The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data
from experiments, field measurements, and large-scale simulations at multiple …

Deep reinforcement learning (DRL) has recently been adopted in a wide range of physics
and engineering domains for its ability to solve decision-making problems that were …

We present the first application of an artificial neural network trained through a deep
reinforcement learning agent to perform active flow control. It is shown that, in a two …

The prediction of extreme events, from avalanches and droughts to tsunamis and epidemics,
depends on the formulation and analysis of relevant, complex dynamical systems. Such …

In terms of multiple temporal and spatial scales, massive data from experiments, flow field
measurements, and high-fidelity numerical simulations have greatly promoted the rapid …

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 …

This paper presents an exhaustive investigation into the potential of integrating blockchain
and Artificial Intelligence (AI) technologies within aerospace engineering, explicitly …

Deep reinforcement learning (DRL) is applied to control a nonlinear, chaotic system
governed by the one-dimensional Kuramoto–Sivashinsky (KS) equation. DRL uses …

A novel computationally efficient machine learning (ML) framework has been developed for
constructing the turbulent flow field of single-phase or two-phase particle-liquid flows in a …

Conventional aerodynamic inverse design (AID) methods have major limitations in terms of
optimality and actuality of target parameter distribution. In this research, the target pressure …