68 citations · 105 across the 5 of their papers we have counts for
5 papers
Adversarial sampling of unknown and high-dimensional conditional distributions
Malik Hassanaly, Andrew Glaws, Karen Stengel +1
Many engineering problems require the prediction of realization-to-realization variability or a refined description of modeled quantities. In that case, it is necessary to sample e…
A block-random algorithm for learning on distributed, heterogeneous data
Prakash Mohan, Marc T. Henry de Frahan, Ryan King +1
Most deep learning models are based on deep neural networks with multiple layers between input and output. The parameters defining these layers are initialized using random values…
Deep learning for presumed probability density function models
Marc T. Henry de Frahan, Shashank Yellapantula, Ryan King +2
In this work, we use ML techniques to develop presumed PDF models for large eddy simulations of reacting flows. The joint sub-filter PDF of mixture fraction and progress variable i…
From Deep to Physics-Informed Learning of Turbulence: Diagnostics
Ryan King, Oliver Hennigh, Arvind Mohan +1
We describe tests validating progress made toward acceleration and automation of hydrodynamic codes in the regime of developed turbulence by three Deep Learning (DL) Neural Network…
Advanced Scenario Creation Strategies for Stochastic Economic Dispatch with Renewables
Ryan N. King, Matthew Reynolds, Devon Sigler +1
Real-time dispatch practices for operating the electric grid in an economic and reliable manner are evolving to accommodate higher levels of renewable energy generation. In particu…