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Machine-Learning Methods for Computational Science and Engineering

by 1,*,†, 2,† and 3,†
1
Department of Mechanical and Aerospace Engineering, University of Strathclyde, Glasgow G1 1XJ, UK
2
Defence and Security Research Institute, University of Nicosia, CY-2417 Nicosia, Cyprus
3
School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Computation 2020, 8(1), 15; https://doi.org/10.3390/computation8010015
Received: 9 December 2019 / Revised: 29 January 2020 / Accepted: 13 February 2020 / Published: 3 March 2020
(This article belongs to the Special Issue Machine Learning for Computational Science and Engineering)
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. View Full-Text
Keywords: machine learning (ML); artificial intelligence; data-mining; scientific computing; virtual reality; neural networks; Gaussian processes machine learning (ML); artificial intelligence; data-mining; scientific computing; virtual reality; neural networks; Gaussian processes
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MDPI and ACS Style

Frank, M.; Drikakis, D.; Charissis, V. Machine-Learning Methods for Computational Science and Engineering. Computation 2020, 8, 15. https://doi.org/10.3390/computation8010015

AMA Style

Frank M, Drikakis D, Charissis V. Machine-Learning Methods for Computational Science and Engineering. Computation. 2020; 8(1):15. https://doi.org/10.3390/computation8010015

Chicago/Turabian Style

Frank, Michael; Drikakis, Dimitris; Charissis, Vassilis. 2020. "Machine-Learning Methods for Computational Science and Engineering" Computation 8, no. 1: 15. https://doi.org/10.3390/computation8010015

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