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

The present review covers the latest evolution of computational aeroacoustics, the field that
deals with the noise generated by fluid flows and its propagation in the medium. It highlights …

One of the most promising applications of machine learning in computational physics is to
accelerate the solution of partial differential equations (PDEs). The key objective of machine …

The resolution of the Poisson equation is usually one of the most computationally intensive
steps for incompressible fluid solvers. Lately, DeepLearning, and especially convolutional …

The Poisson equation is critical to get a self-consistent solution in plasma fluid simulations
used for Hall effect thrusters and streamer discharges, since the Poisson solution appears …

Facing the need to increase the accuracy of rocket engine design tools, the present work
introduces an innovative methodology for the design and optimization of rocket engine …

The design of acoustic metasurfaces with desirable properties is challenging due to their
artificial nature and the large space of physical and geometrical parameters. Until recently …

This study provides the framework for a variational Bayesian convolutional neural network
(VB-CNN) to quickly predict the wake velocity field of a pump-jet propulsor and quantify …

Abstract The Lattice Boltzmann Method (LBM) can be applied to several fluid dynamic
problems in the time domain. This numerical method indirectly solves the Navier–Stokes …

This paper focuses on the discovery of optimal flapping wing kinematics using a deep
learning surrogate model for unsteady aerodynamics and multi-objective optimisation. First …