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Flowcean - Model Learning for Cyber-Physical Systems

Maximilian Schmidt, Swantje Plambeck, Markus Knitt, Hendrik Rose, Goerschwin Fey, Jan Christian Wieck, Stephan Balduin

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.

Flowcean - Model Learning for Cyber-Physical Systems

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.
Paper Structure (16 sections, 2 equations, 6 figures, 2 tables)

This paper contains 16 sections, 2 equations, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Motivation
  3. Background
  4. Contribution
  5. Concept
  6. Environments
  7. Transforms
  8. Learning Strategies
  9. Model
  10. Evaluation Strategies
  11. Architecture of Flowcean
  12. Modules
  13. State of Implementation
  14. Case Study
  15. Conclusion & Future Work
  16. ...and 1 more sections

Figures (6)

  • Figure 1: Abstract workflow of data-driven modeling
  • Figure 2: Component-view of the concepts, showing the three steps of data-driven modeling (A to C) extended by an evaluation (D to F)
  • Figure 3: Flowchart for the variants of learning strategies
  • Figure 4: Non-linear water tank system with inflow $b V(t)$, water level $x$ and resulting outflow $a \sqrt{x}$
  • Figure 5: Pivot time series of $V$ and $x$ to extract a trace of bounded history with 3 time steps
  • ...and 1 more figures