Fusion plasma prediction gets 1,000x boost with Korea’s new deep learning model

Scientists have developed a deep learning approach, FPL-net, that speeds up plasma prediction for nuclear fusion by 1,000 times. It solves the Fokker-Planck-Landau equation for fusion plasma collisions with high accuracy in a single step. Using GPUs and preserving key physical quantities during AI learning, FPL-net was validated through thermal equilibrium simulations. This breakthrough could enable digital twin technologies for fusion reactors. Researchers aim to extend the model to more complex plasma environments in the future.