June 2020   |   Networks, Quantum Computing

Quantum backbone

An Oak Ridge early-career award recipient plots the infrastructure for a quantum-information highway.

April 2020

Sizing up the beast

With help from DOE supercomputers, a USC-led team expands models of the fault system beneath its feet, aiming to predict its outbursts.

Climate, Genomics
April 2020

Soil sifters

Algorithms and supercomputers help tease out how soil microbes affect global climate.

Aeronautics, Engineering
January 2020

Beyond the tunnel

Stanford-led team turns to Argonne’s Mira to fine-tune a computational route around aircraft wind-tunnel testing.

Science Highlights

May 2019

AI and deep learning for fusion

Princeton researchers apply deep learning to a new code, Fusion Recurrent Neural Network (FRNN), to forecast events that disrupt fusion reactions.

Bagel-shaped fusion reactors called tokamaks produce energy of the kind that powers the sun. But massive disruptions can halt fusion reactions and damage the reactors.  Princeton University researchers’ Fusion Recurrent Neural Network (FRNN) code harnesses an artificial-intelligence tool called deep-learning to predict disruptive events.  Researchers can also use the code to make predictions that could open avenues for active reactor control and optimization. The method, reported in Nature, holds promise for enabling steady-state operation of tokamaks.

View full highlight »