Nuclear Fusion and Artificial Intelligence: the Dream of Limitless Energy -- AI Daily - Artificial Intelligence News

The Princeton Plasma Physics Lab (PPPL) has also used AI in their Fusion Recurrent Neural Network which aims to predict plasma disruptions ("a fast and anomalous loss of stability that can cause severe damage to plasma facing components"). The prediction system is led by William Tang, a professor in the Department of Astrophysical Sciences at Princeton and a principal research physicist at PPPL) and is considered to be "the first machine learning disruption predictor capable of consistently outperforming, on all metrics that matter, a simple "locked-mode" based predictor." In Eurofusion's paper on their advanced disruption predictor, they explain what the locked-mode is and how it is used in prediction: "When macroscopic instabilities start locking to the wall, the amplitude of the signal used to detect them (called locked mode) grows during the slowing down of their rotation. Therefore, the locked mode amplitude is routinely used as precursor of disruptions caused by this locking of instabilities to the wall.". One company working towards making nuclear fusion commercial is TAE Technologies (formerly known as Tri-Alpha Energy) and its CEO Michl Binderbauer claims that "commercialisation is coming in the next five years".