Sebastian Peitz

1.2k total citations
36 papers, 650 citations indexed

About

Sebastian Peitz is a scholar working on Statistical and Nonlinear Physics, Control and Systems Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Sebastian Peitz has authored 36 papers receiving a total of 650 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Statistical and Nonlinear Physics, 14 papers in Control and Systems Engineering and 12 papers in Computational Theory and Mathematics. Recurrent topics in Sebastian Peitz's work include Model Reduction and Neural Networks (19 papers), Advanced Multi-Objective Optimization Algorithms (10 papers) and Probabilistic and Robust Engineering Design (9 papers). Sebastian Peitz is often cited by papers focused on Model Reduction and Neural Networks (19 papers), Advanced Multi-Objective Optimization Algorithms (10 papers) and Probabilistic and Robust Engineering Design (9 papers). Sebastian Peitz collaborates with scholars based in Germany, United States and United Kingdom. Sebastian Peitz's co-authors include Stefan Klus, Michael Dellnitz, Feliks Nüske, Cecilia Clementi, Christof Schütte, Friedrich Philipp, Karl Worthmann, Manuel Schaller, Steven L. Brunton and J. Nathan Kutz and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Automatica and Physica D Nonlinear Phenomena.

In The Last Decade

Sebastian Peitz

30 papers receiving 622 citations

Peers

Sebastian Peitz

SNP

CSE

SPU

Sonja Glavaški United States

Daisuke Tsubakino Japan

M. R. Heath United States

Elizabeth Qian United States

Kookjin Lee United States

Yeonjong Shin United States

Chenxi Wu China

Susmita Mall India

C. Barratt United States

Bor‐Chin Chang United States

Sonja Glavaški United States

Sebastian Peitz

382 ×1.0

SNP

289 ×1.3

CSE

131 ×0.9

177 ×1.3

SPU

75 ×0.7

Citations per year, relative to Sebastian Peitz Sebastian Peitz (= 1×) peers Sonja Glavaški

Countries citing papers authored by Sebastian Peitz

Since Specialization

Citations

This map shows the geographic impact of Sebastian Peitz's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sebastian Peitz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sebastian Peitz more than expected).

Fields of papers citing papers by Sebastian Peitz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sebastian Peitz. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sebastian Peitz. The network helps show where Sebastian Peitz may publish in the future.

Co-authorship network of co-authors of Sebastian Peitz

This figure shows the co-authorship network connecting the top 25 collaborators of Sebastian Peitz. A scholar is included among the top collaborators of Sebastian Peitz based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sebastian Peitz. Sebastian Peitz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Peitz, Sebastian, et al.. (2025). A multiobjective continuation method to compute the regularization path of deep neural networks. Machine Learning with Applications. 19. 100625–100625. 1 indexed citations

Peitz, Sebastian, et al.. (2025). Multi-objective deep learning: Taxonomy and survey of the state of the art. Machine Learning with Applications. 21. 100700–100700.

Raj, R. Thundil Karuppa, et al.. (2024). Multi‐criteria hydraulic turbine optimization using a genetic algorithm and trust‐region postprocessing. PAMM. 24(4). 1 indexed citations

Peitz, Sebastian, et al.. (2024). Distributed control of partial differential equations using convolutional reinforcement learning. Physica D Nonlinear Phenomena. 461. 134096–134096. 13 indexed citations

Peitz, Sebastian, et al.. (2024). Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems. Journal of Optimization Theory and Applications. 201(2). 539–582. 5 indexed citations

Peitz, Sebastian, et al.. (2024). Learning Bilinear Models of Actuated Koopman Generators from Partially Observed Trajectories. SIAM Journal on Applied Dynamical Systems. 23(1). 885–923. 10 indexed citations

Werner, Stefan & Sebastian Peitz. (2024). Numerical Evidence for Sample Efficiency of Model-Based Over Model-Free Reinforcement Learning Control of Partial Differential Equations. 2965–2971.

Peitz, Sebastian, et al.. (2024). Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping. SIAM Journal on Optimization. 34(3). 2259–2286. 2 indexed citations

Peitz, Sebastian, H. Harder, Feliks Nüske, et al.. (2024). Equivariance and partial observations in Koopman operator theory for partial differential equations. 12(2). 305–324. 3 indexed citations

10.

Peitz, Sebastian, et al.. (2024). A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces. Journal of Optimization Theory and Applications. 203(1). 455–487.

11.

Wallscheid, Oliver, et al.. (2023). ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids. The Journal of Open Source Software. 8(89). 5616–5616.

12.

Dellnitz, Michael, et al.. (2023). Efficient Time-Stepping for Numerical Integration Using Reinforcement Learning. SIAM Journal on Scientific Computing. 45(2). A579–A595. 3 indexed citations

13.

Peitz, Sebastian, et al.. (2023). Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits. PAMM. 23(2). 1 indexed citations

14.

Peitz, Sebastian, et al.. (2022). On the structure of regularization paths for piecewise differentiable regularization terms. Journal of Global Optimization. 85(3). 709–741. 1 indexed citations

15.

Nüske, Feliks, Sebastian Peitz, Friedrich Philipp, Manuel Schaller, & Karl Worthmann. (2022). Finite-Data Error Bounds for Koopman-Based Prediction and Control. Journal of Nonlinear Science. 33(1). 42 indexed citations

16.

Peitz, Sebastian, et al.. (2021). On the Treatment of Optimization Problems With L1 Penalty Terms via Multiobjective Continuation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(11). 7797–7808. 12 indexed citations

17.

Schütze, Oliver, et al.. (2019). Pareto Explorer: a global/local exploration tool for many-objective optimization problems. Engineering Optimization. 52(5). 832–855. 26 indexed citations

18.

Peitz, Sebastian & Michael Dellnitz. (2018). A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction. Mathematical and Computational Applications. 23(2). 30–30. 56 indexed citations

19.

Peitz, Sebastian, Sina Ober‐Blöbaum, & Michael Dellnitz. (2018). Multiobjective optimal control methods for the Navier-Stokes equations using reduced order modeling. Oxford University Research Archive (ORA) (University of Oxford). 14 indexed citations

20.

Peitz, Sebastian, et al.. (2017). A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems. arXiv (Cornell University). 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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