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ORCIDSebastian Peitz
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2020 – today
- 2024
[j15]Konstantin Sonntag
, Sebastian Peitz:
Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems. J. Optim. Theory Appl. 201(2): 539-582 (2024)- [j14]Konstantin Sonntag, Bennet Gebken, Georg Müller, Sebastian Peitz, Stefan Volkwein:
A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces. J. Optim. Theory Appl. 203(1): 455-487 (2024) - [j13]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): 885-923 (2024) - [j12]Konstantin Sonntag, Sebastian Peitz:
Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping. SIAM J. Optim. 34(3): 2259-2286 (2024) - [c3]Stefan Werner, Sebastian Peitz:
Numerical Evidence for Sample Efficiency of Model-Based Over Model-Free Reinforcement Learning Control of Partial Differential Equations. ECC 2024: 2965-2971 - [c2]Sèdjro Salomon Hotegni, Manuel Berkemeier, Sebastian Peitz:
Multi-Objective Optimization for Sparse Deep Multi-Task Learning. IJCNN 2024: 1-9 - [i16]Hans Harder, Sebastian Peitz:
On the continuity and smoothness of the value function in reinforcement learning and optimal control. CoRR abs/2403.14432 (2024) - [i15]Hans Harder, Sebastian Peitz:
Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines. CoRR abs/2404.18530 (2024) - [i14]Junaid Akhter, Paul David Fährmann, Konstantin Sonntag, Sebastian Peitz:
Common pitfalls to avoid while using multiobjective optimization in machine learning. CoRR abs/2405.01480 (2024) - [i13]Sèdjro Salomon Hotegni, Sebastian Peitz:
MOREL: Enhancing Adversarial Robustness through Multi-Objective Representation Learning. CoRR abs/2410.01697 (2024) - 2023
- [j11]Sebastian Peitz, Katharina Bieker:
On the universal transformation of data-driven models to control systems. Autom. 149: 110840 (2023) - [j10]Bennet Gebken, Katharina Bieker, Sebastian Peitz:
On the structure of regularization paths for piecewise differentiable regularization terms. J. Glob. Optim. 85(3): 709-741 (2023) - [j9]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): 14 (2023) - [j8]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): 5616 (2023) - [j7]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): 579- (2023) - [i12]Sebastian Peitz, Jan Stenner, Vikas Chidananda, Oliver Wallscheid, Steven L. Brunton, Kunihiko Taira:
Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning. CoRR abs/2301.10737 (2023) - [i11]Stefan Werner, Sebastian Peitz:
Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs. CoRR abs/2302.07160 (2023) - [i10]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 abs/2307.15325 (2023) - [i9]Augustina C. Amakor, Konstantin Sonntag, Sebastian Peitz:
A multiobjective continuation method to compute the regularization path of deep neural networks. CoRR abs/2308.12044 (2023) - [i8]Sèdjro S. Hotegni, Sebastian Peitz, Manuel Berkemeier:
Multi-Objective Optimization for Sparse Deep Neural Network Training. CoRR abs/2308.12243 (2023) - 2022
- [j6]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): 7797-7808 (2022) - [i7]Samuel E. Otto, Sebastian Peitz, Clarence W. Rowley:
Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories. CoRR abs/2209.09977 (2022) - 2021
- [j5]Bennet Gebken, Sebastian Peitz:
Inverse multiobjective optimization: Inferring decision criteria from data. J. Glob. Optim. 80(1): 3-29 (2021) - [j4]Bennet Gebken, Sebastian Peitz:
An Efficient Descent Method for Locally Lipschitz Multiobjective Optimization Problems. J. Optim. Theory Appl. 188(3): 696-723 (2021) - [c1]Meike Wohlleben, Amelie Bender, Sebastian Peitz, Walter Sextro:
Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction. LOD 2021: 101-115 - [i6]Sebastian Peitz, Katharina Bieker:
On the Universal Transformation of Data-Driven Models to Control Systems. CoRR abs/2102.04722 (2021) - [i5]Manuel Berkemeier, Sebastian Peitz:
Derivative-Free Multiobjective Trust Region Descent MethodUsing Radial Basis Function Surrogate Models. CoRR abs/2102.13444 (2021) - [i4]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 abs/2104.03562 (2021) - 2020
- [j3]Sebastian Peitz, Samuel E. Otto, Clarence W. Rowley:
Data-Driven Model Predictive Control using Interpolated Koopman Generators. SIAM J. Appl. Dyn. Syst. 19(3): 2162-2193 (2020) - [i3]Katharina Bieker, Bennet Gebken, Sebastian Peitz:
On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation. CoRR abs/2012.07483 (2020)
2010 – 2019
- 2019
- [j2]Sebastian Peitz, Stefan Klus:
Koopman operator-based model reduction for switched-system control of PDEs. Autom. 106: 184-191 (2019) - [j1]Bennet Gebken, Sebastian Peitz, Michael Dellnitz:
On the hierarchical structure of Pareto critical sets. J. Glob. Optim. 73(4): 891-913 (2019) - [i2]Katharina Bieker, Sebastian Peitz, Steven L. Brunton, J. Nathan Kutz, Michael Dellnitz:
Deep Model Predictive Control with Online Learning for Complex Physical Systems. CoRR abs/1905.10094 (2019) - 2018
- [i1]Stefan Klus, Sebastian Peitz, Ingmar Schuster:
Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions. CoRR abs/1805.10118 (2018)
Coauthor Index
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last updated on 2024-11-11 22:24 CET by the dblp team
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