Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions. - Stefan Klus; Sebastian Peitz; Ingmar Schuster (CoRR, 2018)

Koopman operator-based model reduction for switched-system control of PDEs. - Sebastian Peitz; Stefan Klus (Autom., 2019)

Deep Model Predictive Control with Online Learning for Complex Physical Systems. - Katharina Bieker; Sebastian Peitz; Steven L. Brunton; J. Nathan Kutz; Michael Dellnitz (CoRR, 2019)

On the hierarchical structure of Pareto critical sets. - Bennet Gebken; Sebastian Peitz; Michael Dellnitz (J. Glob. Optim., 2019)

Data-Driven Model Predictive Control using Interpolated Koopman Generators. - Sebastian Peitz; Samuel E. Otto; Clarence W. Rowley (SIAM J. Appl. Dyn. Syst., 2020)

Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction. - Meike Wohlleben; Amelie Bender; Sebastian Peitz; Walter Sextro (LOD, 2021)

Derivative-Free Multiobjective Trust Region Descent MethodUsing Radial Basis Function Surrogate Models. - Manuel Berkemeier; Sebastian Peitz (CoRR, 2021)

Inverse multiobjective optimization: Inferring decision criteria from data. - Bennet Gebken; Sebastian Peitz (J. Glob. Optim., 2021)

An Efficient Descent Method for Locally Lipschitz Multiobjective Optimization Problems. - Bennet Gebken; Sebastian Peitz (J. Optim. Theory Appl., 2021)

On the Treatment of Optimization Problems With L1 Penalty Terms via Multiobjective Continuation. - Katharina Bieker; Bennet Gebken; Sebastian Peitz (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning. - Sebastian Peitz; Jan Stenner; Vikas Chidananda; Oliver Wallscheid; Steven L. Brunton; Kunihiko Taira (CoRR, 2023)

Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs. - Stefan Werner; Sebastian Peitz (CoRR, 2023)

Partial observations, coarse graining and equivariance in Koopman operator theory for large-scale dynamical systems. - Sebastian Peitz; Hans Harder; Feliks Nüske; Friedrich Philipp; Manuel Schaller; Karl Worthmann (CoRR, 2023)

A multiobjective continuation method to compute the regularization path of deep neural networks. - Augustina C. Amakor; Konstantin Sonntag; Sebastian Peitz (CoRR, 2023)

Multi-Objective Optimization for Sparse Deep Neural Network Training. - Sèdjro S. Hotegni; Sebastian Peitz; Manuel Berkemeier (CoRR, 2023)

Finite-Data Error Bounds for Koopman-Based Prediction and Control. - Feliks Nüske; Sebastian Peitz; Friedrich Philipp; Manuel Schaller; Karl Worthmann (J. Nonlinear Sci., 2023)

On the universal transformation of data-driven models to control systems. - Sebastian Peitz; Katharina Bieker (Autom., 2023)

On the structure of regularization paths for piecewise differentiable regularization terms. - Bennet Gebken; Katharina Bieker; Sebastian Peitz (J. Glob. Optim., 2023)

Efficient Time-Stepping for Numerical Integration Using Reinforcement Learning. - Michael Dellnitz; Eyke Hüllermeier; Marvin Lücke; Sina Ober-Blöbaum; Christian Offen; Sebastian Peitz; Karlson Pfannschmidt (SIAM J. Sci. Comput., 2023)

ElectricGrid.jl - A Julia-based modeling and simulation tool for power electronics-driven electric energy grids. - Oliver Wallscheid; Sebastian Peitz; Jan Stenner; Daniel Weber 004; Septimus Boshoff; Marvin Meyer; Vikas Chidananda; Oliver Schweins (J. Open Source Softw., 2023)

On the continuity and smoothness of the value function in reinforcement learning and optimal control. - Hans Harder; Sebastian Peitz (CDC, 2024)

Accelerating the analysis of optical quantum systems using the Koopman operator. - Anna Hunstig; Sebastian Peitz; Hendrik Rose; Torsten Meier (CDC, 2024)

Numerical Evidence for Sample Efficiency of Model-Based Over Model-Free Reinforcement Learning Control of Partial Differential Equations. - Stefan Werner; Sebastian Peitz (ECC, 2024)

Multi-Objective Optimization for Sparse Deep Multi-Task Learning. - Sèdjro Salomon Hotegni; Manuel Berkemeier; Sebastian Peitz (IJCNN, 2024)

Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines. - Hans Harder; Sebastian Peitz (CoRR, 2024)

Common pitfalls to avoid while using multiobjective optimization in machine learning. - Junaid Akhter; Paul David Fährmann; Konstantin Sonntag; Sebastian Peitz (CoRR, 2024)

MOREL: Enhancing Adversarial Robustness through Multi-Objective Representation Learning. - Sèdjro Salomon Hotegni; Sebastian Peitz (CoRR, 2024)

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art. - Sebastian Peitz; Sèdjro Salomon Hotegni (CoRR, 2024)

The impact of AI on engineering design procedures for dynamical systems. - Kristin Miriam de Payrebrune; Kathrin Flaßkamp; Tom Ströhla; Thomas Sattel; Dieter Bestle; Benedict Röder; Peter Eberhard; Sebastian Peitz; Marcus Stoffel; Rutwik Gulakala; Aditya Borse; Meike Wohlleben; et al. (CoRR, 2024)

Surrogate-assisted multi-objective design of complex multibody systems. - Augustina C. Amakor; Manuel Berkemeier; Meike Wohlleben; Walter Sextro; Sebastian Peitz (CoRR, 2024)

Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping. - Konstantin Sonntag; Sebastian Peitz (SIAM J. Optim., 2024)

Learning Bilinear Models of Actuated Koopman Generators from Partially Observed Trajectories. - Samuel E. Otto; Sebastian Peitz; Clarence W. Rowley (SIAM J. Appl. Dyn. Syst., 2024)

Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems. - Konstantin Sonntag; Sebastian Peitz (J. Optim. Theory Appl., 2024)

A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces. - Konstantin Sonntag; Bennet Gebken; Georg Müller; Sebastian Peitz; Stefan Volkwein (J. Optim. Theory Appl., 2024)

Effective Backdoor Learning on Open-Set Face Recognition Systems. - Diana Voth; Leonidas Dane; Jonas Grebe; Sebastian Peitz; Philipp Terhörst (WACV, 2025)

Control of Rayleigh-Bénard Convection: Effectiveness of Reinforcement Learning in the Turbulent Regime. - Thorben Markmann; Michiel Straat; Sebastian Peitz; Barbara Hammer (CoRR, 2025)

Surrogate Modeling of 3D Rayleigh-Benard Convection with Equivariant Autoencoders. - Fynn Fromme; Christine Allen-Blanchette; Hans Harder; Sebastian Peitz (CoRR, 2025)

Error Analysis of Kernel EDMD for Prediction and Control in the Koopman Framework. - Friedrich M. Philipp; Manuel Schaller; Karl Worthmann; Sebastian Peitz; Feliks Nüske (J. Nonlinear Sci., 2025)