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

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.

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.

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).

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).

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

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.

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).

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.

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