Fusion Plasma

Fusion plasma research aims to understand and control the complex behavior of ionized gases to achieve sustained nuclear fusion reactions for energy production. Current research heavily utilizes machine learning, particularly deep recurrent networks, Bayesian neural networks, and Dynamic Mode Decomposition, to analyze high-dimensional data from tokamak experiments, develop reduced-order models for efficient simulations, and improve predictive capabilities of plasma dynamics. These data-driven approaches, often combined with physics-informed neural networks, are crucial for optimizing plasma confinement, suppressing instabilities like Edge Localized Modes, and ultimately advancing the development of fusion energy.