Sebastian Peitz

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 31

Top Topics

Multiobjective Optimization

Partially Observed

Reinforcement Learning

Predictive Control

Top Venues

J. Glob. Optim.

J. Optim. Theory Appl.

SIAM J. Appl. Dyn. Syst.

Publications

Samuel E. Otto, Sebastian Peitz, Clarence W. Rowley
Learning Bilinear Models of Actuated Koopman Generators from Partially Observed Trajectories. SIAM J. Appl. Dyn. Syst. 23 (1) (2024)
Stefan Werner, Sebastian Peitz
Numerical Evidence for Sample Efficiency of Model-Based Over Model-Free Reinforcement Learning Control of Partial Differential Equations. ECC (2024)
Hans Harder, Sebastian Peitz
On the continuity and smoothness of the value function in reinforcement learning and optimal control. CoRR (2024)
Junaid Akhter, Paul David Fährmann, Konstantin Sonntag, Sebastian Peitz
Common pitfalls to avoid while using multiobjective optimization in machine learning. CoRR (2024)
Hans Harder, Sebastian Peitz
Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines. CoRR (2024)
Konstantin Sonntag, Sebastian Peitz
Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping. SIAM J. Optim. 34 (3) (2024)
Konstantin Sonntag, Sebastian Peitz
Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems. J. Optim. Theory Appl. 201 (2) (2024)
Sèdjro S. Hotegni, Sebastian Peitz, Manuel Berkemeier
Multi-Objective Optimization for Sparse Deep Neural Network Training. CoRR (2023)
Oliver Wallscheid, Sebastian Peitz, Jan Stenner, Daniel Weber, Septimus Boshoff, Marvin Meyer, Vikas Chidananda, Oliver Schweins
ElectricGrid.jl - A Julia-based modeling and simulation tool for power electronics-driven electric energy grids. J. Open Source Softw. 8 (89) (2023)
Augustina C. Amakor, Konstantin Sonntag, Sebastian Peitz
A multiobjective continuation method to compute the regularization path of deep neural networks. CoRR (2023)
Feliks Nüske, Sebastian Peitz, Friedrich Philipp, Manuel Schaller, Karl Worthmann
Finite-Data Error Bounds for Koopman-Based Prediction and Control. J. Nonlinear Sci. 33 (1) (2023)
Sebastian Peitz, Hans Harder, Feliks Nüske, Friedrich Philipp, Manuel Schaller, Karl Worthmann
Partial observations, coarse graining and equivariance in Koopman operator theory for large-scale dynamical systems. CoRR (2023)
Sebastian Peitz, Katharina Bieker
On the universal transformation of data-driven models to control systems. Autom. 149 (2023)
Bennet Gebken, Katharina Bieker, Sebastian Peitz
On the structure of regularization paths for piecewise differentiable regularization terms. J. Glob. Optim. 85 (3) (2023)
Michael Dellnitz, Eyke Hüllermeier, Marvin Lücke, Sina Ober-Blöbaum, Christian Offen, Sebastian Peitz, Karlson Pfannschmidt
Efficient Time-Stepping for Numerical Integration Using Reinforcement Learning. SIAM J. Sci. Comput. 45 (2) (2023)
Sebastian Peitz, Jan Stenner, Vikas Chidananda, Oliver Wallscheid, Steven L. Brunton, Kunihiko Taira
Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning. CoRR (2023)
Stefan Werner, Sebastian Peitz
Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs. CoRR (2023)
Samuel E. Otto, Sebastian Peitz, Clarence W. Rowley
Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories. CoRR (2022)
Katharina Bieker, Bennet Gebken, Sebastian Peitz
On the Treatment of Optimization Problems With L1 Penalty Terms via Multiobjective Continuation. IEEE Trans. Pattern Anal. Mach. Intell. 44 (11) (2022)
Bennet Gebken, Sebastian Peitz
Inverse multiobjective optimization: Inferring decision criteria from data. J. Glob. Optim. 80 (1) (2021)
Sebastian Peitz, Katharina Bieker
On the Universal Transformation of Data-Driven Models to Control Systems. CoRR (2021)
Michael Dellnitz, Eyke Hüllermeier, Marvin Lücke, Sina Ober-Blöbaum, Christian Offen, Sebastian Peitz, Karlson Pfannschmidt
Efficient time stepping for numerical integration using reinforcement learning. CoRR (2021)
Meike Wohlleben, Amelie Bender, Sebastian Peitz, Walter Sextro
Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction. LOD (2021)
Bennet Gebken, Sebastian Peitz
An Efficient Descent Method for Locally Lipschitz Multiobjective Optimization Problems. J. Optim. Theory Appl. 188 (3) (2021)
Manuel Berkemeier, Sebastian Peitz
Derivative-Free Multiobjective Trust Region Descent MethodUsing Radial Basis Function Surrogate Models. CoRR (2021)
Katharina Bieker, Bennet Gebken, Sebastian Peitz
On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation. CoRR (2020)
Sebastian Peitz, Samuel E. Otto, Clarence W. Rowley
Data-Driven Model Predictive Control using Interpolated Koopman Generators. SIAM J. Appl. Dyn. Syst. 19 (3) (2020)
Sebastian Peitz, Stefan Klus
Koopman operator-based model reduction for switched-system control of PDEs. Autom. 106 (2019)
Katharina Bieker, Sebastian Peitz, Steven L. Brunton, J. Nathan Kutz, Michael Dellnitz
Deep Model Predictive Control with Online Learning for Complex Physical Systems. CoRR (2019)
Bennet Gebken, Sebastian Peitz, Michael Dellnitz
On the hierarchical structure of Pareto critical sets. J. Glob. Optim. 73 (4) (2019)
Stefan Klus, Sebastian Peitz, Ingmar Schuster
Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions. CoRR (2018)