Fast and accurate airflow simulations in the built environment are critical to provide
acceptable thermal comfort and air quality to the occupants. Computational Fluid Dynamics …

Over the last 20 years Machine Learning (ML) has been applied to a wide variety of
applications in the fields of engineering and computer science. In the field of material …

CFD is widely used in physical system design and optimization, where it is used to predict
engineering quantities of interest, such as the lift on a plane wing or the drag on a motor …

There is a growing interest in developing data-driven subgrid-scale (SGS) models for large-
eddy simulation (LES) using machine learning (ML). In a priori (offline) tests, some recent …

We demonstrate how incorporating physics constraints into convolutional neural networks
(CNNs) enables learning subgrid-scale (SGS) closures for stable and accurate large-eddy …

Deep learning algorithms provide plausible benefits for efficient prediction and analysis of
nuclear reactor safety phenomena. However, research works that discuss the critical …

We demonstrate the first climate-scale, numerical ocean simulations improved through
distributed, online inference of Deep Neural Networks (DNN) using SmartSim. SmartSim is a …

Physics-informed neural networks (PINNs) are increasingly employed to replace/augment
traditional numerical methods in solving partial differential equations (PDEs). While state-of …

Fluid flow in the transonic regime finds relevance in aerospace engineering, particularly in
the design of commercial air transportation vehicles. Computational fluid dynamics models …

The prohibitive cost and low fidelity of experimental data in industry-scale thermofluid
systems limit the usefulness of pure data-driven machine learning methods. Physics …