ORNL researchers developed a novel nonlinear level set learning method to reduce dimensionality in high-dimensional function approximation.
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.
Estimating complex, non-linear model states and parameters from uncertain systems of equations and noisy observation data with current filtering methods is a key challenge in mathematical modeling.
ORNL researchers developed a stochastic approximate gradient ascent method to reduce posterior uncertainty in Bayesian experimental design involving implicit models.
The researchers from ORNL have developed a new and faster algorithm for the graph all-pair shortest-path (APSP) problem.
The Department of Energy’s Oak Ridge National Laboratory hosted the 17th annual Smoky Mountains Computational Sciences and Engineering Conference, or SMC, from August 26 to 28.
In late July, staff from the Department of Energy’s Oak Ridge National Laboratory hosted the third annual International Conference on Neuromorphic Systems, or ICONS.
COVID-19 has upended nearly every aspect of our daily lives and forced us all to rethink how we can continue our work in a more physically isolated world.
Scientists have tapped the immense power of the Summit supercomputer at Oak Ridge National Laboratory to comb through millions of medical journal articles to identify potential vaccines, drugs and effective measures that could suppress or stop the