Using deep-learning to uncover physics of magnetic (charged particle) confinement in Magnetized Liner Inertial Fusion

Magnetized Liner Inertial Fusion (MagLIF) is a magneto-inertial fusion (MIF) concept studied on the Z-machine at Sandia National Laboratories. In MagLIF an axially premagnetized and laser preheated gaseous deuterium (DD or DT) fuel contained in a cylindrical beryllium tube or liner undergoes quasi-adiabatic heating and flux compression to achieve fusion relevant conditions. The magnetic field-radius product (BR) near bang time determines the extent of confinement of charged fusion products and is of fundamental interest in understanding MagLIF performance. We built an artificial neural network surrogate trained on expensive physics calculations of magnetized fast charged-particle transport and associated secondary neutron emission in MIF plasmas used to diagnose BR. This enables Bayesian inference of BR for a series of MagLIF experiments that systematically vary inputs including laser preheat energy deposited, gas fill density, and target dimensions. We demonstrate flux loss consistent with Nernst advection of magnetic field out of the hot fuel and diffusion into the cold target wall under these changes to experimental conditions.