Flowcean
Flowcean is a research-oriented Python toolkit for defining, simulating, identifying, evaluating, and reproducing models of cyber-physical systems.
Flowcean builds on research into automatic model generation for CPS. Its modular environments, transforms, learning strategies, metrics, adapters, and backend integrations support a broad range of data-driven modeling studies. First-class support for hybrid dynamical systems adds explicit system structure and simulation to this established foundation without requiring every CPS model to be hybrid.
Capabilities
- Define and simulate hybrid systems with continuous dynamics, events, transitions, and resets.
- Identify hybrid dynamics and mode selectors with HyDRA.
- Learn models from datasets, incremental streams, active environments, simulations, or connected CPS data sources.
- Compose reusable environments, transforms, learners, models, metrics, adapters, and evaluation strategies across studies.
Start Here
Install Flowcean from PyPI:
pip install flowcean
Then choose a path:
- Follow the installation guide for user and developer setups.
- Build the minimal hybrid system.
- Browse the hybrid systems benchmark gallery.
- Run the simulated hybrid system identification workflow.
- Explore the user guide overview, modules, and learning strategies for Flowcean's general model-learning toolkit.
Citation
If you use Flowcean in research, please consider citing:
- Towards the Automatic Generation of Models for Prediction, Monitoring, and Testing of Cyber-Physical Systems, IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2023.
- Flowcean - Model Learning for Cyber-Physical Systems, Italian Workshop on Artificial Intelligence and Applications for Business and Industries (AIABI) at AIxIA, 2024, ArXiv, abs/2603.12015.
Acknowledgement
This work has been funded by BMBF project AGenC no. 16IS22047A.
