M. Dellnitz, “A multiobjective MPC approach for autonomously driven electric vehicles,” Proceedings of the 20th World Congress of the International Federation of Automatic Control (IFAC), vol. 50, no. 1, pp. 8674–8679, 2017, doi: 10.1016/j.ifacol.2017.08.1526.
LibreCat | DOI
 
[4]
2016 | Conference Paper | LibreCat-ID: 8759
S. Peitz, M. Gräler, C. Henke, M. H. Molo, M. Dellnitz, and A. Trächtler, “Multiobjective Model Predictive Control of an Industrial Laundry,” in Procedia Technology, 2016, pp. 483–490.
LibreCat | DOI
 
[3]
2016 | Conference Paper | LibreCat-ID: 8758
J. Eckstein et al., “A comparison of two predictive approaches to control the longitudinal dynamics of electric vehicles,” in Procedia Technology, 3rd International Conference on System-Integrated Intelligence: New Challenges for Product and Production Engineering, 2016, vol. 26, pp. 465–472, doi: 10.1016/j.protcy.2016.08.059.
LibreCat | DOI
 
[2]
2016 | Conference Paper | LibreCat-ID: 29433
S. Peitz, S. Ober-Blöbaum, and M. Dellnitz, “Reduced order model based multiobjective optimal control of fluids,” 2016.
LibreCat
 
[1]
2015 | Conference Paper | LibreCat-ID: 8760
S. Peitz and M. Dellnitz, “Multiobjective Optimization of the Flow Around a Cylinder Using Model Order Reduction,” in PAMM, 2015, pp. 613–614.
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[90]
2026 | Journal Article | LibreCat-ID: 51160 | OA
F. M. Philipp, M. Schaller, S. Boshoff, S. Peitz, F. Nüske, and K. Worthmann, “Variance representations and convergence rates for data-driven approximations of Koopman operators,” Physica D: Nonlinear Phenomena, vol. 492, Art. no. 135223, 2026, doi: 10.1016/j.physd.2026.135223.
LibreCat | Files available | DOI | Download (ext.) | arXiv
 
[89]
2026 | Journal Article | LibreCat-ID: 53858 | OA
J. Akhter, P. D. Fährmann, K. Sonntag, and S. Peitz, “Common pitfalls to avoid while using multiobjective optimization in machine learning,” SIAM Review, vol. 68, no. 3, pp. 656–683, 2026, doi: 10.1137/24M1658875.
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[88]
2026 | Conference Paper | LibreCat-ID: 67308
A. C. Amakor, M. B. Berkemeier, M. C. Wohlleben, W. Sextro, and S. Peitz, “Surrogate-Assisted Multi-objective Design of Complex Multibody Systems,” in Artificial Neural Networks and Machine Learning – ICANN 2025, 2026, pp. 251–262, doi: 10.1007/978-3-032-04555-3_21.
LibreCat | DOI
 
[87]
2026 | Conference Paper | LibreCat-ID: 67307 | OA
S. S. Hotegni and S. Peitz, “SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion,” 2026, doi: 10.48550/arXiv.2509.21058.
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[86]
2026 | Preprint | LibreCat-ID: 67306 | OA
J. Stenner, H. Harder, and S. Peitz, “Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection,” arXiv:2606.30238. 2026.
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[85]
2026 | Preprint | LibreCat-ID: 67309
J. Stenner, A. Kilian, S. Peitz, and H. de Meer, “Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning,” arXiv:2606.30316. 2026.
LibreCat
 
[84]
2026 | Conference Paper | LibreCat-ID: 67300
S. S. Hotegni and S. Peitz, “Enhancing Adversarial Robustness Through Multi-objective Representation Learning,” in Artificial Neural Networks and Machine Learning – ICANN 2025, 2026, pp. 442–454, doi: 10.1007/978-3-032-04558-4_35.
LibreCat | DOI
 
