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 …

Multistage shedding in cavity flow processes is a challenging and crucial topic in cavitating
flows. This paper employs three different turbulence models to obtain a more …

The applications of physics-informed neural networks (PINNs) in material parameters
identification of composite laminates are currently research highlights. We present an …

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 …

This study explores challenges in multivariate modal decomposition for various flow
scenarios, emphasizing the problem of inconsistent physical modes in Proper Orthogonal …

The rapid and accurate recognition of cavitation in centrifugal pumps has become essential
for improving production efficiency and ensuring machinery longevity. To address the …

Due to experimental limitations and data transmission constraints, we often encounter
situations where we can only obtain incomplete flow field data. However, even with …

Self-awareness of its own state during autonomous operation is critical for Autonomous
Underwater Vehicles (AUVs) to execute tasks and monitor their health. Automated data …