Effective models of Cyber-Physical Systems (CPS) are crucial for their design and operation. Constructing such models is difficult and time-consuming due to the inherent complexity of CPS. As a result, data-driven model generation using machine learning methods is gaining popularity. In this paper, we present Flowcean, a novel framework designed to automate the generation of models through data-driven learning that focuses on modularity and usability. By offering various learning strategies, data processing methods, and evaluation metrics, our framework provides a comprehensive solution, tailored to CPS scenarios. Flowcean facilitates the integration of diverse learning libraries and tools within a modular and flexible architecture, ensuring adaptability to a wide range of modeling tasks. This streamlines the process of model generation and evaluation, making it more efficient and accessible.
@article{arxiv.2603.12015,
title = {Flowcean - Model Learning for Cyber-Physical Systems},
author = {Maximilian Schmidt and Swantje Plambeck and Markus Knitt and Hendrik Rose and Goerschwin Fey and Jan Christian Wieck and Stephan Balduin},
journal= {arXiv preprint arXiv:2603.12015},
year = {2026}
}