articleProgress in Aerospace SciencesSep 1, 2022HYBRID OA

Machine learning in aerodynamic shape optimization

National University of Singapore · University of Michigan

Indexed incrossref

Abstract

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…

Citation impact

323
total citations
FWCI
34.02
Percentile
100%
References
591
Citations per year

Authors

3

Topics & keywords

Keywords
  • Aerodynamics
  • Computer science
  • Enhanced Data Rates for GSM Evolution
  • Scale (ratio)
  • Architecture
  • Perspective (graphical)
  • Artificial intelligence
  • Machine learning
UN Sustainable Development Goals
  • Industry, innovation and infrastructure
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