Flowcean - Model Learning for Cyber-Physical Systems

TL;DR

Flowcean is a flexible, modular framework that automates data-driven model learning for Cyber-Physical Systems, enhancing efficiency and usability in complex CPS modeling tasks.

Contribution

It introduces a comprehensive, adaptable framework that integrates various learning strategies and tools specifically designed for CPS model generation.

Findings

Streamlines CPS model generation process

Supports diverse learning strategies and tools

Improves efficiency and accessibility in CPS modeling

Abstract

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.