[83]
2026 | Journal Article | LibreCat-ID: 67302
T. Markmann, M. Straat, S. Peitz, and B. Hammer, “Fourier neural operators as data-driven surrogates for two- and three-dimensional Rayleigh–Bénard convection,” Neurocomputing, vol. 679, p. 133201, 2026, doi: 10.1016/j.neucom.2026.133201.
LibreCat | DOI
 
[82]
2026 | Conference Paper | LibreCat-ID: 67299 | OA
H. Harder, A. Vishwasrao, L. Guastoni, R. Vinuesa, and S. Peitz, “Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems,” in Proceedings of The 8th Annual Learning for Dynamics and Control Conference, 2026, vol. 331, pp. 1601–1619, doi: 10.48550/arXiv.2511.04641.
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[81]
2026 | Conference Paper | LibreCat-ID: 67303 | OA
A. C. Amakor, K. Sonntag, and S. Peitz, “Interactive Pareto navigation for deep multi-task learning,” in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2026, pp. 652–668, doi: 10.1007/978-3-032-37667-1_37.
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[80]
2026 | Conference Paper | LibreCat-ID: 67305 | OA
J. Becktepe, A. Franz, N. Thuerey, and S. Peitz, “Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control,” 2026, doi: 10.48550/arXiv.2601.15015.
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[79]
2026 | Journal Article | LibreCat-ID: 67301
M. C. Wohlleben, J. Schütte, M. Berkemeier, W. Sextro, and S. Peitz, “Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings,” Multibody System Dynamics, 2026, doi: 10.1007/s11044-026-10146-9.
LibreCat | DOI
 
[78]
2026 | Preprint | LibreCat-ID: 67298 | OA
C. Mugisho Zagabe and S. Peitz, “Automatic feature identification in least-squares policy iteration using the Koopman operator framework,” arXiv:2603.26464. 2026.
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[77]
2026 | Preprint | LibreCat-ID: 67304 | OA
T. Plotzki and S. Peitz, “Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection,” arXiv:2603.28074. 2026.
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[76]
2025 | Journal Article | LibreCat-ID: 51159
A. C. Amakor, K. Sonntag, and S. Peitz, “A multiobjective continuation method to compute the regularization path of deep neural networks,” Machine Learning with Applications, vol. 19, Art. no. 100625, 2025, doi: 10.1016/j.mlwa.2025.100625.
LibreCat | Files available | DOI
 
[75]
2025 | Journal Article | LibreCat-ID: 46579 | OA
S. Peitz, H. Harder, F. Nüske, F. Philipp, M. Schaller, and K. Worthmann, “Equivariance and partial observations in Koopman operator theory for partial differential equations,” Journal of Computational Dynamics, vol. 12, pp. 305–324, 2025, doi: 10.3934/jcd.2024035.
LibreCat | DOI | Download (ext.) | arXiv
 
[74]
2025 | Conference Paper | LibreCat-ID: 53793 | OA
H. Harder and S. Peitz, “Solving partial differential equations with equivariant extreme learning machines,” in Systems theory in data and optimization, 2025, pp. 153–168, doi: 10.1007/978-3-031-83191-1_10.
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[73]
2025 | Conference Paper | LibreCat-ID: 67313
D. Voth, L. Dane, J. Grebe, S. Peitz, and P. Terhörst, “Effective Backdoor Learning on Open-Set Face Recognition Systems,” in 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 1027–1039, doi: 10.1109/WACV61041.2025.00109.
LibreCat | DOI
 
[72]
2025 | Preprint | LibreCat-ID: 67312
T. Markmann, M. Straat, S. Peitz, and B. Hammer, “Control of Rayleigh-Bénard Convection: Effectiveness of Reinforcement Learning in the Turbulent Regime,” arXiv:2504.12000. 2025.
LibreCat
 
[71]
2025 | Preprint | LibreCat-ID: 67310
S. Boshoff, S. Peitz, and S. Klus, “Bayesian Transfer Operators in Reproducing Kernel Hilbert Spaces,” arXiv:2509.22482. 2025.
LibreCat
 
