Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Finite-Rate-Chemistry Flows and Predicting Lean Premixed Gas Turbine Combustors. (arXiv:2210.16219v1 [physics.flu-dyn])

The accurate prediction of small scales in underresolved flows is still one
of the main challenges in predictive simulations of complex configurations.
Over the last few years, data-driven modeling has become popular in many fields
as large, often extensively labeled datasets are now available and training of
large neural networks has become possible on graphics processing units (GPUs)
that speed up the learning process tremendously. In fact, the successful
application of deep neural networks in fluid dynamics, such as for …

arxiv chemistry generative adversarial networks lean networks physics rate