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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) - [c5]Hans Harder, Sebastian Peitz:
On the continuity and smoothness of the value function in reinforcement learning and optimal control. CDC 2024: 1935-1940 - [c4]Anna Hunstig, Sebastian Peitz, Hendrik Rose, Torsten Meier:
Accelerating the analysis of optical quantum systems using the Koopman operator. CDC 2024: 7964-7969 - [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 - [i19]Hans Harder, Sebastian Peitz:
On the continuity and smoothness of the value function in reinforcement learning and optimal control. CoRR abs/2403.14432 (2024) - [i18]Hans Harder, Sebastian Peitz:
Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines. CoRR abs/2404.18530 (2024) - [i17]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) - [i16]Sèdjro Salomon Hotegni, Sebastian Peitz:
MOREL: Enhancing Adversarial Robustness through Multi-Objective Representation Learning. CoRR abs/2410.01697 (2024) - [i15]Sebastian Peitz, Sèdjro Salomon Hotegni:
Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art. CoRR abs/2412.01566 (2024) - [i14]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, Walter Sextro, Maximilian Raff, C. David Remy, Manish Yadav, Merten Stender, Jan van Delden, Timo Lüddecke, Sabine C. Langer, Julius Schultz, Christopher Blech:
The impact of AI on engineering design procedures for dynamical systems. CoRR abs/2412.12230 (2024) - [i13]Augustina C. Amakor, Manuel Berkemeier, Meike Wohlleben, Walter Sextro, Sebastian Peitz:
Surrogate-assisted multi-objective design of complex multibody systems. CoRR abs/2412.14854 (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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