2026/03/12 by Maximilian Schmidt, Swantje Plambeck, Markus Knitt +4 · 1 voice
Computer Science · Engineering · #Adaptability #Digital Transformation in Industry #Key (lock) #Modeling and Simulation Systems #Modular design #Modular programming #Modularity (biology) #Process (computing) #Range (aeronautics) #Smart Grid Security and Resilience #cs.AI #cs.LG
paper · pdf · open access · doi:10.48550/arxiv.2603.12015
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2026/03/12 · arxiv published 2026/03/12 · arxiv updated 2026/03/12 · openalex created_date 2026/03/14 · openalex updated_date 2026/07/28
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.