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).

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