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.
A team of researchers from 91°µÍø (ORNL) designed, implemented, and evaluated a high-performance computing (HPC) runtime system.
Researchers from 91°µÍø and the University of Central Florida have extended an evolutionary approach for training spiking neural networks.