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Data driven discovery of cyber physical systems

Author

Listed:
  • Ye Yuan

    (Huazhong University of Science and Technology
    Huazhong University of Science and Technology)

  • Xiuchuan Tang

    (Huazhong University of Science and Technology)

  • Wei Zhou

    (Huazhong University of Science and Technology)

  • Wei Pan

    (Delft University of Technology)

  • Xiuting Li

    (Huazhong University of Science and Technology)

  • Hai-Tao Zhang

    (Huazhong University of Science and Technology
    Huazhong University of Science and Technology)

  • Han Ding

    (Huazhong University of Science and Technology
    Huazhong University of Science and Technology)

  • Jorge Goncalves

    (Huazhong University of Science and Technology
    University of Cambridge
    University of Luxembourg)

Abstract

Cyber-physical systems embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber-physical systems have proved resistant to modeling due to their intrinsic complexity arising from the combination of physical and cyber components and the interaction between them. This study proposes a general framework for discovering cyber-physical systems directly from data. The framework involves the identification of physical systems as well as the inference of transition logics. It has been applied successfully to a number of real-world examples. The novel framework seeks to understand the underlying mechanism of cyber-physical systems as well as make predictions concerning their state trajectories based on the discovered models. Such information has been proven essential for the assessment of the performance of cyber-physical systems; it can potentially help debug in the implementation procedure and guide the redesign to achieve the required performance.

Suggested Citation

  • Ye Yuan & Xiuchuan Tang & Wei Zhou & Wei Pan & Xiuting Li & Hai-Tao Zhang & Han Ding & Jorge Goncalves, 2019. "Data driven discovery of cyber physical systems," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12490-1
    DOI: 10.1038/s41467-019-12490-1
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    Cited by:

    1. Ping, Zuowei & Li, Xiuting & He, Wei & Yang, Tao & Yuan, Ye, 2020. "Sparse learning of network-reduced models for locating low frequency oscillations in power systems," Applied Energy, Elsevier, vol. 262(C).
    2. Lorenzo Lucchini & Laura Alessandretti & Bruno Lepri & Angela Gallo & Andrea Baronchelli, 2020. "From code to market: Network of developers and correlated returns of cryptocurrencies," Papers 2004.07290, arXiv.org, revised Dec 2020.
    3. Li, Yutong & Hou, Jian & Yan, Gangfeng, 2024. "Exploration-enhanced multi-agent reinforcement learning for distributed PV-ESS scheduling with incomplete data," Applied Energy, Elsevier, vol. 359(C).
    4. Zhao Chen & Yang Liu & Hao Sun, 2021. "Physics-informed learning of governing equations from scarce data," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    5. Ding, Jia & Wang, Maolin & Ping, Zuowei & Fu, Dongfei & Vassiliadis, Vassilios S., 2020. "An integrated method based on relevance vector machine for short-term load forecasting," European Journal of Operational Research, Elsevier, vol. 287(2), pages 497-510.

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