Optimizing high energy density experimental designs to elucidate complex coupling between physics phenomena for training ML models

Complex engineered systems in the high energy density regime such as inertial confinement fusion (ICF) remain challenging to predict with radiation hydrodynamic codes. While progress has been made with large 3D simulations, computing power limits the number of simulations that can be done. Similarly, the low data return rates for these complex engineered systems makes it hard to uncover interactions between the various pieces of unit physics that make up the system. The typical approach to unraveling and validating the unit physics in these systems is to attempt to hold all parameters constant while varying a single parameter and measuring the system response. These slices through parameter space take many experiments and except for parameters that cannot be held fixed, there is little information about the coupling between the underlying physics. Here we examine ways to optimize the approach to collecting data that span the parameter space and target not only validate our understanding of the individual effects, but also collect information about the coupling. This multivariate approach can possibly provide a better set of data to validate our models with the same number of experiments. For machine learning models trained on simulation data and aimed to enable faster exploration of the space, these data sets could provide a more global validation the ML model. This could be especially impactful In the case of transfer learning, by not limiting data to small regions of space focused on optimizing the yield a given ICF target design.