In the relentless pursuit of harnessing fusion energy, researchers are increasingly turning to artificial intelligence to tackle complex challenges. A recent study published in the journal *Nuclear Fusion* (formerly known as *Nuclear Fusion*) showcases a novel application of machine learning in tokamak plasma stability, with significant implications for the future of fusion energy.
Led by L. Bardoczi from the Department of Physics and Astronomy at the University of California, Irvine, the research introduces a neural network-based classifier and regressor designed to predict the onset of neoclassical tearing modes (NTMs) in tokamaks with remarkable precision. NTMs are disruptive magnetic instabilities that can degrade plasma confinement and halt fusion reactions, posing a substantial challenge to the viability of tokamak-based fusion reactors.
The study leverages data from the DIII-D tokamak, a leading experimental facility in the United States, to train neural networks on distinguishing between sawtooth oscillations and NTMs. Sawteeth are periodic disruptions in the plasma current that can sometimes mask the onset of NTMs, making early detection and classification crucial for maintaining plasma stability.
“By applying singular value decomposition to balance the datasets, we were able to improve model regularization and prediction stability,” Bardoczi explained. “This allowed our neural networks to learn the key distinguishing features of sawtooth and tearing modes in the poloidal profiles of their magnetic amplitude and phase.”
The results are impressive. When the modes occur independently, the networks achieve perfect classification. Even in the more experimentally relevant scenario where both modes coexist, the networks maintain exceptional performance. Tests on synthetic data further demonstrate the superior accuracy of the neural network trained on complex-valued input, achieving a mean time delay and standard deviation below 1 millisecond.
The implications for the energy sector are profound. Effective prediction and classification of NTMs can lead to more stable plasma conditions, improved energy output, and reduced downtime in fusion reactors. As Bardoczi noted, “Applying these methods across a broad range of H-mode scenarios will enable future studies to systematically identify dominant NTM triggers as scenario-specific variables, paving the way for more effective tearing mode avoidance strategies in future fusion reactor designs.”
This research not only highlights the potential of machine learning in advancing fusion energy but also underscores the importance of interdisciplinary collaboration. By integrating expertise from plasma physics, data science, and engineering, researchers are pushing the boundaries of what’s possible in the quest for clean, sustainable energy.
As the global community continues to grapple with the challenges of climate change and energy security, innovations like these offer a beacon of hope. The journey towards commercial fusion energy is fraught with complexities, but with each breakthrough, we edge closer to a future powered by the same force that fuels the stars.