Turbulence is often studied by tracking its spatiotemporal evolution and analyzing the
dynamics of its different scales. The dual to this perspective is that of an observer who starts …

We report a new workflow for background-oriented schlieren (BOS), termed “physics-
informed BOS,” to extract density, velocity, and pressure fields from a pair of reference and …

The state of turbulent, minimal-channel flow is estimated from spatio-temporal sparse
observations of the velocity, using both a physics-informed neural network (PINN) and …

This study establishes a continuous sliding window weak-constraint four-dimensional
variational approach for reproducing a complete instantaneous flow from sparse …

Particle tracking velocimetry (PTV) is widely used to measure time-resolved, three-
dimensional velocity and pressure fields in fluid dynamics research. Inaccurate localization …

The super-temporal-resolution (STR) reconstruction of turbulent flows is an important data
augmentation application for increasing the data reach in measurement techniques and …

A model based on a convolutional neural network (CNN) is designed to reconstruct the three-
dimensional turbulent flows beneath a free surface using surface measurements, including …

This study establishes a compressible continuous adjoint data assimilation (C 2 ADA)
approach for reproducing a complete mean flow from sparse wall pressure observations …

Data assimilation (DA) integrates experimental measurements into computational models to
enable high-fidelity predictions of dynamical systems. However, the cost associated with …

We design a nonlinear estimator for channel flows at $ Re_ {\tau}= 180$ and $590 $. The
nonlinear estimator uses a linear estimator structure based on the linearised Navier-Stokes …