Applying Machine Learning to Study Fluid Mechanics
It offers a general guide for researchers in fluid mechanics, but it is incremental as it synthesizes existing methods without presenting new results.
This paper provides an overview of using machine learning to build data-driven models in fluid mechanics, breaking down the process into five stages and discussing how to embed prior physical knowledge at each stage.
This paper provides a short overview of how to use machine learning to build data-driven models in fluid mechanics. The process of machine learning is broken down into five stages: (1) formulating a problem to model, (2) collecting and curating training data to inform the model, (3) choosing an architecture with which to represent the model, (4) designing a loss function to assess the performance of the model, and (5) selecting and implementing an optimization algorithm to train the model. At each stage, we discuss how prior physical knowledge may be embedding into the process, with specific examples from the field of fluid mechanics.