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.
As medical research has become more specialized, the scientific community’s understanding of the human body has increased, resulting in enhanced treatments, new drugs, and better health outcomes.
A side effect of this information explosion, however, is th
High-resolution imaging of materials produces complex, copious data.
Steady progress in the development of advanced materials has led to modern civilization’s foundational technologies—better batteries, resilient building materials and atom-scale semiconductors.
In the Stone, Bronze and Iron Ages, the state of the art of materials science defined technology’s zenith and accelerated economies.
Piranha, an award-winning intelligent agent-based technology to analyze text data with unprecedented speed and accuracy, will be showcased at the Smithsonian’s Innovation Festival Sept. 26-27.