ML4CPS – Machine Learning for Cyber-Physical Systems

Submissions requested

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

• 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.

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