  In the design of stellarators, energetic particle confinement is a critical
point of concern which remains challenging to study from a numerical point of
view. Standard Monte Carlo analyses are highly expensive because a large number
of particle trajectories need to be integrated over long time scales, and small
time steps must be taken to accurately capture the features of the wide variety
of trajectories. Even when they are based on guiding center trajectories, as
opposed to full-orbit trajectories, these standard Monte Carlo studies are too
expensive to be included in most stellarator optimization codes. We present the
first multifidelity Monte Carlo scheme for accelerating the estimation of
energetic particle confinement in stellarators. Our approach relies on a
two-level hierarchy, in which a guiding center model serves as the
high-fidelity model, and a data-driven linear interpolant is leveraged as the
low-fidelity surrogate model. We apply multifidelity Monte Carlo to the study
of energetic particle confinement in a 4-period quasi-helically symmetric
stellarator, assessing various metrics of confinement. Stemming from the very
high computational efficiency of our surrogate model as well as its sufficient
correlation to the high-fidelity model, we obtain speedups of up to 10 with
multifidelity Monte Carlo compared to standard Monte Carlo.
