Overview of Machine Learning Techniques in Fluid Flow Analysis

Machine learning techniques have become important in fluid flow analysis, providing advanced tools to model, predict, and understand complex fluid dynamics. These techniques, capable of managing intricate patterns and large datasets, are increasingly applied across various domains of fluid dynamics, leading to the development of more refined and accurate models. This chapter begins with a brief general introduction about fluid flows and then introduces the fundamental concepts of machine learning, covering key algorithms, such as linear regression, decision trees, random forests, support vector machines, gradient boosting, and artificial neural networks. Additionally, physics-informed neural networks (PINNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) have been presented, which embed physical laws into neural networks to improve model accuracy and interpretability. This chapter also discusses essential evaluation metrics, including mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and R-squared ( R 2 ) correlation, providing a comprehensive framework for assessing model performance in fluid flow analysis. By combining theoretical insights with practical applications, this chapter equips readers with the knowledge to effectively apply machine learning techniques in advancing fluid dynamics research.