What Machine Learning Can and Cannot Do for Inertial Confinement Fusion

Machine learning methodologies have played remarkable roles in solving complex systems with large data, well-defined input–output pairs, and clearly definable goals metrics. The are effective image analysis, classification, without long chains of logic. Recently, machine-learning been widely applied to inertial confinement fusion (ICF) capsules the design optimization OMEGA (Omega Laser Facility) capsule implosion NIF (National Ignition ignition capsules, leading significant progress. As machine is being increasingly applied, concerns arise regarding its capabilities limitations context ICF. ICF a complicated physical system that relies on physics knowledge human judgment guide learning. Additionally, experimental database for not enough provide credible training data. Most researchers field use simulations, or mix simulations results, instead real data train models related tools. They then trained model predict future events. This methodology can be successful, subject careful choice simulations. However, because extreme sensitivity neutron yield input parameters, physics-guided extremely important necessary, especially when small, uncertain-domain large, still developed. In this work, we identify problems suitable circumstances where less likely successful. study investigates applications highlights fundamental research challenges directions associated