Prof. Dr. Sebastian Peitz
Scientific Machine Learning (Peitz)
Contact and Affiliations
- E-Mail:
- sebastian.peitz@uni-paderborn.de
- Phone:
- +49 5251 60-5021
- ORCID:
- 0000-0002-3389-793X
- Web:
- Homepage
-
- Office Address:
-
Pohlweg 51
33098 Paderborn - Room:
- O4.213
- E-Mail:
- sebastian.peitz@uni-paderborn.de
- Phone:
- +49 5251 60-5021
- ORCID:
- 0000-0002-3389-793X
- Web:
- Homepage
-
- Office Address:
-
Pohlweg 51
33098 Paderborn - Room:
- O4.213
About Sebastian Peitz
Curriculum Vitae
Since 01.10.2026: Professor for Scientific Machine Learning
01.10.2024 - 30.09.2026: Professor of Safe Autonomous Systems (TU Dortmund)
01.04.2021 - 30.09.2024: Assistant Professor (Jun.-Prof.)
Data Science for Engineering, Department of Computer Science, Paderborn University
01.10.2017 - 31.03.2021: Managing Director
Institute for Industrial Mathematics, Paderborn University
01.10.2013 - 30.09.2017: Scientific Employee
Chair of Applied Mathematics and Institute for Industrial Mathematics (Prof. Dr. Michael Dellnitz, Paderborn University)
08.08.2017: PhD
Title: "Exploiting Structure in Multiobjective Optimization and Optimal Control"
01.10.2007 - 18.07.2013: Studies
Mechanical Engineering (RWTH Aachen)
Graduation: 07/18/2013 (M. Sc.)
16.06.2006: Abitur
Hans-Ehrenberg-Gymnasium (Sennestadt)
2024: ERC Starting Grant
Funded by the European Research Council of the EU.
Title: “Koopman-Operator-based Reinforcement Learning Control of Partial Differential Equations” (DOI)
2022: Junior Research Group leader ("Multicriteria Machine Learning"), funded by BMBF
2019: Best paper award at the 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE)
Research
Selected Projects
- ESN4NW: Energieoptimierte Supercomputer-Netzwerke durch die Nutzung von Windenergie
- Hybrid Modeling for Data-enhanced Multiobjective Optimization of Multibody Systems
- Multicriteria machine learning – efficiency, robustness, interactivity and system knowledge
- SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems
- DARE: Training, validation and benchmark tools for the development of data-driven operation and control strategies for intelligent, local energy systems
Publications
Selected Publications
Finite-data error bounds for Koopman-based prediction and control
F. Nüske, S. Peitz, F. Philipp, M. Schaller, K. Worthmann, Journal of Nonlinear Science 33 (2023).
On the Universal Transformation of Data-Driven Models to Control Systems
S. Peitz, K. Bieker, Automatica 149 (2023).
Koopman analysis of quantum systems
S. Klus, F. Nüske, S. Peitz, Journal of Physics A: Mathematical and Theoretical 55 (2022) 314002.
On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation
K. Bieker, B. Gebken, S. Peitz, IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (2022) 7797–7808.
Deep model predictive flow control with limited sensor data and online learning
K. Bieker, S. Peitz, S.L. Brunton, J.N. Kutz, M. Dellnitz, Theoretical and Computational Fluid Dynamics 34 (2020) 577–591.
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