research
∙
10/28/2022
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Da
Models for finite-rate-chemistry in underresolved flows still pose one o...
share
research
∙
10/28/2022
Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data and deep learning
In this paper, deep learning (DL) methods are evaluated in the context o...
research
∙
11/26/2019
Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows
Turbulence is still one of the main challenges for accurately predicting...
research
∙
10/01/2019
Deep learning at scale for subgrid modeling in turbulent flows
Modeling of turbulent flows is still challenging. One way to deal with t...
research
∙
09/20/2018
