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Machine learning technique offers insight into plasma behavior

Machine learning, which lets researchers determine if two processes are causally linked without revealing how, could help stabilize the plasma within doughnut-shaped fusion devices known as tokamaks. Such learning can facilitate the avoidance of disruptions — off-normal events in tokamak plasmas that can lead to very fast loss of the stored thermal and magnetic energies and threaten the integrity of the machine.

PPPL-led team wins major award of time on DOE supercomputers for fusion studies in 2017

A nationwide team of researchers led by physicist C.S. Chang of the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) has won the use of 269.9 million supercomputer hours to complete an extreme-scale study of the complex edge region of fusion plasmas. The award was made by the DOE’s ASCR Leadership Computing Challenge (ALCC) program for 2017, supported by DOE’s Office of Science.

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