Flowcean - Model Learning for Cyber-Physical Systems
This addresses the problem of time-consuming model creation for CPS designers and operators, but it appears incremental as it builds on existing data-driven methods with a focus on framework integration.
The paper tackles the difficulty of constructing models for Cyber-Physical Systems (CPS) by introducing Flowcean, a framework that automates data-driven model generation, resulting in a more efficient and accessible process through modularity and usability.
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.