Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

Antony Jameson pioneered CFD-based aerodynamic design optimization in the late 1980s.
In addition to developing the fundamental theory, Jameson implemented that theory in …

Abstract The Surrogate Modeling Toolbox (SMT) is an open-source Python package that
offers a collection of surrogate modeling methods, sampling techniques, and a set of sample …

Physics-Informed Neural Networks (PINN) are algorithms from deep learning leveraging
physical laws by including partial differential equations together with a respective set of …

As a high-fidelity approach to transition prediction, the coupled Reynolds-averaged Navier–
Stokes (RANS) and linear stability theory (LST)-based e N method is widely used in …

The modernization in automobile industries has been booming in recent times, which has
led to the development of lightweight and fuel-efficient design of different automobile …

A gas-particle cyclone separator is an economical device for removing particulate solids
from a gas system, which is widely utilized in industrial applications. This review mainly …

The industrial need to predict the behaviour of radically new products brings renewed
interest in how to set up and make use of physical prototypes and testing. However …

Physics-informed neural networks (PINNs) have been proposed to learn the solution of
partial differential equations (PDE). In PINNs, the residual form of the PDE of interest and its …

This paper offers a systematic literature review of real-time detection and classification of
Power Quality Disturbances (PQDs). A particular focus is given to voltage sags and notches …