Predicting Neutron Yield of NIF ICF Experiments by Applying Machine Learning to a Small (n<150) and Heterogeneous Experimental Dataset

Machine learning (ML) is a promising tool for predicting the performance of fusion experiments. Unfortunately, deep learning models require significant amounts of synthetic data to predict the performance of low shot rate experiments such as the National Ignition Facility (NIF). Here, we present an alternative approach based on shallow ML models trained exclusively on experimental data. We narrow our focus to predicting neutron yield, an important performance metric that is notoriously difficult to estimate. Although the dataset includes fewer than 150 shots, each with widely-varying experimental set-ups, we demonstrate a ML model that predicts the logarithm of neutron yield with an average error of ~10% using only a priori knowledge. Another model achieves an average of ~7% error utilizing X-ray diagnostic metrics in addition to a priori knowledge. These models can be used to roughly estimate the neutron yield of proposed NIF shots and identify the relative significance of shot parameters.