Amakor, Augustina Chidinma, et al. “A Multiobjective Continuation Method to Compute the Regularization Path of Deep Neural Networks.” ArXiv, 2023.
LibreCat: /record/51159

Philipp, Friedrich, et al. “Error Analysis of Kernel EDMD for Prediction and Control in the Koopman  Framework.” ArXiv:2312.10460, 2023.
LibreCat: /record/51158
arXiv: 2312.10460

Sonntag, Konstantin, and Sebastian Peitz. “Fast Convergence of Inertial Multiobjective Gradient-like Systems with Asymptotic Vanishing Damping.” ArXiv:2307.00975, 2023.
LibreCat: /record/32447
arXiv: 2307.00975

Bernreuther, Marco, et al. “Multiobjective Optimization of Non-Smooth PDE-Constrained Problems.” ArXiv:2308.01113, 2023.
LibreCat: /record/46578
arXiv: 2308.01113

Banholzer, Stefan, et al. “ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical Continuation.”10.1007/978-3-030-79393-7_3__Non-Smooth and Complementarity-Based Distributed Parameter Systems, edited by Hintermüller Michael et al., Springer, 2022, pp. 43–76, doi:10.1007/978-3-030-79393-7_3.
LibreCat: /record/16296
DOI: 10.1007/978-3-030-79393-7_3

Peitz, Sebastian, et al. “Efficient Virtual Design and Testing of Autonomous Vehicles.”10.1007/978-3-030-81455-7_23__German Success Stories in Industrial Mathematics, edited by H. G. Bock et al., vol. 35, Springer International Publishing, 2022, doi:10.1007/978-3-030-81455-7_23.
LibreCat: /record/30294
DOI: 10.1007/978-3-030-81455-7_23

Klus, Stefan, et al. “Koopman Analysis of Quantum Systems.”10.1088/1751-8121/ac7d22__Journal of Physics A: Mathematical and Theoretical, vol. 55, no. 31, IOP Publishing Ltd., 2022, p. 314002, doi:10.1088/1751-8121/ac7d22.
LibreCat: /record/29673
DOI: 10.1088/1751-8121/ac7d22
arXiv: 2201.12062

Berkemeier, Manuel Bastian, and Sebastian Peitz. “Multi-Objective Trust-Region Filter Method for Nonlinear Constraints Using Inexact Gradients.” ArXiv:2208.12094, 2022.
LibreCat: /record/33150
arXiv: 2208.12094

Bieker, Katharina, et al. “On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation.”10.1109/TPAMI.2021.3114962__IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 11, IEEE, 2022, pp. 7797–808, doi:10.1109/TPAMI.2021.3114962.
LibreCat: /record/20731
DOI: 10.1109/TPAMI.2021.3114962

Wohlleben, Meike Claudia, et al. “Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction.”10.1007/978-3-030-95470-3_8__Machine Learning, Optimization, and Data Science, Springer International Publishing, 2022, doi:10.1007/978-3-030-95470-3_8.
LibreCat: /record/29727
DOI: 10.1007/978-3-030-95470-3_8

Berkemeier, Manuel Bastian, and Sebastian Peitz. “Derivative-Free Multiobjective Trust Region Descent Method Using Radial  Basis Function Surrogate Models.”10.3390/mca26020031__Mathematical and Computational Applications, vol. 26, no. 2, 31, 2021, doi:10.3390/mca26020031.
LibreCat: /record/21337
DOI: 10.3390/mca26020031

Gebken, Bennet, and Sebastian Peitz. “An Efficient Descent Method for Locally Lipschitz Multiobjective Optimization Problems.”10.1007/s10957-020-01803-w__Journal of Optimization Theory and Applications, vol. 188, 2021, pp. 696–723, doi:10.1007/s10957-020-01803-w.
LibreCat: /record/16867
DOI: 10.1007/s10957-020-01803-w

Gebken, Bennet, and Sebastian Peitz. “Inverse Multiobjective Optimization: Inferring Decision Criteria from Data.”10.1007/s10898-020-00983-z__Journal of Global Optimization, vol. 80, Springer, 2021, pp. 3–29, doi:10.1007/s10898-020-00983-z.
LibreCat: /record/16295
DOI: 10.1007/s10898-020-00983-z

Ober-Blöbaum, Sina, and Sebastian Peitz. “Explicit Multiobjective Model Predictive Control for Nonlinear Systems  with Symmetries.”10.1002/rnc.5281__International Journal of Robust and Nonlinear Control, vol. 31(2), 2021, pp. 380–403, doi:10.1002/rnc.5281.
LibreCat: /record/16294
DOI: 10.1002/rnc.5281

Flaßkamp, Kathrin, et al. “Symmetry in Optimal Control: A Multiobjective Model Predictive Control Approach.”10.1007/978-3-030-51264-4_9__Advances in Dynamics, Optimization and Computation, edited by Oliver Junge et al., Springer, 2020, doi:10.1007/978-3-030-51264-4_9.
LibreCat: /record/17411
DOI: 10.1007/978-3-030-51264-4_9

Schütze, Oliver, et al. “Pareto Explorer: A Global/Local Exploration Tool for Many-Objective Optimization Problems.”10.1080/0305215x.2019.1617286__Engineering Optimization, vol. 52, no. 5, 2020, pp. 832–55, doi:10.1080/0305215x.2019.1617286.
LibreCat: /record/10596
DOI: 10.1080/0305215x.2019.1617286

Klus, Stefan, et al. “Data-Driven Approximation of the Koopman Generator: Model Reduction, System Identification, and Control.”10.1016/j.physd.2020.132416__Physica D: Nonlinear Phenomena, vol. 406, 132416, 2020, doi:10.1016/j.physd.2020.132416.
LibreCat: /record/16288
DOI: 10.1016/j.physd.2020.132416

Peitz, Sebastian, and Stefan Klus. “Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator.”10.1007/978-3-030-35713-9_10__Lecture Notes in Control and Information Sciences, vol. 484, Springer, 2020, pp. 257–82, doi:10.1007/978-3-030-35713-9_10.
LibreCat: /record/16289
DOI: 10.1007/978-3-030-35713-9_10

Bieker, Katharina, et al. “Deep Model Predictive Flow Control with Limited Sensor Data and Online Learning.”10.1007/s00162-020-00520-4__Theoretical and Computational Fluid Dynamics, vol. 34, 2020, pp. 577–591, doi:10.1007/s00162-020-00520-4.
LibreCat: /record/16290
DOI: 10.1007/s00162-020-00520-4

Peitz, Sebastian, et al. “Data-Driven Model Predictive Control Using Interpolated Koopman  Generators.”10.1137/20M1325678__SIAM Journal on Applied Dynamical Systems, vol. 19, no. 3, 2020, pp. 2162–93, doi:10.1137/20M1325678.
LibreCat: /record/16309
DOI: 10.1137/20M1325678