Reference : Data driven discovery of cyber physical systems
Scientific journals : Article
Engineering, computing & technology : Multidisciplinary, general & others
Systems Biomedicine
http://hdl.handle.net/10993/44262
Data driven discovery of cyber physical systems
English
Yuan, Ye [Huazhong University of Science and Technology > Key Laboratory of Image Processing and Intelligent Control]
Tang, Xiuchuan [Huazhong University of Science and Technology > School of Mechanical Science and Engineering]
Zhou, Wei [Huazhong University of Science and Technology > Key Laboratory of Image Processing and Intelligent Control]
Pan, Wei [Delft University of Technology > Department of Cognitive Robotics]
Li, Xiuting [Huazhong University of Science and Technology > Key Laboratory of Image Processing and Intelligent Control]
Zhang, Hai-Tao [Huazhong University of Science and Technology > Key Laboratory of Image Processing and Intelligent Control]
Ding, Han [Huazhong University of Science and Technology > School of Mechanical Science and Engineering]
Goncalves, Jorge mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
25-Oct-2019
Nature Communications
Nature Publishing Group
Yes (verified by ORBilu)
International
2041-1723
London
United Kingdom
[en] 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.
Fonds National de la Recherche - FnR
Researchers
http://hdl.handle.net/10993/44262
10.1038/s41467-019-12490-1
FnR ; FNR10907093 > Jorge Gonçalves > CriTICS > Critical transitions in complex systems: from theory to applications > 01/11/2016 > 30/04/2023 > 2016

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