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 …

Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

We introduce physics-informed neural networks–neural networks that are trained to solve
supervised learning tasks while respecting any given laws of physics described by general …

Data from experiments and direct simulations of turbulence have historically been used to
calibrate simple engineering models such as those based on the Reynolds-averaged Navier …

THE field of fluid mechanics involves a range of rich and vibrant problems with complex
dynamics stemming from instabilities, nonlinearities, and turbulence. The analysis of these …

This study investigates the accuracy of deep learning models for the inference of Reynolds-
averaged Navier–Stokes (RANS) solutions. This study focuses on a modernized U-net …

There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS)
turbulence models that are informed by and can represent a richer set of turbulence physics …

Data science models, although successful in a number of commercial domains, have had
limited applicability in scientific problems involving complex physical phenomena. Theory …

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 …

Abstract Reynolds-averaged Navier-Stokes (RANS) equations are widely used in
engineering turbulent flow simulations. However, RANS predictions may have large …