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Machine-Learning Methods for Computational Science and Engineering

Frank, Michael; Drikakis, Dimitris; Charissis, Vassilis

Authors

Michael Frank

Dimitris Drikakis



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.

Journal Article Type Article
Acceptance Date Feb 13, 2020
Online Publication Date Mar 3, 2020
Publication Date 2020
Deposit Date Apr 3, 2023
Publicly Available Date Apr 3, 2023
Journal Computation
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 8
Issue 1
Article Number 15
DOI https://doi.org/10.3390/computation8010015
Keywords machine learning (ML); artificial intelligence; data-mining; scientific computing; virtual reality; neural networks; Gaussian processes

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