In the field of fluid mechanics, it is a potential consensus that nonlinear dimensionality
reduction (DR) techniques outperform linear methods. However, this conclusion has been …

In the field of fluid mechanics, dimensionality reduction (DR) is widely used for feature
extraction and information simplification of high-dimensional spatiotemporal data. It is well …

Clustering applied to unsteady flow fields can simplify flow field data and partition the flow
field into regions of interest. Unfortunately, these areas are often unexplored when applied …

Abstract Principal Orthogonal Decomposition (POD), as a data-driven method for extracting
key features from fluid flow, overlooks the potential interactions and correlations among …

Modal decomposition is a data-driven method widely used in fluid mechanics to extract
energy and dynamically significant features of fluid flow. However, traditional modal …

The application of data-driven methods to study cavitation flow provides insights into the
underlying mechanisms and richer physical details of cavitation phenomena. This paper …