Flowcean - Model Learning for Cyber-Physical Systems
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866908882018762752 |
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| author | Schmidt, Maximilian Plambeck, Swantje Knitt, Markus Rose, Hendrik Fey, Goerschwin Wieck, Jan Christian Balduin, Stephan |
| author_facet | Schmidt, Maximilian Plambeck, Swantje Knitt, Markus Rose, Hendrik Fey, Goerschwin Wieck, Jan Christian Balduin, Stephan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12015 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Flowcean - Model Learning for Cyber-Physical Systems Schmidt, Maximilian Plambeck, Swantje Knitt, Markus Rose, Hendrik Fey, Goerschwin Wieck, Jan Christian Balduin, Stephan Machine Learning Artificial Intelligence 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. |
| title | Flowcean - Model Learning for Cyber-Physical Systems |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2603.12015 |