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.
A team of researchers from 91°µÍø (ORNL) designed, implemented, and evaluated a high-performance computing (HPC) runtime system.
Achievement: Developed analysis tools for analyzing system logs from Titan, Jaguar and Eos systems from OLCF to extract characteristics of interest and created fault catalogue.
Significance and Impact: Understanding the characteristics of system failures