Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …

Physics-informed machine-learning (PIML) enables the integration of domain knowledge
with machine learning (ML) algorithms, which results in higher data efficiency and more …

Abstract Machine learning models are gaining increasing popularity in the domain of fluid
dynamics for their potential to accelerate the production of high-fidelity computational fluid …

We present a new data reconstruction method with supervised machine learning techniques
inspired by super resolution and inbetweening to recover high-resolution turbulent flows …

This paper surveys machine-learning-based super-resolution reconstruction for vortical
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …

Reynolds-averaged Navier–Stokes simulations represent a cost-effective option for practical
engineering applications, but are facing ever-growing demands for more accurate …

When discussing research in physics and in science more generally, it is common to ascribe
equal importance to the three components of the scientific trinity: theoretical, experimental …

Recent applications of machine learning, in particular deep learning, motivate the need to
address the generalizability of the statistical inference approaches in physical sciences. In …

Spatial artificial neural network (ANN) models are developed for subgrid-scale (SGS) forces
in the large eddy simulation (LES) of turbulence. The input features are based on the first …

Deconvolutional artificial neural network (DANN) models are developed for subgrid-scale
(SGS) stress in large eddy simulation (LES) of turbulence. The filtered velocities at different …