We demonstrate an algorithm for efficient rare event sampling in a rotorcraft model. Helicopter design parameters are typically chosen for efficient performance in cruise and hover. At the same time, structural components such as the length of the tail are chosen so that the rotorcraft is stable under perturbation by environmental factors such as noisy wind. In the face of stochastic forcing, however, environmental conditions may still lead to rare accidents despite good engineering design. We adapt a recent dynamic importance sampling algorithm for small-noise diffusions, derived from the theory of large deviations, to efficient sampling of rare events in a model rotorcraft system. The method achieves variance reduction in estimating the probabilities of stall events, and helps identify the dynamics leading to these phenomena.
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