  Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and
continued developments in deep learning. We review the applications of ML in
ASO to date and provide a perspective on the state-of-the-art and future
directions. We first introduce conventional ASO and current challenges. Next,
we introduce ML fundamentals and detail ML algorithms that have been successful
in ASO. Then, we review ML applications to ASO addressing three aspects:
compact geometric design space, fast aerodynamic analysis, and efficient
optimization architecture. In addition to providing a comprehensive summary of
the research, we comment on the practicality and effectiveness of the developed
methods. We show how cutting-edge ML approaches can benefit ASO and address
challenging demands, such as interactive design optimization. Practical
large-scale design optimizations remain a challenge because of the high cost of
ML training. Further research on coupling ML model construction with prior
experience and knowledge, such as physics-informed ML, is recommended to solve
large-scale ASO problems.
