ML4CPS – Machine Learning for Cyber-Physical Systems - Professur für Informatik im Maschinenbau

Conference in Berlin, March 19-20, 2026

About the Conference

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, 19th till 20th 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.

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.

Agenda

tbd

Conference Location

Fraunhofer Forum Berlin

Anna-Louisa-Karsch-Straße 2

10178 Berlin

Hosts

Important Dates

Paper Submission: December 19, 2025 Feedback: January 30, 2026

Notification of Acceptance: February 13, 2026

Camera-Ready Submission: March 6, 2026

Submission Guidelines

Papers are chosen on a peer-review basis and accepted papers are published by the Helmut Schmidt University Press (openHSU) accompanied by a unique DOI. Papers with commercial character will not be taken into consideration. The length of the papers should not exceed 10 pages.

Please use the following template for your submission:

ML4CPS template

Paper Submission will be handled via easychair:

Submission Page

For additional details and submission guidelines, please refer to

[email protected]