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10/28/2022
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform Meshes and Demonstration of an Accelerat
This paper extends the methodology to use physics-informed enhanced supe...
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10/28/2022
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Finite-Rate-Chemistry Flows and Predicting Lean Premixed Gas Turbine Combustors
The accurate prediction of small scales in underresolved flows is still ...
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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...
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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...
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03/04/2021
Pandemic Drugs at Pandemic Speed: Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers
The race to meet the challenges of the global pandemic has served as a r...
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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...
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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...
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07/24/2019
A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training
A scalable graphical method is presented for selecting, and partitioning...
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09/20/2018