Machine-learning methods for computational science and engineering

Michael Frank*, Dimitris Drikakis, Vassilis Charissis

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

105 Citations (Scopus)
277 Downloads (Pure)


The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering. We discuss ways of using ML to speed up or improve the quality of simulation techniques such as computational fluid dynamics, molecular dynamics, and structural analysis. We explore the ability of ML to produce computationally efficient surrogate models of physical applications that circumvent the need for the more expensive simulation techniques entirely. We also discuss how ML can be used to process large amounts of data, using as examples many different scientific fields, such as engineering, medicine, astronomy and computing. Finally, we review how ML has been used to create more realistic and responsive virtual reality applications.
Original languageEnglish
Article number15
Number of pages35
Issue number1
Early online date3 Mar 2020
Publication statusPublished - Mar 2020


  • machine learning
  • engineering
  • virtual reality
  • neural networks
  • artificial intelligence
  • CFD simulation
  • Gaussian processes
  • scientific computing
  • data mining
  • ML
  • Machine learning (ML)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering
  • Aerospace Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Automotive Engineering
  • Computational Theory and Mathematics
  • Computational Mechanics
  • Theoretical Computer Science
  • Applied Mathematics
  • General Computer Science
  • Modelling and Simulation


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