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In the design of stellarators, energetic particle confinement is a criticalpoint of concern which remains challenging to study from a numerical point ofview. Standard Monte Carlo analyses are highly expensive because a large numberof particle trajectories need to be integrated over long time scales, and smalltime steps must be taken to accurately capture the features of the wide varietyof trajectories. Even when they are based on guiding center trajectories, asopposed to full-orbit trajectories, these standard Monte Carlo studies are tooexpensive to be included in most stellarator optimization codes. We present thefirst multifidelity Monte Carlo scheme for accelerating the estimation ofenergetic particle confinement in stellarators. Our approach relies on atwo-level hierarchy, in which a guiding center model serves as thehigh-fidelity model, and a data-driven linear interpolant is leveraged as thelow-fidelity surrogate model. We apply multifidelity Monte Carlo to the studyof energetic particle confinement in a 4-period quasi-helically symmetricstellarator, assessing various metrics of confinement. Stemming from the veryhigh computational efficiency of our surrogate model as well as its sufficientcorrelation to the high-fidelity model, we obtain speedups of up to 10 withmultifidelity Monte Carlo compared to standard Monte Carlo.

Accelerating the estimation of energetic particle confinement statistics in stellarators using multifidelity Monte Carlo