Machine learning for cyber physical systems

Proceedings of the conference ML4CPS 2026
Publication date
2026-05-07
Document type
Konferenzband oder Tagungsband
Editor
Beyerer, Jürgen
Kampker, Achim
Li, Rui Yan
Fey, Görschwin
Plambeck, Swantje
Kühnert, Christian
Organisational unit
Fraunhofer Institut für Optronik, Systemtechnik und Bildauswertung (IOSB)
Technische Universität Hamburg
RWTH Aachen
DOI
URI
Conference
Publisher
Universitätsbibliothek der HSU/UniBw H
Contains the following part
Part of the university bibliography
✅
File(s)
Language
English
Abstract
Cyber Physical Systems are characterized by their ability to adapt and learn from their environment. Applications include advanced condition monitoring, predictive maintenance, diagnosis tasks, and many other areas. All these applications have in common that Machine Learning and Artificial Intelligence are the key technologies. However, applying ML and AI to CPS poses challenges such as limited data, less understood algorithms, and the need for high algorithm reliability. These topics were a focal point at the 9th ML4CPS—Machine Learning for Cyber-Physical Systems Conference in Berlin, held from March 19th to 20th, where industry and research experts discussed current advancements and new developments.
Description
This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
Version
Published version
Access right on openHSU
Open access