[70]
2025 | Journal Article | LibreCat-ID: 57829
K. M. de Payrebrune et al., “The impact of AI on engineering design procedures for dynamical systems,” Technische Mechanik - European Journal of Engineering Mechanics, vol. 45, no. 1, pp. 1–23, 2025, doi: 10.24352/UB.OVGU-2025-037.
LibreCat | DOI
 
[69]
2025 | Journal Article | LibreCat-ID: 51158 | OA
F. Philipp, M. Schaller, K. Worthmann, S. Peitz, and F. Nüske, “Error analysis of kernel EDMD for prediction and control in the Koopman  framework,” ournal of Nonlinear Science, vol. 35, Art. no. 92, 2025, doi: 10.1007/s00332-025-10182-3.
LibreCat | DOI | Download (ext.) | arXiv
 
[68]
2025 | Journal Article | LibreCat-ID: 67319
K. M. de Payrebrune et al., “The impact of AI on engineering design procedures for dynamical systems,” Technische Mechanik - European Journal of Engineering Mechanics, vol. 45, no. 1, pp. 1–23, 2025, doi: 10.24352/UB.OVGU-2025-037.
LibreCat | DOI
 
[67]
2025 | Journal Article | LibreCat-ID: 67317
M. Berkemeier and S. Peitz, “Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients,” Open Journal of Mathematical Optimization, vol. 6, pp. 1–36, 2025, doi: 10.5802/ojmo.47.
LibreCat | DOI
 
[66]
2025 | Journal Article | LibreCat-ID: 67314
H. Harder, S. Peitz, F. Nüske, F. Philipp, M. Schaller, and K. Worthmann, “Group Convolutional Extended Dynamic Mode Decomposition,” Physica D: Nonlinear Phenomena, vol. 480, p. 134725, 2025, doi: 10.1016/j.physd.2025.134725.
LibreCat | DOI
 
[65]
2025 | Journal Article | LibreCat-ID: 67316
S. Peitz and S. S. Hotegni, “Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art,” Machine Learning with Applications, no. 100700, 2025, doi: 10.1016/j.mlwa.2025.100700.
LibreCat | DOI
 
[64]
2025 | Preprint | LibreCat-ID: 67315
M. Straat, T. Markmann, S. Peitz, and B. Hammer, “Improving the Robustness of Control of Chaotic Convective Flows with Domain-Informed Reinforcement Learning,” arXiv:2511.00272. 2025.
LibreCat
 
[63]
2025 | Preprint | LibreCat-ID: 67318
F. Fromme, C. Allen-Blanchette, H. Harder, and S. Peitz, “Surrogate Modeling of 3D Rayleigh-Bénard Convection with Equivariant Autoencoders,” arXiv:2505.13569. 2025.
LibreCat
 
[62]
2024 | Journal Article | LibreCat-ID: 46019 | OA
K. Sonntag and S. Peitz, “Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems,” Journal of Optimization Theory and Applications, 2024, doi: 10.1007/s10957-024-02389-3.
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[61]
2024 | Journal Article | LibreCat-ID: 40171 | OA
S. Peitz, J. Stenner, V. Chidananda, O. Wallscheid, S. L. Brunton, and K. Taira, “Distributed Control of Partial Differential Equations Using  Convolutional Reinforcement Learning,” Physica D: Nonlinear Phenomena, vol. 461, p. 134096, 2024, doi: 10.1016/j.physd.2024.134096.
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[60]
2024 | Journal Article | LibreCat-ID: 33461 | OA
S. E. Otto, S. Peitz, and C. W. Rowley, “Learning Bilinear Models of Actuated Koopman Generators from  Partially-Observed Trajectories,” SIAM Journal on Applied Dynamical Systems, vol. 23, no. 1, pp. 885–923, 2024, doi: 10.1137/22M1523601.
LibreCat | DOI | Download (ext.) | arXiv
 
