Applying machine learning to study fluid mechanics

Abstract This paper provides a short overview of how to use machine learning build data-driven models in fluid mechanics. The process is broken down into five stages: (1) formulating problem model, (2) collecting and curating training data inform the (3) choosing an architecture with which represent (4) designing loss function assess performance (5) selecting implementing optimization algorithm train model. At each stage, we discuss prior physical knowledge may be embedding process, specific examples from field Graphic abstract