Nuclear Fusion

Nuclear fusion research aims to harness the energy released when light atomic nuclei fuse, offering a potentially limitless and clean energy source. Current research heavily emphasizes improving the accuracy and efficiency of predictive models for various fusion approaches, including inertial confinement fusion (ICF) and magnetic confinement fusion (MCF), employing machine learning techniques such as large language models (LLMs), deep recurrent networks, and physics-informed neural networks. These models are crucial for optimizing experimental designs, analyzing complex plasma dynamics, and accelerating the development of viable fusion reactors. The successful application of these advanced computational methods holds significant promise for advancing fusion energy toward practical implementation.