58 citations · 106 across the 3 of their papers we have counts for
3 papers
Embedding Hard Physical Constraints in Neural Network Coarse-Graining of 3D Turbulence
Arvind T. Mohan, Nicholas Lubbers, Daniel Livescu +1
In the recent years, deep learning approaches have shown much promise in modeling complex systems in the physical sciences. A major challenge in deep learning of PDEs is enforcing…
Compressed Convolutional LSTM: An Efficient Deep Learning framework to Model High Fidelity 3D Turbulence
Arvind Mohan, Don Daniel, Michael Chertkov +1
High-fidelity modeling of turbulent flows is one of the major challenges in computational physics, with diverse applications in engineering, earth sciences and astrophysics, among…
The ISTI Rapid Response on Exploring Cloud Computing 2018
Carleton Coffrin, James Arnold, Stephan Eidenbenz +45
This report describes eighteen projects that explored how commercial cloud computing services can be utilized for scientific computation at national laboratories. These demonstrati…