What machine learning can and cannot do for ICF

Machine learning (ML) methodologies have played remarkable roles in solving complex systems with large data, well-defined input-output pairs, and clearly definable goals and metrics, The methodology is especially effective in image analysis, classifications, or systems having no long chains of logic or reasoning dependent on diverse background knowledge or common sense. Recently, the methodology has been actively applied to inertial confinement fusion (ICF) capsules and design optimizations of the NIF (National Ignition Facility) ignition capsules, making significant progress. As it is applied more, ML raises concerns on its capabilities and deficiencies for ICF. ICF is a physical system requiring one or more of: long chains of logic, complex planning, and/or relying on physics knowledge and human judgement unknown to the computer. Additionally, the experimental database in ICF is not large enough to be used for credible training, so that most researchers in ICF use simulations (or a mix of simulations and experimental) results instead of real data to train ML and related tools like deep learning. They then use the trained learning model to predict for future events. As expected, the present ML predictions are not as predictive as one would like. Also, because of the extreme sensitivity of the neutron yield to the input implosion parameters, the distribution function in the ICF learning models changes rapidly over time, requiring frequent retraining. In order to be effective, using physics guided machine learning for ICF is preferred, especially while the database is small and the physical capabilities of the learning models are still being developed.  In this work, we identify problems in ICF that are suitable for ML and describe circumstances where ML is less likely to be successful.