Researchers develop models that predict plasma heating 10 million times faster than traditional methods, accelerating fusion energy research.
The AI models accurately predicted the physics involved by identifying and removing problematic data points that caused the original codes to fail.Researchers have developed new artificial intelligence models that can significantly improve the prediction of plasma heating in fusion experiments. For context, understanding and controlling plasma is critical to harnessing fusion energy, which is a clean and abundant energy source.
“With our intelligence, we can train the AI to go even beyond the limitations of available numerical models,” said Álvaro Sánchez Villar, lead author and associate research physicist at the US Department of Energy’s Princeton Plasma Physics Laboratory .The AI models were trained on data generated by a computer code. In certain scenarios, the numerical codes employed for plasma heating prediction gave anomalous results, known as outliers.
“We perform an exploratory analysis of each TORIC database to identify and classify cases that do not follow the targeted behavior, generally termed as outliers, allowing us to choose if they are to be maintained or not in the surrogate training,” remarked the research team in
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