  A surrogate model of the runaway electron avalanche growth rate in a magnetic
fusion plasma is developed. This is accomplished by employing a
physics-informed neural network (PINN) to learn the parametric solution of the
adjoint to the relativistic Fokker-Planck equation. The resulting PINN is able
to evaluate the runaway probability function across a broad range of parameters
in the absence of any synthetic or experimental data. This surrogate of the
adjoint relativistic Fokker-Planck equation is then used to infer the avalanche
growth rate as a function of the electric field, synchrotron radiation and
effective charge. Predictions of the avalanche PINN are compared against first
principle calculations of the avalanche growth rate with excellent agreement
observed across a broad range of parameters.
