Data driven discovery of cyber physical systems.
Mendes Silva Goncalves, Jorge
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Yuan, Y., Tang, X., Zhou, W., Pan, W., Li, X., Zhang, H., Ding, H., & et al. (2019). Data driven discovery of cyber physical systems.. Nature communications, 10 (1), 4894. https://doi.org/10.1038/s41467-019-12490-1
Cyber-physical systems (CPSs) embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, intelligent manufacture and medical monitoring. CPSs have proved resistant to modeling due to their intrinsic complexity arising from the combination of physical components and cyber components and the interaction between them. This study proposes a general framework for reverse engineering CPSs directly from data. The method 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 ranging from mechanical and electrical systems to medical applications. The novel framework seeks to enable researchers to understand the underlying mechanism of CPSs as well as make predictions concerning the trajectory of CPSs based on the discovered model. Such information has been proven essential for the assessment of the performance of CPSs; it can potentially help debug in the implementation procedure and guide the redesign to achieve the required performance.
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External DOI: https://doi.org/10.1038/s41467-019-12490-1
This record's URL: https://www.repository.cam.ac.uk/handle/1810/296808
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