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New machine learning theory that can be applied to fusion energy raises questions about the very nature of science

A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system could be adapted to better predict and control the behavior of the plasma that fuels fusion facilities designed to harvest on Earth the fusion energy that powers the sun and stars.

New findings could improve understanding of potentially damaging solar storms

When fast-moving particles from the sun strike the Earth’s magnetic field, they set off reactions that could disrupt communications satellites and power grids. Now, scientists at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) have learned new details of this process that could lead to better forecasting of this so-called space weather.

Scientists develop forecasting technique that could help advance quest for fusion energy

Bringing the power of the sun to Earth requires sound theory, good engineering, and a little finesse. The process entails trapping charged, ultra-hot gas known as plasma so its particles can fuse and release enormous amounts of energy. The most widely used facilities for this process are doughnut-shaped tokamaks that hold plasma in place with strong magnets that are precisely shaped and positioned.

U.S. Department of Energy
Princeton Plasma Physics Laboratory is a U.S. Department of Energy national laboratory managed by Princeton University.

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