Data-driven and model-based design

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

Abstract

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)
PublisherIEEE
Pages103-108
Number of pages6
ISBN (Electronic)978-1-5386-6531-2
DOIs
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

Conference

ConferenceInternational Conference on Industrial Cyber-Physical Systems
Abbreviated titleICPS
CountryRussian Federation
CitySt. Petersburg
Period15/05/201818/05/2018

Keywords

  • 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. https://doi.org/10.1109/ICPHYS.2018.8387644