Workshop report: Learning approaches for hybrid dynamical systems
Publication date
2025-05-27
Document type
Konferenzbeitrag
Author
Plambeck, Swantje
Schmidt, Maximilian
Balzereit, Kaja
Bracht, Aaron
Redeker, Magnus
Arabizadeh, Negar
Eickmeier, Jens
Fey, Goerschwin
Organisational unit
Publisher
Universitätsbibliothek der HSU/UniBw H
Book title
Machine learning for cyber physical systems : proceedings of the conference ML4CPS 2025
First page
91
Last page
101
Part of the university bibliography
✅
Language
English
Keyword
Cyber-physical systems
Hybrid dynamical systems
Model learning
Model inference
Hybrid automata
Model interoperability
Abstract
This report summarizes the workshop on “Learning Approaches for Hybrid Dynamical Systems”, held at the 2025 Conference on Machine Learning for Cyber-Physical Systems (ML4CPS). The workshop aimed to strengthen collaboration and foster exchange between institutions engaged in research on model learning methods for hybrid CPSs.
The participating research groups approach the topic from diverse perspectives, for example, from an application perspective, from a tool perspective, or from a fundamental and formal perspective. Accordingly, this paper synthesizes the discussions from the workshop and presents an overview of key perspectives on several central topics, including the taxonomy of hybrid systems, current learning paradigms and techniques, and particularly representative use cases.
The participating research groups approach the topic from diverse perspectives, for example, from an application perspective, from a tool perspective, or from a fundamental and formal perspective. Accordingly, this paper synthesizes the discussions from the workshop and presents an overview of key perspectives on several central topics, including the taxonomy of hybrid systems, current learning paradigms and techniques, and particularly representative use cases.
Version
Published version
Access right on openHSU
Open access