Data-driven and model-based design

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


This paper explores novel research directions arising from the revolutions in artificial intelligence and the related fields of machine learning, data science, etc. We identify opportunities for system design to leverage the advances in these disciplines, as well as to identify and study new problems. Specifically, we propose Data-driven and Model-based Design (DMD) as a new system design paradigm, which combines model-based design with classic and novel techniques to learn models from data.
Original languageEnglish
Title of host publication2018 IEEE Industrial Cyber-Physical Systems (ICPS)
Number of pages6
ISBN (Electronic)978-1-5386-6531-2
Publication statusPublished - 1 May 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Industrial Cyber-Physical Systems - St. Petersburg, Russian Federation
Duration: 15 May 201818 May 2018
Conference number: 1


ConferenceInternational Conference on Industrial Cyber-Physical Systems
Abbreviated titleICPS
CountryRussian Federation
CitySt. Petersburg


  • artificial intelligence
  • data analysis
  • learning (artificial intelligence)
  • research directions
  • machine learning
  • data science
  • system design paradigm
  • data-driven
  • model-based design
  • DMD
  • System analysis and design
  • Machine learning
  • Computational modeling
  • Mathematical model
  • Data models
  • Prototypes
  • System design
  • formal methods
  • verification
  • synthesis

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  • Cite this

    Tripakis, S. (2018). Data-driven and model-based design. In 2018 IEEE Industrial Cyber-Physical Systems (ICPS) (pp. 103-108). IEEE.