Condition monitoring plays a vital role in ensuring the reliability and optimal performance of
various engineering systems. Traditional methods for condition monitoring rely on physics …

Variational approaches to image motion segmentation has been an active field of study in
image processing and computer vision for two decades. We present a short overview over …

We present a data-assimilation technique based on a variational formulation and a
Lagrange multipliers approach to enforce the Navier–Stokes equations. A general operator …

Particle image velocimetry (PIV) plays a significant role in experimental fluid mechanics,
which aims to extract the velocity fields from successive particle image pairs. Deep learning …

Despite significant recent progress, dense, time-resolved imaging of complex, non-
stationary 3D flow velocities remains an elusive goal. In this work we tackle this problem by …

The capability of state-of-the-art techniques integrating experimental and computational fluid
dynamics has been expanding recently. In our previous study, we have developed a hybrid …

In this study, we propose a deep learning-based spatial refinement method to provide robust
high-resolution velocity fields for particle image velocimetry (PIV) analysis. We modified the …

In this paper, a variational technique derived from optimal control theory is used in order to
realize a dynamically consistent motion estimation of a whole fluid image sequence. The …

We present in this paper a novel combined scheme dedicated to the measurement of
velocity in fluid experimental flows through image sequences. The proposed technique …

The integration of numerical simulation and experimental measurements in cellular
materials at the sub-cellular scale is a real challenge. On the experimental side, the almost …