Invited Lecture: Data-driven Discovery of Cyber-Physical Systems

Abstract: A major cross-disciplinary challenge concerns the need to adequately model cyber-physical systems (CPSs). CPSs, which embed software into the physical world (for example, in smart grids, robotics, intelligent manufacture and medical monitoring), have proved resistant to modeling due to the intrinsic complexity arising from (a) the combination of physical and cyber components and (b) the interaction between systems. This study proposes a solution in the form of a general framework for reverse engineering CPSs from data without prior knowledge. The method, which draws from artificial intelligence, involves the identification of physical systems as well as the inference of computer logics using sparse identification. The novel framework, which has been applied successfully to a number of real-world examples, seeks to enable researchers to make predictions concerning the trajectory of CPSs based on the discovered model. Such information may prove essential for the assessment of the performance of CPS and the design of failure-proof CPS. We can also use the proposed framework for the creation of design guidelines for new CPSs.