Machine-Learning Methods 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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Computation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-3197/8/1/15 |
| _version_ | 1818840052907638784 |
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| author | Michael Frank Dimitris Drikakis Vassilis Charissis |
| author_facet | Michael Frank Dimitris Drikakis Vassilis Charissis |
| author_sort | Michael Frank |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T04:04:04Z |
| format | Article |
| id | doaj.art-eb0b00c1f76b4a969d60be36c76c800e |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-12-19T04:04:04Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-eb0b00c1f76b4a969d60be36c76c800e2022-12-21T20:36:36ZengMDPI AGComputation2079-31972020-03-01811510.3390/computation8010015computation8010015Machine-Learning Methods for Computational Science and EngineeringMichael Frank0Dimitris Drikakis1Vassilis Charissis2Department of Mechanical and Aerospace Engineering, University of Strathclyde, Glasgow G1 1XJ, UKDefence and Security Research Institute, University of Nicosia, CY-2417 Nicosia, CyprusSchool of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UKThe 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.https://www.mdpi.com/2079-3197/8/1/15machine learning (ml)artificial intelligencedata-miningscientific computingvirtual realityneural networksgaussian processes |
| spellingShingle | Michael Frank Dimitris Drikakis Vassilis Charissis Machine-Learning Methods for Computational Science and Engineering Computation machine learning (ml) artificial intelligence data-mining scientific computing virtual reality neural networks gaussian processes |
| title | Machine-Learning Methods for Computational Science and Engineering |
| title_full | Machine-Learning Methods for Computational Science and Engineering |
| title_fullStr | Machine-Learning Methods for Computational Science and Engineering |
| title_full_unstemmed | Machine-Learning Methods for Computational Science and Engineering |
| title_short | Machine-Learning Methods for Computational Science and Engineering |
| title_sort | machine learning methods for computational science and engineering |
| topic | machine learning (ml) artificial intelligence data-mining scientific computing virtual reality neural networks gaussian processes |
| url | https://www.mdpi.com/2079-3197/8/1/15 |
| work_keys_str_mv | AT michaelfrank machinelearningmethodsforcomputationalscienceandengineering AT dimitrisdrikakis machinelearningmethodsforcomputationalscienceandengineering AT vassilischarissis machinelearningmethodsforcomputationalscienceandengineering |