Flowcean - Model Learning for Cyber-Physical Systems

2026-03-12

AI Summary

This work proposes Flowcean, a modular and extensible data-driven modeling framework tailored for cyber-physical systems (CPS) to address the challenges of modeling complexity and low automation. Flowcean integrates diverse learning strategies, flexible data processing modules, and a standardized evaluation pipeline, enabling seamless collaboration across multiple libraries and supporting user-defined configurations. By unifying model generation, evaluation, and optimization within a single architectural framework, Flowcean significantly enhances modeling efficiency and generalizability while reducing development barriers and costs. The framework is broadly applicable to a wide range of CPS modeling tasks, offering a practical and scalable solution for complex system design and analysis.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Modeling other Agents
• Machine Learning: Multimodal Learning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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.

Problem

• Cyber-Physical Systems
• data-driven modeling
• model learning
• automated modeling
• machine learning

Innovation

• Flowcean
• Cyber-Physical Systems
• data-driven modeling
• modular architecture
• automated model learning