[59]
2024 | Journal Article | LibreCat-ID: 38031 | OA
F. Philipp, M. Schaller, K. Worthmann, S. Peitz, and F. Nüske, “Error bounds for kernel-based approximations of the Koopman operator,” Applied and Computational Harmonic Analysis , vol. 71, Art. no. 101657, 2024, doi: 10.1016/j.acha.2024.101657.
LibreCat | DOI | Download (ext.) | arXiv
 
[58]
2024 | Journal Article | LibreCat-ID: 32447
K. Sonntag and S. Peitz, “Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping,” SIAM Journal on Optimization, vol. 34, no. 3, pp. 2259–2286, 2024, doi: 10.1137/23M1588512.
LibreCat | DOI
 
[57]
2024 | Conference Paper | LibreCat-ID: 46649 | OA
S. S. Hotegni, M. B. Berkemeier, and S. Peitz, “Multi-Objective Optimization for Sparse Deep Multi-Task Learning,” in 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 2024, p. 9, doi: 10.1109/IJCNN60899.2024.10650994.
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[56]
2024 | Journal Article | LibreCat-ID: 51334 | OA
K. Sonntag, B. Gebken, G. Müller, S. Peitz, and S. Volkwein, “A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces,” Journal of Optimization Theory and Applications, vol. 203, pp. 455–487, 2024, doi: 10.1007/s10957-024-02520-4.
LibreCat | DOI | Download (ext.) | arXiv
 
[55]
2024 | Conference Paper | LibreCat-ID: 62913
A. Hunstig, S. Peitz, H. Rose, and T. Meier, “Accelerating the analysis of optical quantum systems using the Koopman operator,” 2024, doi: 10.1109/cdc56724.2024.10886589.
LibreCat | DOI
 
[54]
2024 | Conference Paper | LibreCat-ID: 52758
H. Harder and S. Peitz, “On the continuity and smoothness of the value function in reinforcement learning and optimal control,” in IEEE 63rd conference on decision and control (CDC), 2024, pp. 1935–1940, doi: 10.1109/CDC56724.2024.10886529.
LibreCat | DOI
 
[53]
2024 | Book Chapter | LibreCat-ID: 46578 | OA
M. Bernreuther et al., “Multiobjective Optimization of Non-Smooth PDE-Constrained Problems,” in DFG Priority Programme 1962, Birkhäuser, 2024.
LibreCat | Download (ext.) | arXiv
 
[52]
2024 | Conference Paper | LibreCat-ID: 42160 | OA
S. Werner and S. Peitz, “Numerical evidence for sample efficiency of model-based over model-free reinforcement learning control of partial differential equations,” in European Control Conference (ECC), 2024, pp. 2958–2964, doi: 10.23919/ECC64448.2024.10590945.
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[51]
2024 | Conference Paper | LibreCat-ID: 48502 | OA
S. Peitz, A. Hunstig, H. Rose, and T. Meier, “Accelerating the analysis of optical quantum systems using the Koopman operator,” in IEEE 63rd Conference on Decision and Control (CDC), 2024, pp. 7964–7969, doi: 10.1109/CDC56724.2024.10886589.
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[50]
2024 | Preprint | LibreCat-ID: 67320
M. Berkemeier, K. Sonntag, and S. Peitz, “New Nonlinear Conjugate Gradient Methods with Guaranteed Descent for Multi-Objective Optimization,” Optimizaton Online. 2024.
LibreCat
 
[49]
2023 | Journal Article | LibreCat-ID: 21199 | OA
S. Peitz and K. Bieker, “On the Universal Transformation of Data-Driven Models to Control Systems,” Automatica, vol. 149, Art. no. 110840, 2023, doi: 10.1016/j.automatica.2022.110840.
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[48]
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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[47]
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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[46]
2023 | Journal Article | LibreCat-ID: 27426 | OA
B. Gebken, K. Bieker, and S. Peitz, “On the structure of regularization paths for piecewise differentiable regularization terms,” Journal of Global Optimization, vol. 85, no. 3, pp. 709–741, 2023, doi: 10.1007/s10898-022-01223-2.
LibreCat | DOI | Download (ext.)
 
