The current class of early-career honorees applies machine-learning tools to science problems.
Supercomputer power is providing influenza insights to help drug designers produce a more effective vaccine.
Argonne National Laboratory’s Aurora will take scientific computing to the next level. Visualization and analysis capabilities must keep up.
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 »