Abstract Machine learning (ML) pervades an increasing number of academic disciplines and
industries. Its impact is profound, and several fields have been fundamentally altered by it …

Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …

A perspective is presented on how machine learning (ML), with its burgeoning popularity
and the increasing availability of portable implementations, might advance fluid mechanics …

Transitional–turbulent spots bridge the deterministic laminar state with the stochastic
turbulent state and affect the transition zone length in engineering flows. Turbulent spot …

Throughout the history of science, physics-based modeling has relied on judiciously
approximating observed dynamics as a balance between a few dominant processes …

Detecting the turbulent/non-turbulent interface is a challenging topic in turbulence research.
In the present study, machine learning methods are used to train detectors for identifying …

In this work, subgrid-scale (SGS) stress and SGS heat flux of compressible isotropic
turbulence are reconstructed by a spatially multi-scale artificial neural network (SMSANN) …

Data from direct numerical simulation of a zero-pressure-gradient incompressible turbulent
boundary layer (TBL)[You and Zaki,“Conditional statistics and flow structures in turbulent …

The effect of approximately homogeneous isotropic turbulence on the dynamics of an
axisymmetric turbulent jet (Re= 10 600 and 5800) in an ambient with a negligible mean flow …

This study reports the observation of outer-layer coherent structures in turbulent boundary
layer from the turbulent/non-turbulent interface perspective at moderate Reynolds number …