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From Deep to Physics-Informed Learning of Turbulence: Diagnostics
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 (NN) schemes trained on Direct Numerical Simulations of turbulence.
The ISTI Rapid Response on Exploring Cloud Computing 2018
This report describes eighteen projects that explored how commercial cloud computing services can be utilized for scientific computation at national laboratories.
Compressed Convolutional LSTM: An Efficient Deep Learning framework to Model High Fidelity 3D Turbulence
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 many others.
Fluid Dynamics Chaotic Dynamics Computational Physics
Machine Learning technique for isotopic determination of radioisotopes using HPGe $\mathrmγ$-ray spectra
$\mathrm{\gamma}$-ray spectroscopy is a quantitative, non-destructive technique that may be utilized for the identification and quantitative isotopic estimation of radionuclides.
