A multiobjective continuation method to compute the regularization path of deep neural networks
A.C. Amakor, K. Sonntag, S. Peitz, ArXiv (2023).
LibreCat: /record/51159

Error analysis of kernel EDMD for prediction and control in the Koopman  framework
F. Philipp, M. Schaller, K. Worthmann, S. Peitz, F. Nüske, ArXiv:2312.10460 (2023).
LibreCat: /record/51158
arXiv: 2312.10460

Fast Convergence of Inertial Multiobjective Gradient-like Systems with Asymptotic Vanishing Damping
K. Sonntag, S. Peitz, ArXiv:2307.00975 (2023).
LibreCat: /record/32447
arXiv: 2307.00975

Multiobjective Optimization of Non-Smooth PDE-Constrained Problems
M. Bernreuther, M. Dellnitz, B. Gebken, G. Müller, S. Peitz, K. Sonntag, S. Volkwein, ArXiv:2308.01113 (2023).
LibreCat: /record/46578
arXiv: 2308.01113

ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical Continuation
S. Banholzer, B. Gebken, M. Dellnitz, S. Peitz, S. Volkwein, in: H. Michael, H. Roland, K. Christian, U. Michael, U. Stefan (Eds.), Non-Smooth and Complementarity-Based Distributed Parameter Systems, Springer, Cham, 2022, pp. 43–76.
LibreCat: /record/16296
DOI: 10.1007/978-3-030-79393-7_3

Efficient Virtual Design and Testing of Autonomous Vehicles
S. Peitz, M. Dellnitz, S. Bannenberg, in: H.G. Bock, K.-H. Küfer, P. Maas, A. Milde, V. Schulz (Eds.), German Success Stories in Industrial Mathematics, Springer International Publishing, Cham, 2022.
LibreCat: /record/30294
DOI: 10.1007/978-3-030-81455-7_23

Koopman analysis of quantum systems
S. Klus, F. Nüske, S. Peitz, Journal of Physics A: Mathematical and Theoretical 55 (2022) 314002.
LibreCat: /record/29673
DOI: 10.1088/1751-8121/ac7d22
arXiv: 2201.12062

Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients
M.B. Berkemeier, S. Peitz, ArXiv:2208.12094 (2022).
LibreCat: /record/33150
arXiv: 2208.12094

On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation
K. Bieker, B. Gebken, S. Peitz, IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (2022) 7797–7808.
LibreCat: /record/20731
DOI: 10.1109/TPAMI.2021.3114962

Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction
M.C. Wohlleben, A. Bender, S. Peitz, W. Sextro, in: Machine Learning, Optimization, and Data Science, Springer International Publishing, Cham, 2022.
LibreCat: /record/29727
DOI: 10.1007/978-3-030-95470-3_8

Derivative-Free Multiobjective Trust Region Descent Method Using Radial  Basis Function Surrogate Models
M.B. Berkemeier, S. Peitz, Mathematical and Computational Applications 26 (2021).
LibreCat: /record/21337
DOI: 10.3390/mca26020031

An efficient descent method for locally Lipschitz multiobjective optimization problems
B. Gebken, S. Peitz, Journal of Optimization Theory and Applications 188 (2021) 696–723.
LibreCat: /record/16867
DOI: 10.1007/s10957-020-01803-w

Inverse multiobjective optimization: Inferring decision criteria from data
B. Gebken, S. Peitz, Journal of Global Optimization 80 (2021) 3–29.
LibreCat: /record/16295
DOI: 10.1007/s10898-020-00983-z

Explicit multiobjective model predictive control for nonlinear systems  with symmetries
S. Ober-Blöbaum, S. Peitz, International Journal of Robust and Nonlinear Control 31(2) (2021) 380–403.
LibreCat: /record/16294
DOI: 10.1002/rnc.5281

Symmetry in Optimal Control: A Multiobjective Model Predictive Control Approach
K. Flaßkamp, S. Ober-Blöbaum, S. Peitz, in: O. Junge, O. Schütze, G. Froyland, S. Ober-Blöbaum, K. Padberg-Gehle (Eds.), Advances in Dynamics, Optimization and Computation, Springer, Cham, 2020.
LibreCat: /record/17411
DOI: 10.1007/978-3-030-51264-4_9

Pareto Explorer: a global/local exploration tool for many-objective optimization problems
O. Schütze, O. Cuate, A. Martín, S. Peitz, M. Dellnitz, Engineering Optimization 52 (2020) 832–855.
LibreCat: /record/10596
DOI: 10.1080/0305215x.2019.1617286

Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
S. Klus, F. Nüske, S. Peitz, J.-H. Niemann, C. Clementi, C. Schütte, Physica D: Nonlinear Phenomena 406 (2020).
LibreCat: /record/16288
DOI: 10.1016/j.physd.2020.132416

Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator
S. Peitz, S. Klus, in: Lecture Notes in Control and Information Sciences, Springer, Cham, 2020, pp. 257–282.
LibreCat: /record/16289
DOI: 10.1007/978-3-030-35713-9_10

Deep model predictive flow control with limited sensor data and online learning
K. Bieker, S. Peitz, S.L. Brunton, J.N. Kutz, M. Dellnitz, Theoretical and Computational Fluid Dynamics 34 (2020) 577–591.
LibreCat: /record/16290
DOI: 10.1007/s00162-020-00520-4

Data-Driven Model Predictive Control using Interpolated Koopman  Generators
S. Peitz, S.E. Otto, C.W. Rowley, SIAM Journal on Applied Dynamical Systems 19 (2020) 2162–2193.
LibreCat: /record/16309
DOI: 10.1137/20M1325678