Inertial Confinement Fusion

Inertial confinement fusion (ICF) aims to achieve controlled nuclear fusion by imploding a small fuel capsule using high-powered lasers, a process currently hampered by complex plasma dynamics and the need for precise target fabrication. Recent research heavily utilizes machine learning, employing techniques like neural networks (including convolutional and generative models) and reservoir computing, to improve prediction of key parameters (e.g., hot electron dynamics, surface roughness), optimize experimental design, and enhance the analysis of sparse experimental data, particularly 3D reconstructions from limited X-ray views. These data-driven approaches are significantly advancing ICF research by accelerating the optimization of experimental parameters and uncovering previously unknown physical phenomena, ultimately contributing to the development of efficient and reliable fusion energy.