Deep-Learning-Enabled Assessment of Magnetic Confinement in Magnetized Liner Inertial Fusion

Magnetized Liner Inertial Fusion (MagLIF) is a magneto-inertial fusion (MIF) concept being studied on the Z-machine at Sandia National Laboratories. MagLIF relies on quasi-adiabatic heating of a gaseous deuterium (DD) fuel and flux compression of a background axially oriented magnetic field to achieve fusion relevant plasma conditions. The magnetic field-radius product (BR) at bang time determines the extent of confinement of charged fusion products and is thus of fundamental interest in understanding MagLIF performance. We surrogate expensive physics calculations of magnetized fast charged-particle transport and associated secondary neutron emission in MIF plasmas with an artificial neural network. This enables Bayesian inference of BR for a series of MagLIF experiments that systematically vary the laser preheat energy deposited in the target. We demonstrate fuel magnetization decreases with deposited preheat energy in a fashion consistent with Nernst advection of magnetic field out of the hot fuel and diffusion into the target liner. This constitutes the first ever systematic experimental study of the magnetic confinement properties as a function of fundamental inputs on any neutron-producing MIF platform.