Two models based on convolutional neural networks are trained to predict the two-
dimensional instantaneous velocity-fluctuation fields at different wall-normal locations in a …

The capabilities of recurrent neural networks and Koopman-based frameworks are
assessed in the prediction of temporal dynamics of the low-order model of near-wall …

Data-driven methods have become an essential part of the methodological portfolio of fluid
dynamicists, motivating students and practitioners to gather practical knowledge from a …

Particle image velocimetry (PIV) has been extensively used in wind-tunnel test for flow-field
measurement. However, the sampling frequency of traditional PIV is low and physics of flow …

Data reconstruction of rotating turbulent snapshots is investigated utilizing data-driven tools.
This problem is crucial for numerous geophysical applications and fundamental aspects …

流体运动理论上可用Navier− Stokes 方程描述, 但由于对流项带来的非线性,
仅在少数情况可求得方程解析解. 对于复杂工程流动问题, 数值模拟难以高效精准计算高雷诺数 …

Different types of neural networks have been used to solve the flow sensing problem in
turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements …

This study assesses the capability of extended proper orthogonal decomposition (EPOD)
and convolutional neural networks (CNNs) to reconstruct large-scale and very-large-scale …

We address the problem of data augmentation in a rotating turbulence set-up, a
paradigmatic challenge in geophysical applications. The goal is to reconstruct information in …

Fluid flows can be theoretically described by the Navier− Stokes equations. However, due to
the nonlinear convection term, analytical solutions of the equations can only be obtained for …