Search Results

Documents authored by Schmidt, Maximilian

Document
DOI: 10.4230/OASIcs.DX.2025.7
One-Shot Learning in Hybrid System Identification: A New Modular Paradigm

Authors: Swantje Plambeck, Maximilian Schmidt, Louise Travé-Massuyès, and Goerschwin Fey

Published in: OASIcs, Volume 136, 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)

Abstract
Identification of hybrid systems requires learning models that capture both discrete transitions and continuous dynamics from observational data. Traditional approaches follow a stepwise process, separating trace segmentation and mode-specific regression, which often leads to inconsistencies due to unmodeled interdependencies. In this paper, we propose a new iterative learning paradigm that jointly optimizes segmentation and flow function identification. The method incrementally constructs a hybrid model by evaluating and expanding candidate flow functions over observed traces, introducing new modes only when existing ones fail to explain the data. The approach is modular and agnostic to the choice of the regression technique, allowing the identification of hybrid systems with varying levels of complexity. Empirical results on benchmark examples demonstrate that the proposed method produces more compact models compared to traditional techniques, while supporting flexible integration of different regression methods. By favoring fewer, more generalizable modes, the resulting models are not only likely to reduce complexity but also simplify diagnostic reasoning, improve fault isolation, and enhance robustness by avoiding overfitting to spurious mode changes.
Cite as

Swantje Plambeck, Maximilian Schmidt, Louise Travé-Massuyès, and Goerschwin Fey. One-Shot Learning in Hybrid System Identification: A New Modular Paradigm. In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 7:1-7:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{plambeck_et_al:OASIcs.DX.2025.7,
  author =	{Plambeck, Swantje and Schmidt, Maximilian and Trav\'{e}-Massuy\`{e}s, Louise and Fey, Goerschwin},
  title =	{{One-Shot Learning in Hybrid System Identification: A New Modular Paradigm}},
  booktitle =	{36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)},
  pages =	{7:1--7:18},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-394-2},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{136},
  editor =	{Quinones-Grueiro, Marcos and Biswas, Gautam and Pill, Ingo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2025.7},
  URN =		{urn:nbn:de:0030-drops-247969},
  doi =		{10.4230/OASIcs.DX.2025.7},
  annote =	{Keywords: Hybrid System, Model Learning, Symbolic Regression}
}
Document
Short Paper
DOI: 10.4230/OASIcs.DX.2024.30
Usability of Symbolic Regression for Hybrid System Identification - System Classes and Parameters (Short Paper)

Authors: Swantje Plambeck, Maximilian Schmidt, Audine Subias, Louise Travé-Massuyès, and Goerschwin Fey

Published in: OASIcs, Volume 125, 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)

Abstract
Hybrid systems, which combine both continuous and discrete behavior, are used in many fields, including robotics, biological systems, and control systems. However, due to their complexity, finding an accurate model is a challenge. This paper discusses the usage of symbolic regression to learn hybrid systems from data and specifically analyses learning parameters for a recent algorithm. Symbolic regression is a powerful tool that can automatically discover accurate and interpretable mathematical models in the form of symbolic expressions. Models generated by symbolic regression are a valuable tool for system identification and diagnosis, e.g., to predict future system behavior or detect anomalies. A major opportunity of our approach is the ability to detect transitions between different continuous behaviors of a system directly based on the dynamics. From a diagnosis perspective, this can advantageously be used to detect the system entering fault modes and identify their models. This paper presents a parameter study for a symbolic regression based identification algorithm.
Cite as

Swantje Plambeck, Maximilian Schmidt, Audine Subias, Louise Travé-Massuyès, and Goerschwin Fey. Usability of Symbolic Regression for Hybrid System Identification - System Classes and Parameters (Short Paper). In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 30:1-30:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

Copy BibTex To Clipboard

@InProceedings{plambeck_et_al:OASIcs.DX.2024.30,
  author =	{Plambeck, Swantje and Schmidt, Maximilian and Subias, Audine and Trav\'{e}-Massuy\`{e}s, Louise and Fey, Goerschwin},
  title =	{{Usability of Symbolic Regression for Hybrid System Identification - System Classes and Parameters}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{30:1--30:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-356-0},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{125},
  editor =	{Pill, Ingo and Natan, Avraham and Wotawa, Franz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.30},
  URN =		{urn:nbn:de:0030-drops-221223},
  doi =		{10.4230/OASIcs.DX.2024.30},
  annote =	{Keywords: Hybrid Systems, Symbolic Regression, System Identification}
}
Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2025 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact