January 2020   |   Aeronautics, Engineering

Beyond the tunnel

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

Fusion Energy
November 2019

Tracking tungsten

Supercomputer simulations provide a snapshot of how plasma reacts with – and can damage – components in large fusion reactors.

Biology, Engineering
November 2019

Spying on cancer

With an assist from the Mira supercomputer, researchers are tuning an imaging technique to detect possible cancers in their early stages.

Materials Science
August 2019

Power play

Carnegie Mellon-led team generates atomic-scale simulations at Argonne in search of the best new solar cell materials.

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.

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