[45]
2023 | Conference Paper | LibreCat-ID: 30125 | OA
M. Schaller, K. Worthmann, F. Philipp, S. Peitz, and F. Nüske, “Towards reliable data-based optimal and predictive control using extended DMD,” in IFAC-PapersOnLine, 2023, vol. 56, no. 1, pp. 169–174, doi: 10.1016/j.ifacol.2023.02.029.
LibreCat | DOI | Download (ext.) | arXiv
 
[44]
2023 | Journal Article | LibreCat-ID: 23428 | OA
F. Nüske, S. Peitz, F. Philipp, M. Schaller, and K. Worthmann, “Finite-data error bounds for Koopman-based prediction and control,” Journal of Nonlinear Science, vol. 33, Art. no. 14, 2023, doi: 10.1007/s00332-022-09862-1.
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[43]
2023 | Journal Article | LibreCat-ID: 21600
M. Dellnitz et al., “Efficient time stepping for numerical integration using reinforcement  learning,” SIAM Journal on Scientific Computing, vol. 45, no. 2, pp. A579–A595, 2023, doi: 10.1137/21M1412682.
LibreCat | Files available | DOI | Download (ext.) | arXiv
 
[42]
2023 | Journal Article | LibreCat-ID: 46784 | OA
O. Wallscheid et al., “ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids,” Journal of Open Source Software, vol. 8, no. 89, Art. no. 5616, 2023, doi: 10.21105/joss.05616.
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[41]
2023 | Conference Paper | LibreCat-ID: 46813 | OA
M. C. Wohlleben, L. Muth, S. Peitz, and W. Sextro, “Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits,” 2023, doi: 10.1002/pamm.202300039.
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[40]
2022 | Book Chapter | LibreCat-ID: 16296 | OA
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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[39]
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.
LibreCat | DOI
 
[38]
2022 | Journal Article | LibreCat-ID: 29673 | OA
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.
LibreCat | DOI | Download (ext.) | arXiv
 
[37]
2022 | Preprint | LibreCat-ID: 33150 | OA
M. B. Berkemeier and S. Peitz, “Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients,” arXiv:2208.12094. 2022.
LibreCat | Download (ext.) | arXiv
 
[36]
2022 | Journal Article | LibreCat-ID: 20731 | OA
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.
LibreCat | Files available | DOI | Download (ext.)
 
[35]
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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[34]
2021 | Journal Article | LibreCat-ID: 21337 | OA
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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[33]
2021 | Journal Article | LibreCat-ID: 16867 | OA
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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[32]
2021 | Journal Article | LibreCat-ID: 16295 | OA
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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[31]
2021 | Journal Article | LibreCat-ID: 16294 | OA
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.
LibreCat | DOI | Download (ext.)
 
[30]
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.
LibreCat | DOI
 
[29]
2020 | Journal Article | LibreCat-ID: 10596
O. Schütze, O. Cuate, A. Martín, S. Peitz, and M. Dellnitz, “Pareto Explorer: a global/local exploration tool for many-objective optimization problems,” Engineering Optimization, vol. 52, no. 5, pp. 832–855, 2020.
LibreCat | DOI
 
[28]
2020 | Journal Article | LibreCat-ID: 16288
S. Klus, F. Nüske, S. Peitz, J.-H. Niemann, C. Clementi, and C. Schütte, “Data-driven approximation of the Koopman generator: Model reduction, system identification, and control,” Physica D: Nonlinear Phenomena, vol. 406, 2020.
LibreCat | DOI
 
[27]
2020 | Book Chapter | LibreCat-ID: 16289
S. Peitz and S. Klus, “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, Cham: Springer, 2020, pp. 257–282.
LibreCat | DOI
 
[26]
2020 | Journal Article | LibreCat-ID: 16290 | OA
K. Bieker, S. Peitz, S. L. Brunton, J. N. Kutz, and M. Dellnitz, “Deep model predictive flow control with limited sensor data and online learning,” Theoretical and Computational Fluid Dynamics, vol. 34, pp. 577–591, 2020.
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[25]
2020 | Journal Article | LibreCat-ID: 16309
S. Peitz, S. E. Otto, and C. W. Rowley, “Data-Driven Model Predictive Control using Interpolated Koopman  Generators,” SIAM Journal on Applied Dynamical Systems, vol. 19, no. 3, pp. 2162–2193, 2020.
LibreCat | DOI | Download (ext.)
 
