Publications - Data Science for Engineering

A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces

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

arXiv

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.

DOI

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).

DOI arXiv

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).

arXiv

Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping

K. Sonntag, S. Peitz, SIAM Journal on Optimization 34 (2024) 2259–2286.

DOI

Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems

K. Sonntag, S. Peitz, Journal of Optimization Theory and Applications (2024).

DOI

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.

DOI arXiv

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.

DOI

A multiobjective continuation method to compute the regularization path of deep neural networks

A.C. Amakor, K. Sonntag, S. Peitz, ArXiv (2023).

Accelerating the analysis of optical quantum systems using the Koopman operator

S. Peitz, A. Hunstig, H. Rose, T. Meier, (2023).

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.

DOI GitHub arXiv

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).

DOI

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.

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).

arXiv

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).

DOI

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.

Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs

S. Werner, S. Peitz, ArXiv:2302.07160 (2023).

arXiv

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).

arXiv

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.

DOI

On the Universal Transformation of Data-Driven Models to Control Systems

S. Peitz, K. Bieker, Automatica 149 (2023).

DOI

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).

arXiv

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.

DOI arXiv

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.

DOI

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.

DOI

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.

DOI

Koopman analysis of quantum systems

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

DOI arXiv

Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients

M.B. Berkemeier, S. Peitz, ArXiv:2208.12094 (2022).

arXiv

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.

DOI

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.

DOI

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

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

DOI

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