ML4CPS – Machine Learning for Cyber-Physical Systems

Extended Deadline for Paper Submissions: January 17, 2025

In response to requests from the community, we have extended the deadline for submitting research papers.

Revised Deadline: January 17, 2025

This additional time is provided to enhance and refine your research. For details and submission instructions, please visit our Submissions Page.

Registration

Registration is now open for the conference. We invite all attendees to secure their participation by registering through the following link: Register Here.

About the Conference

Cyber-physical systems possess the capability to adjust to evolving demands. When coupled with machine learning, for advanced automation and autonomy in various domains like predictive maintenance, self-optimization, and fault diagnosis spring to mind. An essential condition for exploiting this efficiency potential is the accessibility of machine learning techniques to engineers.

Therefore, the 8th Machine Learning 4 Cyber Physical Systems – ML4CPS – conference offers researchers and users from various fields an exchange platform. The conference will take place March 2025, 6^th till 7^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.

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

• Generative AI for CPS: Technologies like large language models facilitate human-like interactions with machines. This unlocks novel opportunities for intelligent automa­tion and the increase of the overall performance and func­tionality of cyber-physical systems.
• Automated Modelling: Developing and using models to learn behaviors and structures of cyber-physical systems. This includes intelligent methods to integrate prior and domain-knowledge as well as engineering approaches.
• Industrial AI: Integrating AI into manufacturing processes can help to optimize them and enhance operational efficiency. Integrating AI into legacy systems and existing infrastructure is still a major challenge.
• Edge AI: This involves running AI algorithms directly on local devices, which enables real-time data processing. Main challenges are energy efficiency and limited computational resources.
• Self-supervised learning: Minimizing the reliance on large, labeled datasets for machine learning is a key focus when examining production data. Techniques that allow models to learn from unlabeled data are essential.

Agenda

TBD

Conference Location

Fraunhofer Forum Berlin

Anna-Louisa-Karsch-Straße 2

10178 Berlin

Hosts

Important Dates

Paper submission: December 20, 2024 January 17, 2025

Reviewer Feedback: January 31, 2025

Camera-Ready Submission: February 15, 2025

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]

Committee

General Chairs:

Prof. Jürgen Beyerer, Fraunhofer IOSB

Prof. Oliver Niggemann, HSU

Prof. Achim Kampker, RWTH Aachen

Prof. Görschwin Fey, TUHH

Alois Kritl, ARIC

Organising Committee:

Christian Kühnert, Fraunhofer IOSB

Alexander Diedrich, HSU

Rui Yan Li, RWTH Aachen

Phillip Johann Overlöper, HSU

Program Committee:

Volker Lohweg, HS-OWL

Alexander Fay, HSU

Alexander Windmann, HSU

Ingo Pill

Alexander Maier, HS Bielefeld

Kaja Balzereit, HS Bielefeld

Silke Merkelbach, Fraunhofer IEM

Marcel Drescher, RWTH Aachen

Idel Montalvo, IngeniousWare GmbH

Andreas Schwung, Fraunhofer IOSB

Previous Conferences

ML4CPS 2024

ML4CPS 2023