{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/data-driven-discovery-of-cyber-physical","title":"Data-driven Discovery of Cyber-Physical Systems","arxiv_id":"1810.00697","date":"2018-10-01","proceeding":null,"authors":["Ye Yuan","Xiuchuan Tang","Wei Pan","Xiuting Li","Wei Zhou","Hai-Tao Zhang","Han Ding","Jorge Goncalves"],"abstract":"Cyber-physical systems (CPSs) embed software into the physical world. They\nappear in a wide range of applications such as smart grids, robotics,\nintelligent manufacture and medical monitoring. CPSs have proved resistant to\nmodeling due to their intrinsic complexity arising from the combination of\nphysical components and cyber components and the interaction between them. This\nstudy proposes a general framework for reverse engineering CPSs directly from\ndata. The method involves the identification of physical systems as well as the\ninference of transition logic. It has been applied successfully to a number of\nreal-world examples ranging from mechanical and electrical systems to medical\napplications. The novel framework seeks to enable researchers to make\npredictions concerning the trajectory of CPSs based on the discovered model.\nSuch information has been proven essential for the assessment of the\nperformance of CPS, the design of failure-proof CPS and the creation of design\nguidelines for new CPSs.","url_abs":"http://arxiv.org/abs/1810.00697v1","url_pdf":"http://arxiv.org/pdf/1810.00697v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"data-driven-discovery-of-cyber-physical","repo_url":"https://github.com/HAIRLAB/CPSid","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}