  Deterministic models are approximations of reality that are easy to interpret
and often easier to build than stochastic alternatives. Unfortunately, as
nature is capricious, observational data can never be fully explained by
deterministic models in practice. Observation and process noise need to be
added to adapt deterministic models to behave stochastically, such that they
are capable of explaining and extrapolating from noisy data. We investigate and
address computational inefficiencies that arise from adding process noise to
deterministic simulators that fail to return for certain inputs; a property we
describe as "brittle." We show how to train a conditional normalizing flow to
propose perturbations such that the simulator succeeds with high probability,
increasing computational efficiency.
