ML4CPS – Machine Learning for Cyber-Physical Systems

Submissions requested

The contributions may relate to the following topics, but are not limited to them:

• 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.

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.
• Submissions are to be made via EasyChair using this link
• For additional details and submission guidelines, please refer to: ml4cps_orga@hsu-hh.de