The field of machine learning (ML) has rapidly advanced the state of the art in many fields of
science and engineering, including experimental fluid dynamics, which is one of the original …

This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …

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 …

We propose a customized convolutional neural network based autoencoder called a
hierarchical autoencoder, which allows us to extract nonlinear autoencoder modes of flow …

This work evaluates the applicability of super-resolution generative adversarial networks
(SRGANs) as a methodology for the reconstruction of turbulent-flow quantities from coarse …

Solving computational fluid dynamics problems requires using large computational
resources. The computational time and memory requirements to solve realistic problems …

We propose a method using supervised machine learning to estimate velocity fields from
particle images having missing regions due to experimental limitations. As a first example, 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 …

One of the successful feedback controls for skin-friction drag reduction designed by Choi et
al.(J. Fluid Mech., vol. 262, 1994, pp. 75–110), called 'opposition control', has a limitation in …

We propose an interpretable deep learning (DL) model that extracts physical features from
turbulence data. Based on a conditional generative adversarial network combined with a …