Sebastian Peitz

According to our database, Sebastian Peitz authored at least 22 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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., March, 2024

On the continuity and smoothness of the value function in reinforcement learning and optimal control.

Hans Harder, Sebastian Peitz

CoRR, 2024

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, Septimus Boshoff, Marvin Meyer, Vikas Chidananda, Oliver Schweins

J. Open Source Softw., September, 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., April, 2023

On the structure of regularization paths for piecewise differentiable regularization terms.

Bennet Gebken, Katharina Bieker, Sebastian Peitz

J. Glob. Optim., March, 2023

On the universal transformation of data-driven models to control systems.

Sebastian Peitz, Katharina Bieker

Autom., March, 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

Multi-Objective Optimization for Sparse Deep Neural Network Training.

Sèdjro S. Hotegni, Sebastian Peitz, Manuel Berkemeier

CoRR, 2023

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

Augustina C. Amakor, Konstantin Sonntag, 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

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

Stefan Werner, Sebastian Peitz

CoRR, 2023

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

2022

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

2021

An Efficient Descent Method for Locally Lipschitz Multiobjective Optimization Problems.

Bennet Gebken, Sebastian Peitz

J. Optim. Theory Appl., 2021

Inverse multiobjective optimization: Inferring decision criteria from data.

Bennet Gebken, Sebastian Peitz

J. Glob. Optim., 2021

Derivative-Free Multiobjective Trust Region Descent MethodUsing Radial Basis Function Surrogate Models.

Manuel Berkemeier, Sebastian Peitz

CoRR, 2021

Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction.

Meike Wohlleben, Amelie Bender, Sebastian Peitz, Walter Sextro

Proceedings of the Machine Learning, Optimization, and Data Science, 2021

2020

Data-Driven Model Predictive Control using Interpolated Koopman Generators.

Sebastian Peitz, Samuel E. Otto, Clarence W. Rowley

SIAM J. Appl. Dyn. Syst., 2020

2019

On the hierarchical structure of Pareto critical sets.

Bennet Gebken, Sebastian Peitz, Michael Dellnitz

J. Glob. Optim., 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

Koopman operator-based model reduction for switched-system control of PDEs.

Sebastian Peitz, Stefan Klus

Autom., 2019

2018

Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions.

Stefan Klus, Sebastian Peitz, Ingmar Schuster

CoRR, 2018