ML4CPS 2026: Machine Learning 4 Cyber Physical Systems 2026 Fraunhofer Forum, Anna-Louisa-Karsch-Straße 2 Berlin, Germany, March 19-20, 2026

Cyber-physical systems are required to adapt to changing demands, often experience architectural changes over their lifetime, and generate a heterogeneous set of data. All of this leads to significant demands on monitoring and control software. This conference focuses on aspects of machine learning and related domains, such as predictive maintenance, self-optimization, fault diagnosis, re-planning, and reconfiguration. To build intelligent cyber-physical systems close cooperation between AI-research and industrial engineering is necessary. To facilitate such an exchange is the goal of this conference.

The 9th Machine Learning for Cyber Physical Systems (ML4CPS) conference offers researchers and users from various fields an exchange platform. The conference will take place March 2026, 19^th till 20^th at the Fraunhofer Forum in Berlin. Hosts are Fraunhofer IOSB, Helmut Schmidt University, Hamburg University of Technology, and the Chair of Production Engineering of E-Mobility Components (PEM) of RWTH Aachen.

Submission Guidelines

Papers are chosen on a peer-review basis, accepted papers are published by the Helmut Schmidt University Press (openHSU) accom­panied by a unique DOI number. Papers with commercial character will not be taken into consideration. The length of the papers should not exceed 12 pages.

In this year’s edition of the conference, we also encourage submission of contributions from industry detailing scientific problems they encounter. To contribute, an extended abstract of a maximum of one page is required and must be submitted through the conference portal.

For additional details and submission guidelines, please refer to: www.hsu-hh.de/imb/en/ml4cps

Papers may cover, but are not limited to the following topics:

• LLM-Agents for CPS: Large multimodal models for text, images, and time-series data offer new opportunities for industrial applications. They can unlock novel opportunities for intelligent automa­tion and the increase of the overall performance and func­tionality of cyber-physical systems.
• Physics-Inspired ML: Prior knowledge can be integrated into the neural network, through the network architecture, additional data from simulations, or imposing constraints on the loss function. This can be crucial for building robust and reliable Neural Networks.
• Industrial AI: Integrating AI into manufacturing processes can help to optimize them and enhance operational efficiency. Still, integrating AI into legacy systems and existing infrastructure is still a major challenge.
• Green AI: Reducing the energy consumption of AI systems is essential for industrial and edge applications. This topic focuses on methods for energy-efficient models, and the trade-off between performance and resource usage.
• Hybrid Methods & Hybrid Systems: Hybrid methods integrate multiple learning and modeling techniques while hybrid systems combine discrete and continuous dynamics and, thus, are powerful paradigms for complex CPS and industrial processes. Methods related to data-driven model identification, diagnosis, verification, and analysis are relevant challenges for the community.

Contact

All questions related to paper submissions should be emailed to ml4cps_orga@hsu-hh.de.