The effects of hyperparameters on deep learning of turbulent signals

The effect of hyperparameter selection in deep learning (DL) models for fluid dynamics remains an open question in the current scientific literature. Many authors report results using deep learning models. However, better insight is required to assess deep learning models' behavior, particularly for complex datasets such as turbulent signals. This study presents a meticulous investigation of the long short-term memory (LSTM) hyperparameters, focusing specifically on applications involving predicting signals in shock turbulent boundary layer interaction. Unlike conventional methodologies that utilize automated optimization techniques, this research explores the intricacies and impact of manual adjustments to the deep learning model. The investigation includes the number of layers, neurons per layer, learning rate, dropout rate, and batch size to investigate their impact on the model's predictive accuracy and computational efficiency. The paper details the iterative tuning process through a series of experimental setups, highlighting how each parameter adjustment contributes to a deeper understanding of complex, time-series data. The findings emphasize the effectiveness of precise manual tuning in achieving superior model performance, providing valuable insights to researchers and practitioners who seek to leverage long short-term memory networks for intricate temporal data analysis. The optimization not only refines the predictability of the long short-term memory in specific contexts but also serves as a guide for similar manual tuning in other specialized domains, thereby informing the development of more effective deep learning models.