Pub­lic­a­tions

A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces

K. Sonntag, B. Gebken, G. Müller, S. Peitz, S. Volkwein, ArXiv:2402.06376 (2024).

Common pitfalls to avoid while using multiobjective optimization in machine learning

J. Akhter, P.D. Fährmann, K. Sonntag, S. Peitz, ArXiv (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.

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).

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).

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.

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.

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) A579–A595.

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).

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).

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).

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).

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.

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).

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.

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.

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.

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.

Koopman analysis of quantum systems

S. Klus, F. Nüske, S. Peitz, Journal of Physics A: Mathematical and Theoretical 55 (2022) 314002.

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.

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.

Derivative-Free Multiobjective Trust Region Descent Method Using Radial Basis Function Surrogate Models

M.B. Berkemeier, S. Peitz, Mathematical and Computational Applications 26 (2021).

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