A web-based GUI for INTERSECT has been created which allows a user to configure an experiment on an electron microscope, setting such parameters as maximum number of steps for the machine learning algorithm to perform.
The Department of Energy’s Office of Science has selected three Oak Ridge National Laboratory scientists for Early Career Research Program awards.
Oak Ridge National Laboratory researchers developed an invertible neural network (INN) to effectively and efficiently solve earth-system model calibration and simulation problems.
A study led by researchers at ORNL could help make materials design as customizable as point-and-click.
A research team from ORNL, Pacific Northwest National Laboratory, and Arizona State University has developed a novel method to detect out-of-distribution (OOD) samples in continual learning without forgetting the learned knowledge of preceding tasks.
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.