3 papers
physics.app-ph2019
An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction
Elizabeth Kautz, Wufei Ma, Saumyadeep Jana +4
Micrograph quantification is an essential component of several materials science studies. Machine learning methods, in particular convolutional neural networks, have previously dem…
physics.app-ph2019
Nanoscale spatially resolved mapping of uranium enrichment in actinide-bearing materials
Elizabeth Kautz, Douglas Burkes, Vineet Joshi +2
Spatially resolved analysis of uranium isotopes in small volumes of actinide-bearing materials is critical for a variety of technical disciplines, including earth and planetary sci…
cond-mat.mtrl-sci2019
A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels
Elizabeth Kautz, Alexander Hagen, Jesse Johns +1
A deep neural network was developed for the purpose of predicting thermal conductivity with a case study performed on neutron irradiated nuclear fuel. Traditional thermal conductiv…