ML4CPS – Machine Learning for Cyber-Physical Systems

Conference in Berlin, March 21-22, 2024

About the Conference

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

Therefore, the 7th Machine Learning for Cyber Physical Systems – ML4CPS – conferenceoffers researchers and users from various fields an exchange platform. The conference will take place from 21st till 22th of March 2024 at the Fraunhofer Forum in Berlin. Hosts are the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB) and the Helmut Schmidt University (HSU).

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

• Large Language Models for CPS: Large language models possess the capability to facilitate human-machine interaction. Their capacity to interpret and generate text unlocks novel opportunities for intelligent automation, contextual comprehension, and seamless integration of various data sources, ultimately enhancing the overall performance and functionality of cyber-physical systems.

• Multimodal Learning: These models possess the capability to facilitate the integration of multiple diverse sensors. Multimodal machine learning models can effectively combine sensor measurements, visual data, contextual information, and various other types of inputs. By using these models, cyber-physical systems can enhance their perception, especially in complex real-world scenarios, leading to improved overall performance.

• Robust Machine Learning: Those techniques play a vital role since they reduce effects of noisy or adversarial data. By incorporating robustness measures, machine learning models can demonstrate improved resilience, enhanced generalization capabilities, and reliable performance when being used for cyber-physical systems.

• Integrating domain knowledge in neural networks: This is a crucial aspect for building robust and high-performing neural networks. There are several ways to incorporate prior knowledge into the neural network, like designing the network architecture, incorporating additional data from simulations, or imposing constraints on the loss function. All approaches lead to improved network performance and adaptability in cyber-physical systems.



Conference Location

Fraunhofer Forum Berlin

Anna-Louisa-Karsch-Straße 2, 10178 Berlin

Brandenburger Tor


Fraunhofer IOSB

Important Dates

Paper submission: 10th of January 2024

Reviewer Feedback: 1st of February 2024

Camera-Ready Submission: 1st of March 2024

Paper Submission

Papers are chosen on a peer-review basis and accepted papers are published with a unique DOI number. Papers with commercial character will not be taken into consideration. Submissions are limited to eight content pages, including all figures and tables; additional pages containing references are allowed.

Please use the following template for your submission:

ML4CPS template

Paper Submission will be handled via easychair:

Submission page

All questions related to paper submissions should be emailed to:

[email protected]


General Chairs:

Oliver Niggemann, Helmut-Schmidt-University Hamburg

Jürgen Beyerer, Fraunhofer IOSB/Karlsruher Institut für Technologie (KIT)

Program Chairs:

Alexander Diedrich, Helmut-Schmidt-University Hamburg

Alexander Windmann, Helmut-Schmidt-University Hamburg

Christian Kühnert, Fraunhofer IOSB

International Program Committee:

Volker Lohweg, HS-OWL, DE

Alexander Fay, HSU, DE

Ingo Pill, Silicon Austria Labs, AT

Patrick Rodler, Alpen-Adria-Universität Klagenfurt, AT

Roni Stern, Ben-Gurion Universität, Israel

Alexander Maier, HS Bielefeld

Kaja Balzereit, HS Bielefeld

Idel Montalvo, IngeniousWare GmbH

Martin Wagner,  Technologiezentrum Wasser

Previous Conferences

ML4CPS 2023


Letzte Änderung: 5. December 2023