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.
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.