Models based on advanced statistical theory and machine-learning algorithms aim to predict a disease’s trajectory.
National labs lead the push for operating systems that let applications run at exascale.
A University of Michigan team uses high-performance computing at Oak Ridge to predict how crystals form.
Data from experiments and advanced codes combine with supercomputing muscle to help explain a half-century-old mystery.
Developing practical fusion energy has been impeded for decades by high heat loss from magnetically confined plasmas. Researchers from MIT, University of California at San Diego and General Atomics captured the dynamics of plasma turbulence linked to heat loss on unprecedented scales. Pictured here is the inside of the Alcator C-Mod tokamak, the inset depicting plasma turbulence simulations that show long wavelength blobs coexisting with short wavelength streamers – small, finger-like structures that comprise the turbulence in the core of the experimental plasmas.View full highlight »