Plasma Dynamic

Plasma dynamics research focuses on understanding and predicting the complex behavior of plasmas, aiming to improve control and modeling for applications like fusion energy. Current efforts heavily utilize machine learning, employing architectures like neural ordinary differential equations, neural networks (including convolutional and recurrent types), and graph neural networks to create reduced-order models, improve data analysis (e.g., super-resolution), and accelerate simulations. These advancements are crucial for optimizing fusion reactor designs, enhancing disruption prediction and avoidance, and enabling more efficient and accurate plasma simulations in various scientific and technological contexts.