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).

Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines
H. Harder, S. Peitz, (n.d.).

On the continuity and smoothness of the value function in reinforcement learning and optimal control
H. Harder, S. Peitz, (2024).

Common pitfalls to avoid while using multiobjective optimization in machine learning
J. Akhter, P.D. Fährmann, K. Sonntag, S. Peitz, ArXiv (2024).

Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping
K. Sonntag, S. Peitz, SIAM Journal on Optimization 34 (2024) 2259–2286.

Multi-Objective Optimization for Sparse Deep Multi-Task Learning
S.S. Hotegni, M.B. Berkemeier, S. Peitz, in: 2024 International Joint Conference on Neural Networks (IJCNN), IEEE, Yokohama, Japan, 2024, p. 9.

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

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).

Hybrid control of interconnected power converters using both expert-driven droop and data-driven reinforcement learning approaches
S. Boshoff, J. Stenner, D. Weber, M. Meyer, V. Chidananda, S. Peitz, O. Wallscheid, in: IEEE Power and Energy Student Summit (PESS), VDE, 2023, pp. 124–129.

ElectricGrid.jl – Automated modeling of decentralized electrical energy grids
M. Meyer, D. Weber, V. Chidananda, O. Schweins, J. Stenner, S. Boshoff, S. Peitz, O. Wallscheid, in: IEEE Power and Energy Student Summit (PESS), VDE, 2023, pp. 112–117.

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.