The team conducted numerical studies to demonstrate the connection between the parameters of neural networks and the stochastic stability of DMMs.
A research team from ORNL and Pacific Northwest National Laboratory has developed a deep variational framework to learn an approximate posterior for uncertainty quantification.
ORNL and three partnering institutions have received $4.2 million over three years to apply artificial intelligence to the advancement of complex systems in which human decision making could be enhanced via technology.