The Sensitivity of Inertial Confinement Fusion Simulations to Equations of State and Conductivity Datasets

Simulations of inertial confinement fusion experiments rely on accurate input datasets to be maximally predictive. These types of datasets include the equations of state and transport properties, such as the electrical and thermal conductivity. In this talk, we discuss a Bayesian Inference approach for generating ensembles of these datasets[1, 2] that are suitable for uncertainty quantification analyses. We then carry out radiation-magnetohydrodynamic simulations of the Magnetized Liner Inertial Fusion (MagLIF) platform at Sandia National Laboratories to quantify the impact that the variation in these datasets have on outputs such as the nuclear fusion yield. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525; SAND2025-01456A.