ORNL is home to the world's fastest exascale supercomputer, Frontier, which was built in part to facilitate energy-efficient and scalable AI-based algorithms and simulations.
The Department of Energy’s Oak Ridge National Laboratory hosted the second 2023 cohort of the International Atomic Energy Agency’s Lise Meitner Programme in October.
ORNL hosted its fourth Artificial Intelligence for Robust Engineering and Science, or AIRES, workshop from April 18-20. Over 100 attendees from government, academia and industry convened to identify research challenges and investment areas, carving the future of the discipline.
As renewable sources of energy such as wind and sun power are being increasingly added to the country’s electrical grid, old-fashioned nuclear energy is also being primed for a resurgence.
A method using augmented reality to create accurate visual representations of ionizing radiation, developed at ORNL, has been licensed by Teletrix, a firm that creates advanced simulation tools to train the nation’s radiation control workforce.
A force within the supercomputing community, Jack Dongarra developed software packages that became standard in the industry, allowing high-performance computers to become increasingly more powerful in recent decades.
In collaboration with the Department of Veterans Affairs, a team at Oak Ridge National Laboratory has expanded a VA-developed predictive computing model to identify veterans at risk of suicide and sped it up to run 300 times faster, a gain that could profoundly affect the VA’s ability to reach susceptible veterans quickly.
Oak Ridge National Laboratory is training next-generation cameras called dynamic vision sensors, or DVS, to interpret live information—a capability that has applications in robotics and could improve autonomous vehicle sensing.
Researchers at Oak Ridge National Laboratory are taking inspiration from neural networks to create computers that mimic the human brain—a quickly growing field known as neuromorphic computing.
A study led by Oak Ridge National Laboratory explored the interface between the Department of Veterans Affairs’ healthcare data system and the data itself to detect the likelihood of errors and designed an auto-surveillance tool