Applying Machine Learning to Study Fluid Mechanics

TL;DR

This paper reviews how machine learning can be applied to fluid mechanics by outlining a five-stage process and emphasizing the integration of physical knowledge at each step.

Contribution

It provides a structured overview of applying machine learning to fluid mechanics, highlighting the importance of embedding physical insights into the modeling process.

Findings

Machine learning stages can be tailored for fluid mechanics models.

Physical knowledge can be integrated at each stage of ML modeling.

Guidelines for data collection and model design in fluid mechanics.

Abstract

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.