Machine-Learning Methods for Computational Science and Engineering

TLDR

Machine learning has recently gained renewed interest in natural sciences and engineering, where its algorithms are applied to scientific computing, data mining, and processing. This review surveys the current state of machine‑learning methods applied to computational science and engineering. The authors examine how machine learning can accelerate or enhance simulation techniques such as computational fluid dynamics, molecular dynamics, and structural analysis, build efficient surrogate models that replace costly simulations, process large scientific datasets across diverse fields, and improve virtual‑reality applications.

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.