Abstract Machine learning is rapidly becoming a core technology for scientific computing,
with numerous opportunities to advance the field of computational fluid dynamics. Here we …

Deep reinforcement learning (DRL) has been applied to a variety of problems during the
past decade and has provided effective control strategies in high-dimensional and non …

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 …

We introduce a reinforcement learning (RL) environment to design and benchmark control
strategies aimed at reducing drag in turbulent fluid flows enclosed in a channel. The …

Turbulence is a complicated phenomenon because of its chaotic behavior with multiple
spatiotemporal scales. Turbulence also has irregularity and diffusivity, making predicting …

We propose an open-source Python platform for applications of deep reinforcement learning
(DRL) in fluid mechanics. DRL has been widely used in optimizing decision making in …

We mitigate vortex-induced vibrations of a square cylinder at a Reynolds number of 100
using deep reinforcement learning (DRL)-based active flow control (AFC). The proposed …

This study proposes a newly developed deep-learning-based method to generate turbulent
inflow conditions for spatially developing turbulent boundary layer (TBL) simulations. A …

Machine learning frameworks such as genetic programming and reinforcement learning
(RL) are gaining popularity in flow control. This work presents a comparative analysis of the …