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 (https://doi.org/10.3390/computation8010015)
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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.
ORCID iDs
Frank, Michael ORCID: https://orcid.org/0000-0003-1684-0939, Drikakis, Dimitris and Charissis, Vassilis;-
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Item type: Article ID code: 72005 Dates: DateEvent3 March 2020Published13 February 2020AcceptedSubjects: Science > Mathematics > Electronic computers. Computer science Department: Strategic Research Themes > Ocean, Air and Space
Faculty of Engineering
Faculty of Engineering > Mechanical and Aerospace EngineeringDepositing user: Pure Administrator Date deposited: 07 Apr 2020 13:24 Last modified: 04 Dec 2024 10:47 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/72005