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 …

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 …

Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …

Combustion science is an interdisciplinary study that involves nonlinear physical and
chemical phenomena in time and length scales, including complex chemical reactions and …

Two deep learning (DL) models addressing the super-resolution (SR) reconstruction of
turbulent flows from low-resolution coarse flow field data are developed. One is the static …

In this paper, a data driven approach is presented for the prediction of incompressible
laminar steady flow field over airfoils based on the combination of deep Convolutional …

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

A fully connected neural network (NN) is used to develop a subgrid-scale (SGS) model
map** the relation between the SGS stresses and filtered flow variables in a turbulent …

A framework is introduced that leverages known physics to reduce overfitting in machine
learning for scientific applications. The partial differential equation (PDE) that expresses the …

An artificial neural network (ANN) is used to establish the relation between the resolved-
scale flow field and the subgrid-scale (SGS) stress tensor, to develop a new SGS model for …