  A Bayesian optimization framework is used to investigate scenarios for
disruptions mitigated with combined deuterium and neon injection in ITER. The
optimization cost function takes into account limits on the maximum runaway
current, the transported fraction of the heat loss and the current quench time.
The aim is to explore the dependence of the cost function on injected
densities, and provide insights into the behaviour of the disruption dynamics
for representative scenarios. The simulations are conducted using the numerical
framework DREAM (Disruption Runaway Electron Analysis Model). We show that
irrespective of the quantities of the material deposition, multi-megaampere
runaway currents will be produced in the deuterium-tritium phase of operations,
even in the optimal scenarios. However, the severity of the outcome can be
influenced by tailoring the radial profile of the injected material; in
particular if the injected neon is deposited at the edge region it leads to a
significant reduction of both the final runaway current and the transported
heat losses. The Bayesian approach allows us to map the parameter space
efficiently, with more accuracy in favorable parameter regions, thereby
providing us information about the robustness of the optima.