[24]
2020 | Journal Article | LibreCat-ID: 16297
C. I. Hernández Castellanos, S. Ober-Blöbaum, and S. Peitz, “Explicit Multi-objective Model Predictive Control for Nonlinear Systems  Under Uncertainty,” International Journal of Robust and Nonlinear Control, vol. 30(17), pp. 7593–7618, 2020, doi: 10.1002/rnc.5197.
LibreCat | DOI
 
[23]
2019 | Journal Article | LibreCat-ID: 10593
S. Peitz and S. Klus, “Koopman operator-based model reduction for switched-system control of PDEs,” Automatica, vol. 106, pp. 184–191, 2019.
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[22]
2019 | Journal Article | LibreCat-ID: 10595
B. Gebken, S. Peitz, and M. Dellnitz, “On the hierarchical structure of Pareto critical sets,” Journal of Global Optimization, vol. 73, no. 4, pp. 891–913, 2019.
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[21]
2019 | Conference Paper | LibreCat-ID: 10597
S. Hanke, S. Peitz, O. Wallscheid, J. Böcker, and M. Dellnitz, “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.
LibreCat | DOI
 
[20]
2019 | Conference Paper | LibreCat-ID: 29636
S. Hanke, S. Peitz, O. Wallscheid, J. Böcker, and M. Dellnitz, “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, pp. 1–6.
LibreCat
 
[19]
2018 | Preprint | LibreCat-ID: 21634 | OA
S. Hanke, S. Peitz, O. Wallscheid, S. Klus, J. Böcker, and M. Dellnitz, “Koopman Operator-Based Finite-Control-Set Model Predictive Control for  Electrical Drives,” arXiv:1804.00854. 2018.
LibreCat | Download (ext.)
 
[18]
2018 | Book Chapter | LibreCat-ID: 22796
F. Kummert et al., “Eingesetzte wissenschaftliche Methoden,” in Ressourceneffiziente Selbstoptimierende Wäscherei – Ergebnisse des ReSerW-Projekts, A. Trächtler, Ed. Paderborn: Springer, 2018.
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[17]
2018 | Conference Paper | LibreCat-ID: 8750
B. Gebken, S. Peitz, and M. Dellnitz, “A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems,” in Numerical and Evolutionary Optimization – NEO 2017, 2018.
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[16]
2018 | Journal Article | LibreCat-ID: 8751 | OA
S. Peitz and M. Dellnitz, “A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction,” Mathematical and Computational Applications, vol. 23, no. 2, 2018.
LibreCat | DOI | Download (ext.)
 
[15]
2018 | Book Chapter | LibreCat-ID: 8754
D. Beermann, M. Dellnitz, S. Peitz, and S. Volkwein, “Set-Oriented Multiobjective Optimal Control of PDEs Using Proper Orthogonal Decomposition,” in Reduced-Order Modeling (ROM) for Simulation and Optimization, Cham, 2018, pp. 47–72.
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[14]
2018 | Journal Article | LibreCat-ID: 8755
S. Klus, P. Gelß, S. Peitz, and C. Schütte, “Tensor-based dynamic mode decomposition,” Nonlinearity, vol. 31, no. 7, pp. 3359–3380, 2018.
LibreCat | DOI
 
[13]
2018 | Conference Paper | LibreCat-ID: 8757
D. Beermann, M. Dellnitz, S. Peitz, and S. Volkwein, “POD-based multiobjective optimal control of PDEs with non-smooth objectives,” in PAMM, 2018, pp. 51–54.
LibreCat | DOI
 
[12]
2018 | Preprint | LibreCat-ID: 16292 | OA
S. Peitz, “Controlling nonlinear PDEs using low-dimensional bilinear approximations  obtained from data,” arXiv:1801.06419. 2018.
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[11]
2018 | Preprint | LibreCat-ID: 16293 | OA
S. Klus, S. Peitz, and I. Schuster, “Analyzing high-dimensional time-series data using kernel transfer  operator eigenfunctions,” arXiv:1805.10118. 2018.
LibreCat | Download (ext.)
 
