Extended Dynamic Mode Decomposition: Sharp bounds on the sample  efficiency
F.M. Philipp, M. Schaller, S. Boshoff, S. Peitz, F. Nüske, K. Worthmann, ArXiv:2402.02494 (2024).

Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems
K. Sonntag, S. Peitz, Journal of Optimization Theory and Applications (2024).

A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces
K. Sonntag, B. Gebken, G. Müller, S. Peitz, S. Volkwein, ArXiv:2402.06376 (2024).

Distributed Control of Partial Differential Equations Using  Convolutional Reinforcement Learning
S. Peitz, J. Stenner, V. Chidananda, O. Wallscheid, S.L. Brunton, K. Taira, Physica D: Nonlinear Phenomena 461 (2024) 134096.

Learning Bilinear Models of Actuated Koopman Generators from  Partially-Observed Trajectories
S.E. Otto, S. Peitz, C.W. Rowley, SIAM Journal on Applied Dynamical Systems 23 (2024) 885–923.

Error bounds for kernel-based approximations of the Koopman operator
F. Philipp, M. Schaller, K. Worthmann, S. Peitz, F. Nüske, Applied and Computational Harmonic Analysis  71 (2024).

On the Universal Transformation of Data-Driven Models to Control Systems
S. Peitz, K. Bieker, Automatica 149 (2023).

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.

Approximation Algorithms for Fair Range Clustering
S.S. Hotegni, S. Mahabadi, A. Vakilian, in: Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023., n.d.

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).

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

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).