A comprehensive evaluation of dimensionality reduction techniques in fluid dynamics

This article presents a detailed evaluation of six dimensionality reduction methods in computational fluid dynamics, focusing on their application to the analysis of high-dimensional spatiotemporal data. It underscores the significance of machine learning in enhancing fluid dynamics knowledge through dimensionality reduction (DR) techniques, which are divided into linear and nonlinear categories. The study assesses these methods based on criteria such as reconstruction error, computational efficiency, interpretability, preservation of data structure, and task-oriented performance. Findings reveal that nonlinear DR algorithms generally offer lower reconstruction errors and that principal component analysis excels in preserving data structure and supporting machine learning tasks like classification and regression. The article advocates for a strategic selection of DR techniques tailored to specific data characteristics and analytical objectives, highlighting the balance between dimensionality reduction and data integrity preservation.