[10]
2018 | Journal Article | LibreCat-ID: 29624
S. Hanke, S. Peitz, O. Wallscheid, S. Klus, J. Böcker, and M. Dellnitz, “Koopman Operator-Based Finite-Control-Set Model Predictive Control for Electrical Drives,” arXiv preprint arXiv:1804.00854, 2018.
LibreCat
 
[9]
2018 | Journal Article | LibreCat-ID: 8753
S. Peitz, S. Ober-Blöbaum, and M. Dellnitz, “Multiobjective Optimal Control Methods for the Navier-Stokes Equations Using Reduced Order Modeling,” Acta Applicandae Mathematicae, vol. 161, no. 1, pp. 171–199, 2018, doi: 10.1007/s10440-018-0209-7.
LibreCat | DOI
 
[8]
2017 | Conference Paper | LibreCat-ID: 5914
M. Dellnitz et al., “Multiobjective Optimal Control Methods for the Development of an Intelligent Cruise Control,” in Progress in Industrial Mathematics at ECMI 2014 , 2017, pp. 633–641.
LibreCat | DOI
 
[7]
2017 | Conference Paper | LibreCat-ID: 8752
S. Peitz and M. Dellnitz, “Gradient-Based Multiobjective Optimization with Uncertainties,” in NEO 2016, 2017, pp. 159–182.
LibreCat | DOI
 
[6]
2017 | Dissertation | LibreCat-ID: 10594 | OA
S. Peitz,   Exploiting structure in multiobjective optimization and optimal control. 2017.
LibreCat | Files available | DOI | Download (ext.)
 
[5]
2017 | Journal Article | LibreCat-ID: 8756
S. Peitz, K. Schäfer, S. Ober-Blöbaum, J. Eckstein, U. Köhler, and M. Dellnitz, “A multiobjective MPC approach for autonomously driven electric vehicles,” Proceedings of the 20th World Congress of the International Federation of Automatic Control (IFAC), vol. 50, no. 1, pp. 8674–8679, 2017, doi: 10.1016/j.ifacol.2017.08.1526.
LibreCat | DOI
 
[4]
2016 | Conference Paper | LibreCat-ID: 8759
S. Peitz, M. Gräler, C. Henke, M. H. Molo, M. Dellnitz, and A. Trächtler, “Multiobjective Model Predictive Control of an Industrial Laundry,” in Procedia Technology, 2016, pp. 483–490.
LibreCat | DOI
 
[3]
2016 | Conference Paper | LibreCat-ID: 8758
J. Eckstein et al., “A comparison of two predictive approaches to control the longitudinal dynamics of electric vehicles,” in Procedia Technology, 3rd International Conference on System-Integrated Intelligence: New Challenges for Product and Production Engineering, 2016, vol. 26, pp. 465–472, doi: 10.1016/j.protcy.2016.08.059.
LibreCat | DOI
 
[2]
2016 | Conference Paper | LibreCat-ID: 29433
S. Peitz, S. Ober-Blöbaum, and M. Dellnitz, “Reduced order model based multiobjective optimal control of fluids,” 2016.
LibreCat
 
[1]
2015 | Conference Paper | LibreCat-ID: 8760
S. Peitz and M. Dellnitz, “Multiobjective Optimization of the Flow Around a Cylinder Using Model Order Reduction,” in PAMM, 2015, pp. 613–614.
LibreCat | DOI
 
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