Machine Learning in Aerodynamic Shape Optimization
This is an incremental review paper for researchers in aerodynamic design, summarizing existing methods and future directions without presenting new results.
The paper reviews the use of machine learning in aerodynamic shape optimization to address challenges like compact design spaces and fast analysis, highlighting how cutting-edge ML approaches can benefit the field but noting that practical large-scale optimizations remain difficult due to high training costs.
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.