Data-driven Discovery of Cyber-Physical Systems
This addresses the challenge of predicting and assessing CPS performance for researchers and designers, though it appears incremental as it builds on existing reverse engineering concepts.
The study tackled the problem of modeling complex cyber-physical systems (CPSs) by proposing a general framework for reverse engineering them directly from data, successfully applying it to real-world examples like mechanical, electrical, and medical systems.
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 logic. 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 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 CPS, the design of failure-proof CPS and the creation of design guidelines for new CPSs.