Learning a model is paramount for sample efficiency in reinforcement  learning control of PDEs
S. Werner, S. Peitz, ArXiv:2302.07160 (2023).
LibreCat: /record/42160
arXiv: 2302.07160

On the structure of regularization paths for piecewise differentiable regularization terms
B. Gebken, K. Bieker, S. Peitz, Journal of Global Optimization 85 (2023) 709–741.
LibreCat: /record/27426
DOI: 10.1007/s10898-022-01223-2

Towards reliable data-based optimal and predictive control using extended DMD
M. Schaller, K. Worthmann, F. Philipp, S. Peitz, F. Nüske, in: IFAC-PapersOnLine, 2023, pp. 169–174.
LibreCat: /record/30125
DOI: 10.1016/j.ifacol.2023.02.029
arXiv: 2202.09084

Partial observations, coarse graining and equivariance in Koopman  operator theory for large-scale dynamical systems
S. Peitz, H. Harder, F. Nüske, F. Philipp, M. Schaller, K. Worthmann, ArXiv:2307.15325 (2023).
LibreCat: /record/46579
arXiv: 2307.15325

Finite-data error bounds for Koopman-based prediction and control
F. Nüske, S. Peitz, F. Philipp, M. Schaller, K. Worthmann, Journal of Nonlinear Science 33 (2023).
LibreCat: /record/23428
DOI: 10.1007/s00332-022-09862-1

Multi-Objective Optimization for Sparse Deep Neural Network Training
S.S. Hotegni, S. Peitz, M.B. Berkemeier, ArXiv:2308.12243 (2023).
LibreCat: /record/46649
arXiv: 2308.12243

Efficient time stepping for numerical integration using reinforcement  learning
M. Dellnitz, E. Hüllermeier, M. Lücke, S. Ober-Blöbaum, C. Offen, S. Peitz, K. Pfannschmidt, SIAM Journal on Scientific Computing 45 (2023) 9–5.
LibreCat: /record/21600
DOI: 10.1137/21M1412682
arXiv: arXiv:2104.03562

ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids
O. Wallscheid, S. Peitz, J. Stenner, D. Weber, S. Boshoff, M. Meyer, V. Chidananda, O. Schweins, Journal of Open Source Software 8 (2023).
LibreCat: /record/46784
DOI: 10.21105/joss.05616

Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits
M.C. Wohlleben, L. Muth, S. Peitz, W. Sextro, in: Proceedings in Applied Mathematics and Mechanics, Wiley, 2023.
LibreCat: /record/46813
DOI: 10.1002/pamm.202300039

Accelerating the analysis of optical quantum systems using the Koopman operator
S. Peitz, A. Hunstig, H. Rose, T. Meier, (2023).
LibreCat: /record/48502

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