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A team of researchers from Princeton University and Harvard University applies extensive learning techniques to predict sudden disturbances that can stop fusion reactions and damage donut-shaped tokamaks. or the devices that host these reactions. (Image Source: Princeton University)Artificial intelligence (AI) can help develop a safe, clean and virtually unlimited fusion energy for power generation, according to scientists.
A team of researchers from Princeton University and Harvard University applies extensive learning techniques to predict sudden disturbances that can stop fusion reactions and damage donut-shaped tokamaks. or the devices that host these reactions.
In-depth learning is a powerful new version of the machine-learning form of machine learning, according to findings published in Nature magazine.
"This research opens a promising new chapter in the effort to bring unlimited energy to Earth," said Steven Cowley, director of the Princeton Plasma Physics Laboratory (PPPL) at the US Department of Energy (DOE). ).
"Artificial intelligence is exploding in all sciences and it is now starting to contribute to the global quest for fusion energy," Cowley said in a statement.
Fusion, which drives the Sun and the stars, consists of fusing light elements in the form of plasma – the hot, charged state of matter composed of free electrons and atomic nuclei – that generates energy.
Scientists seek to replicate the fusion on Earth to obtain an abundant source of energy for the production of electricity.
"Artificial intelligence is the most intriguing field of scientific growth at the moment, and marrying it to the science of fusion is very exciting," said William Tang, one of the leading PPPL research physicists.
"We have accelerated the ability to predict with great precision the most dangerous challenge of clean fusion energy," Tang said.
Unlike traditional software, which perform the prescribed instructions, deep learning learns from mistakes.
Neural networks, interconnected node layers – mathematical algorithms – that are "parameterized" or weighted by the program to give the desired form to reality are what appear to be magical.
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For any given input, the nodes seek to produce a specified output, such as a correct identification of a face or accurate forecasts of a disturbance.
The training starts when a node fails to perform this task: the weights automatically adjust for new data until the correct output is obtained.
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