Review on the coupling and promotion of machine learning methods to CFD

As the computer level advances in 1990s, CFD (the computational fluid dynamics, for short) has promoted fast. The process of using CFD to analyze complex or ideal conditions has a very wide application background. However, confronted by cases with higher accuracy as well as larger number of samples, CFD needs to spend much time and money for the sake of resolving the issues. ML (Machine learning, for short) approach provides a promising choice for CFD. This paper reviews the coupling of ML and CFD and the progress in promoting the application of CFD. This paper briefly introduces CFD along with ML approaches, such as supervised learning, unsupervised learning and reinforcement learning. This article also discusses challenges and issues with the aim of being resolved in the research of ML model based on CFD, such as using multiple machine learning models or hybrid models to solve problems and quantifying the uncertainty of machine learning models. If these problems are solved, ML method can provide a promising development prospect for CFD.