Machine Learning Techniques for Turbulence Analysis and Simulation

The research presents a thorough review of machine learning algorithms for predicting turbulence intensity in fluid dynamics, based on both Direct Numerical Simulation (DNS) and Large Eddy Simulation (LES) datasets. We analyze the performance of many models, including convolutional neural networks (CNN), long short-term memory networks (LSTM), random forests (RF), and feedforward neural networks (FNN). The training and validation of the models were done using a dataset that was partitioned into training and test sets, utilizing complex methods including transfer learning and cross-validation. Our analysis demonstrates that the CNN outperformed previous models, reaching a mean absolute error (MAE) of 0.15, a root mean squared error (RMSE) of 0.23, and a coefficient of determination (R2) of 0.91, which suggests a high degree of accuracy in turbulence prediction. The LSTM kept a close followup with an MAE of 0.18, RMSE of 0.26, and $\mathbf{R}^{2}$ of 0.89. The performance of RF and FNN models, however good, was substantially lower, with $R 2$ values of 0.84 and 0.81, respectively. Our findings indicate the usefulness of deep learning approaches in capturing the complicated dynamics of turbulence and offer prospective topics for study in employing these methodologies in real-world fluid applications.