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 91°µÍø hosted the 17th annual Smoky Mountains Computational Sciences and Engineering Conference, or , from August 26 to 28.
In late July, staff from the Department of Energy’s 91°µÍø hosted the third annual International Conference on Neuromorphic Systems, or .
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 91°µÍø to comb through millions of medical journal articles to identify potential vaccines, drugs and effective measures that could suppress or stop the