  A physics-informed neural network (PINN) is used to evaluate the fast ion
distribution in the hot spot of an inertial confinement fusion target. The use
of tailored input and output layers to the neural network is shown to enable a
PINN to learn the parametric solution to the Vlasov-Fokker-Planck equation in
the absence of any synthetic or experimental data. As an explicit demonstration
of the approach, the specific problem of Knudsen layer fusion yield reduction
is treated. Here, predictions from the Vlasov-Fokker-Planck PINN are used to
provide a non-perturbative solution of the fast ion tail in the vicinity of the
hot spot thus allowing the spatial profile of the fusion reactivity to be
evaluated for a range of collisionalities and hot spot conditions. Excellent
agreement is found between the predictions of the Vlasov-Fokker-Planck PINN and
results from traditional numerical solvers with respect to both the energy and
spatial distribution of fast ions and the fusion reactivity profile
demonstrating that the Vlasov-Fokker-Planck PINN provides an accurate and
efficient means of determining the impact of Knudsen layer yield reduction
across a broad range of plasma conditions.
