  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.
