Improving aircraft performance using machine learning: A review

This review covers the new developments in machine learning (ML) that are impacting the multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics (experimental and numerical), aerodynamics, acoustics, combustion and structural health monitoring. We review the state of the art, gathering the advantages and challenges of ML methods across different aerospace disciplines and provide our view on future opportunities. The basic concepts and the most relevant strategies for ML are presented together with the most relevant applications in aerospace engineering, revealing that ML is improving aircraft performance and that these techniques will have a large impact in the near future.(c) 2023 The Author(s). Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).

Automated summary

This review focuses on the impact of new developments in machine learning on the multi-disciplinary field of aerospace engineering. It discusses the state of the art and the advantages and challenges of ML methods in various aerospace disciplines, as well as future opportunities. The article highlights the improvement of aircraft performance through ML and predicts its significant impact in the near future.