Machine-learning methods for computational science and engineering

Frank, Michael and Drikakis, Dimitris and Charissis, Vassilis (2020) Machine-learning methods for computational science and engineering. Computation, 8 (1). 15. ISSN 1866-9964

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    Abstract

    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.

    Creators(s): Frank, Michael ORCID logoORCID: https://orcid.org/0000-0003-1684-0939, Drikakis, Dimitris and Charissis, Vassilis;
    Item type:Article
    ID code:72005
    Keywords:artificial intelligence, data-mining, gaussian processes, machine learning (ML), neural networks, scientific computing, virtual reality, Electronic computers. Computer science, Theoretical Computer Science, Computer Science(all), Modelling and Simulation, Applied Mathematics
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Department:Strategic Research Themes > Ocean, Air and Space
    Faculty of Engineering
    Faculty of Engineering > Mechanical and Aerospace Engineering
    Depositing user: Pure Administrator
    Date deposited:07 Apr 2020 13:24
    Last modified:23 Jan 2021 03:57
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/72005
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